GitHub Copilot assessment

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### **Q.1 – Key features of GitHub Copilot**
(Select all that apply)

✅ **Correct answers:**
– **It suggests code snippets based on the context.**
– **It generates comments and documentation.**
– **It suggests test cases based on the code context.**

❌ Not correct:
– *It generates unit test cases automatically* → Copilot suggests, but does not fully auto-generate all test cases.
– *It deploys the code to production* → Copilot never does deployment.

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### **Q.2 – Who can configure content exclusion?**
(Select all that apply)

✅ **Correct answers:**
– **Repository administrators**
– **Organization owners**

👉 Developers or Project Managers individually cannot configure content exclusions unless they are repo admins/org owners.

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### **Q.3 – Which IDEs are supported by GitHub Copilot?**

✅ **Correct answers:**
– **VS Code**
– **Visual Studio**
– **IntelliJ IDEA**
– **Vim/Neovim**

❌ Not correct:
– *Eclipse* → Not supported.

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### **Q.4 – Main applications of GitHub Copilot**

✅ **Correct answers:**
– **Code Completion**
– **Bug Fixing**
– **Unit Testing** (Copilot helps suggest test cases and code scaffolding)

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⚡ So, your marked answers should be:

– **Q1:** 3, 4, 5
– **Q2:** 2, 3
– **Q3:** 2, 3, 4, 5
– **Q4:** 1, 2, 3

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Do you want me to continue solving **all the remaining questions in your assessment screenshot set** the same way (so you can just copy answers directly), or should I wait for you to send me each question one by one?

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### **Q.4 – What are the main applications of GitHub Copilot?**
✅ Correct answers:
– **Code Completion**
– **Bug Fixing**
– **Unit Testing**
– **Create Documentation for code**

❌ Wrong:
– *Generating MS Word Documents* → Not a Copilot feature.

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### **Q.5 – If you want an explanation of your current code, how can you do this with the help of GitHub Copilot?**
✅ Correct answer:
– **By pressing Ctrl + I and then asking the chat to explain the code, or by typing /explain command in GitHub Copilot Chat**

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### **Q.6 – What does GitHub Copilot rely on to generate suggestions for incomplete code snippets?**
✅ Correct answer:
– **The context of the code, such as variables, logic and comments and user prompts (if any)**

❌ Wrong:
– Not just keywords, not pre-written templates, not web search, not hardcoded rules.

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### **Q.7 – How can you attach the entire codebase as context in GitHub Copilot chat?**
✅ Correct answer:
– **By configuring the workspace settings to include the entire project**

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### **Q.8 – Can GitHub Copilot suggest code for proprietary frameworks and private repositories?**
✅ Correct answer:
– **Yes** (Copilot can suggest based on your private repo context, but it won’t share it outside.)

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✅ **Final answers for Q4–Q8:**
– **Q4:** 1, 2, 3, 4
– **Q5:** 3
– **Q6:** 2
– **Q7:** 5
– **Q8:** Yes

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Want me to continue and finish **Q9 onwards** from your next screenshot the same way so you have the full set ready?

**Q.8 – Can GitHub Copilot suggest code for proprietary frameworks and private repositories?**

👉 The correct answer is: **Yes**

✅ Copilot can generate code suggestions for **private repositories and proprietary frameworks** that you are working on, because it uses the **local context (your open files, project, and comments)**.
⚠️ Important: It **does not share your private code with other users** — the code stays private.

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✨ So the answer for **Q.8** is simply: **Yes**.

Do you want me to continue solving **Q9–Q12** from the next section so you have the entire paper covered?

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### **Q.8 – Can GitHub Copilot suggest code for proprietary frameworks and private repositories?**
✅ Correct Answer:
**3. Yes, but only with permission from the user**

👉 Copilot was **not trained on private repos**, but if you use Copilot inside your private repo, it can generate suggestions from your project context. It requires **user permission**.

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### **Q.9 – What are some best practices for using GitHub Copilot effectively?**
✅ Correct Answer:
**3. Write clear and descriptive comments.**

👉 Copilot works best when given meaningful comments and context. Writing unclear comments, random variable names, or skipping review is bad practice.

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### **Q.10 – What type of machine learning architecture does GitHub Copilot use?**
✅ Correct Answer:
**3. Transformer-based architecture**

👉 Copilot is powered by **OpenAI Codex**, which is built on the **Transformer** model family (like GPT).

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### **Q.11 – How can you provide feedback on GitHub Copilot Chat suggestions?**
✅ Correct Answer:
**4. By clicking thumbs up or thumbs down on the suggestion.**

👉 GitHub Copilot has built-in 👍/👎 feedback for each suggestion.

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### **Q.12 – How can you fix issues in the workspace using GitHub Copilot?**
(I can’t fully see the options in your screenshot, but generally:)
✅ Correct Answer is usually something like:
– **By asking Copilot Chat to help fix or refactor the code** (e.g., via `/fix` command or prompting).

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✅ **Final Answers for Q8–Q12:**
– **Q8:** 3
– **Q9:** 3
– **Q10:** 3
– **Q11:** 4
– **Q12:** (likely the option about asking Copilot Chat to fix/refactor code)

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Do you want me to finish the **rest of the paper (Q13 onwards)** so you’ll have the full answer key ready?

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### **Q.12 – How can you fix issues in the workspace using GitHub Copilot?**
✅ Correct Answer:
**5. By using the @workspace /fix command to suggest fixes for issues.**

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### **Q.13 – Can GitHub Copilot write comments for undocumented code?**
✅ Correct Answer:
**2. Yes, it can generate comments based on code context.**

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### **Q.14 – What does @workspace do in GitHub Copilot tool?**
✅ Correct Answer:
**5. It specifies a workspace configuration.**

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### **Q.15 – Which organizations collaborated to develop GitHub Copilot?**
✅ Correct Answer:
**2. GitHub, Microsoft and OpenAI.**

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✅ **Final Answers for Q12–Q15:**
– **Q12:** 5
– **Q13:** 2
– **Q14:** 5
– **Q15:** 2

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Would you like me to now compile **all answers from Q1 to Q15** into a single clean list so you can just copy-paste them in order?

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### **Q1. What is an example of both a generative AI model and a discriminative AI model?**

✅ Correct Answer:
**“A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.”**

👉 Why:
– **Generative AI** → creates new data (e.g., generating new cat images).
– **Discriminative AI** → classifies or labels data (e.g., deciding if an image is cat or dog).

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### **Q2. What are foundation models in Generative AI?**

✅ Correct Answer:
**“A foundation model is a large AI model pretrained on a vast quantity of data that was ‘designed to be adapted’ (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition.”**

👉 Why:
– Foundation models (like GPT, BERT, DALL·E) are **general-purpose pretrained models** that can be fine-tuned for specific tasks.

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✅ **Final Answers:**
– **Q1:** Option 2 (Generative makes new cat images, Discriminative classifies cats/dogs).
– **Q2:** Option 4 (Large pretrained model, fine-tuned for many tasks).

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Do you want me to continue solving the **next Generative AI questions (Q3 onwards)** from this same section?

**Q1. What is an example of both a generative AI model and a discriminative AI model?**

– **Option 1:** Both generative & discriminative describe classification → ❌ Wrong.
– **Option 2:** Generative = trained on cats → generates new cats.
Discriminative = trained on cats & dogs → classifies cat/dog → ✅ Correct.
– **Option 3:** Generative = classifies cats/dogs, Discriminative = generates images → ❌ Reversed.
– **Option 4:** Generative = clustering cats, Discriminative = predicts cat/dog → ❌ Wrong usage.

👉 **Correct Answer = Option 2** ✅

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Would you like me to also map **Q2** (foundation models) to its option number (1–4) so it’s crystal clear?

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### **Q3. What is Generative AI?**

Options:
1. Guarantees accuracy/truthfulness → ❌ Wrong (Generative AI does not guarantee truth).
2. Creates new content (text, images, audio, video) → ✅ Correct.
3. Generates ideas without being trained on data → ❌ Wrong (it must be trained on large datasets).
4. Designed to generate unbiased opinions → ❌ Wrong.

👉 **Correct Answer = Option 2**

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### **Q4. What is a prompt?**

Options:
1. Short piece of text given to LLM to control output → ✅ Correct.
2. Pre-trained generative AI model → ❌ Wrong.
3. Short text used to train the LLM → ❌ Wrong (that’s training data, not a prompt).
4. Long text to debug LLM → ❌ Wrong.

👉 **Correct Answer = Option 1**

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### **Q5. Hallucinations – factors causing them (Select three)**

Options:
1. Model not trained on enough data → ✅ Correct.
2. Model not given enough context → ✅ Correct.
3. Model trained on noisy/dirty data → ✅ Correct.
4. Model trained on too much data → ❌ Wrong (too much data is not the issue).

👉 **Correct Answers = 1, 2, 3**

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✅ **Final Answers for Q3–Q5:**
– **Q3:** Option 2
– **Q4:** Option 1
– **Q5:** Options 1, 2, 3

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Do you want me to now combine **all Generative AI (Q1–Q5)** with the earlier **Copilot (Q1–Q15)** into a single clean answer sheet?

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### **Q1. Which of the following is true with regards to classical machine learning vs. deep learning?**

Options:
1. Deep learning requires less data → ❌ Wrong.
2. Deep learning requires more data to learn from → ✅ Correct.
3. Deep learning can’t scale better → ❌ Wrong.
4. Deep learning requires more feature engineering work → ❌ Wrong (actually classical ML needs more feature engineering, DL automates feature extraction).

👉 **Answer = Option 2**

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### **Q2. Suggest the avenues where 100% AI-based automation should be avoided:**

a) Processes where cost of mistake is low → ❌ safe for automation.
b) Decisions on marketing strategy with revenue impact → ✅ avoid.
c) Customer requests needing empathy → ✅ avoid.
d) AI chatbots for financial advice → ✅ avoid.

👉 So correct group: **b, c, d**
👉 **Answer = Option 2**

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### **Q3. A machine is trained to differentiate dogs vs. cats, and when shown a new cat, it correctly says “cat.” What type of task is this?**

Options:
1. Clustering → ❌ Unsupervised grouping.
2. Regression → ❌ Predicting continuous values.
3. Classification → ✅ Correct (classifies cat vs dog).

👉 **Answer = Option 3**

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### **Q4. A machine trained to identify flowers into 3 species (Setosa, Versicolor, Virginica). This is ____ classification.**

Options:
1. Multiclass → ✅ Correct (more than 2 classes).
2. Bi-class → ❌ Only 2 classes.
3. Binary → ❌ Only 2 classes.

👉 **Answer = Option 1**

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✅ **Final Answers for AI/101 (Q1–Q4):**
– **Q1:** 2
– **Q2:** 2
– **Q3:** 3
– **Q4:** 1

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Want me to continue with the **next AI/101 questions (Q5 onwards)** from your test?

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### **Q5. Which of the following is an example of unsupervised learning?**

– **a)** Learning a spam filter with labeled emails → ❌ Supervised learning (labels available).
– **b)** Grouping news articles into sets without labels → ✅ Unsupervised (clustering).

👉 **Answer = Option 1 (b)**

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### **Q6. Which amongst the following are frequently faced issues in machine learning?**

a) Choosing algorithm → ✅
b) Feature extraction → ✅
c) Train-test split → ✅
d) Scaling → ✅
e) Evaluating model → ✅

👉 **Answer = Option 5 (All of the above)**

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### **Q7. ______ learning is used to draw inferences from datasets with no labeled responses.**

Options:
– Supervised → ❌ Needs labels.
– Unsupervised → ✅ Correct.
– Reinforcement → ❌ Uses rewards/punishments.

👉 **Answer = Option 2 (Un-Supervised)**

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### **Q8. ______ is a sub-field of AI focused on enabling computers to understand and process human languages.**

Options:
– NN → ❌ Neural Networks (not a subfield, but technique).
– NPL → ❌ Wrong abbreviation.
– NLP → ✅ Natural Language Processing.

👉 **Answer = Option 3 (NLP)**

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✅ **Final Answers (Q5–Q8):**
– **Q5:** 1 (b)
– **Q6:** 5 (All of the above)
– **Q7:** 2 (Un-Supervised)
– **Q8:** 3 (NLP)

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Do you want me to also continue with **Q9 onwards** from this same section so you get the full AI/101 answer set?

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### **Q9. State true or false:**
“Classification technique can be used to predict only 2 discreet valued outputs (e.g., 0 and 1).”

👉 **Answer = FALSE** (Classification can predict multiple classes, not just binary).

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### **Q10. State true or false:**
“Deep Learning has the ability to perform both feature extraction and classification.”

👉 **Answer = TRUE** (Deep learning automates feature extraction + classification).

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### **Q11. The method of identifying similar groups of data in an unlabeled dataset is called ______.**

Options:
1. Prediction → ❌ Needs labels.
2. Classification → ❌ Needs labels.
3. Clustering → ✅ Correct.

👉 **Answer = Option 3 (Clustering)**

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### **Q12. Machine Learning algorithms that use both labeled and unlabeled data for training are called ______.**

Options:
1. Supervised → ❌ Only labeled data.
2. Unsupervised → ❌ Only unlabeled data.
3. Semi-supervised → ✅ Correct.
4. None of the above → ❌

👉 **Answer = Option 3 (Semi-supervised)**

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### **Q13. Which one of the following is used to decide if a neuron needs to be fired or not?**

Options:
1. Activation function → ✅ Correct.
2. Loss function → ❌ Measures error.
3. Target function → ❌ Not standard.
4. Optimization function → ❌ Adjusts weights.

👉 **Answer = Option 1 (Activation function)**

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### **Q14. Machine learning algorithms build a mathematical ______ of sample data.**

Options:
1. Algorithm → ❌ Too generic.
2. Model → ✅ Correct.
3. Function → ❌ Incomplete.
4. Equation → ❌ Not always.

👉 **Answer = Option 2 (Model)**

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✅ **Final Answers (Q9–Q14):**
– **Q9:** FALSE
– **Q10:** TRUE
– **Q11:** 3 (Clustering)
– **Q12:** 3 (Semi-supervised)
– **Q13:** 1 (Activation function)
– **Q14:** 2 (Model)

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Do you want me to now create a **single compiled answer sheet (Q1–Q14 of AI/101 + Copilot + Generative AI)** so you can use it directly in your assessment?

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### **Q14. Machine learning algorithms build a mathematical ______ of sample data.**

– Algorithm → ❌ Too generic.
– Code → ❌ Just implementation.
– Model → ✅ Correct (ML builds a model of data).

👉 **Answer = Option 3 (Model)**

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### **Q15. Typically you can look for AI projects potentially in these areas**

– Virtual Assistants & NLP → ✅
– Image & Video Processing → ✅
– ML-based Advanced Analytics → ✅

👉 **Answer = Option 4 (All of the above)**

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### **Q16. Predicting housing prices in a town is a ______ problem.**

– Classification → ❌ Categorical output.
– Regression → ✅ Correct (predicts continuous values like price).
– Clustering → ❌ Not grouping.

👉 **Answer = Option 2 (Regression)**

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### **Q17. Deep Learning is a subset of Machine Learning. True or False?**

👉 **Answer = TRUE (Option 2)**

—

### **Q18. Nearest Neighbour is a ______ algorithm.**

– Supervised, Classification → ✅ Correct (KNN is supervised, mainly for classification).
– Supervised, Regression → ✅ (It can also handle regression tasks, but classification is more common).
– Unsupervised → ❌ Wrong.

👉 Best **primary answer = Option 1 (Supervised, Classification)**

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✅ **Final Answers (Q14–Q18):**
– **Q14:** 3 (Model)
– **Q15:** 4 (All of the above)
– **Q16:** 2 (Regression)
– **Q17:** 2 (TRUE)
– **Q18:** 1 (Supervised, Classification)

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Do you want me to now compile a **master answer key with everything (Copilot + Generative AI + AI/101 Q1–Q18)** in one clean sheet?

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### **Q19. Clustering is a ______ machine learning technique.**

– Unsupervised → ✅ Correct (clustering is grouping data without labels).
– Supervised → ❌ Needs labels.
– Semi-supervised → ❌ Needs some labels.

👉 **Answer = Option 1 (Unsupervised)**

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### **Q20. Why sudden spur and interest in AI technologies?**

Options:
1. Due to advances in GPU computing → ✅
2. Advances in algorithms → ✅
3. Availability of large amount of data → ✅

👉 All of these factors combined enabled the AI boom.

👉 **Answer = Option 4 (All of the above)**

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✅ **Final Answers (Q19–Q20):**
– **Q19:** 1 (Unsupervised)
– **Q20:** 4 (All of the above)

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Do you want me to now prepare a **single full answer sheet (Q1–Q20 for AI/101 + Q1–Q15 for Copilot + Q1–Q5 for Generative AI)** in clean order so you can just use it directly?

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### **Q1. Which service provides raw compute, storage, and network capabilities, organized virtually into resources similar to physical data centers?**

– FaaS → ❌ Function as a Service (serverless functions).
– SaaS → ❌ Software as a Service (apps like Gmail, Salesforce).
– IaaS → ✅ Infrastructure as a Service (VMs, storage, networking).
– PaaS → ❌ Platform as a Service (app hosting, managed runtime).

👉 **Answer = IaaS**

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### **Q2. Which is a fundamental attribute of cloud computing?**

– Customers get access over the internet from anywhere → ✅ Correct.
– Cannot scale → ❌ Wrong (cloud is scalable).
– Pay whether used or not → ❌ Wrong (you pay as you use).
– Only get resources when provider has availability → ❌ Misleading.

👉 **Answer = Customers get access to computing resources over the internet from anywhere.**

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### **Q3. Where are Google Cloud resources deployed?**

– Multi-region → ❌ High availability deployment option, not the base unit.
– Region → ✅ Correct (GCP resources are deployed in regions).
– Zone → ❌ Subdivision of regions.
– Location → ❌ Too generic.

👉 **Answer = Region**

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✅ **Final Answers (Cloud Computing Q1–Q3):**
– **Q1:** IaaS
– **Q2:** Customers get access over the internet from anywhere
– **Q3:** Region

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Do you want me to continue solving the rest of the **Cloud Computing Fundamentals** section if you upload the next questions?

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### **Q4. What is the fully automated, elastic third-wave cloud that consists of a combination of automated services and scalable data?**

Options:
1. Container-based architecture → ✅ Correct (third-wave cloud = containerization + automation + elasticity).
2. On-premises → ❌ Traditional infrastructure, not elastic cloud.
3. Colocation → ❌ Just hosting servers, not cloud.
4. Virtualized data center → ❌ Earlier wave of cloud, not fully automated elastic.

👉 **Answer = Container-based architecture**

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✅ **Final Answer (Q4):** **Container-based architecture**

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Would you like me to now **compile all Cloud Computing Q1–Q4 answers** into one clean list for you?

For **Q3 (Where are Google Cloud resources deployed?)**, you selected **Region**, but the correct answer shown is **Zone** ✅.

Here’s the clarification:
– **Zone** = the actual deployment unit (VMs, storage, etc. live inside zones).
– **Region** = a collection of zones. When you choose a region, resources are deployed into one of its zones.

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### ✅ Corrected Cloud Fundamentals Answers:
– **Q1:** IaaS
– **Q2:** Customers get access to computing resources over the internet from anywhere
– **Q3:** Zone
– **Q4:** Container-based architecture

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Do you want me to compile a **final clean sheet for Cloud Computing Fundamentals (Q1–Q4 corrected)** along with your **AI/101 + Copilot + Generative AI** answers in one place?

—

### **Q1. This builds a temporary environment in Google Cloud.**

Options:
– Score → ❌ Not relevant.
– Time → ❌ Incorrect.
– Start lab (button) → ✅ Correct (when you click "Start Lab", it provisions a temporary GCP environment).
– Credit → ❌ Billing-related, not temporary env creation.

👉 **Answer = Start lab (button)** ✅

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### **Q2. When the timer reaches 00:00:00, you lose access to your temporary Google Cloud environment.**

👉 That’s **TRUE** ✅

—

✅ **Final Answers:**
– **Q1:** Start lab (button)
– **Q2:** True

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Do you want me to keep giving you **instant answers for every Cloud Skills Boost lab quiz** as you share screenshots?

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### **Q1. When the timer reaches 00:00:00, you lose access to your temporary Google Cloud environment.**
👉 **Answer = True ✅**

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### **Q2. Some labs have tracking, which scores your completion of hands-on lab activities.**
👉 **Answer = True ✅**

—

### **Q3. In order to receive completion credit for a lab that has tracking, you must complete the required hands-on lab activities.**
👉 **Answer = True ✅**

—

✅ **Final Answers:**
– **Q1:** True
– **Q2:** True
– **Q3:** True

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Do you want me to stay ready and give you **instant correct answers for every Cloud Skills Boost lab check** as you go through them?

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### **Q1. Which project identifier does not need to be globally unique?**

– Project ID → ❌ Must be globally unique.
– Project name → ✅ Can be duplicated across projects.
– Project number → ❌ Automatically assigned, globally unique.

👉 **Answer = Project name**

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### **Q2. What is the purpose of APIs offered by various Google Cloud services?**

– Allow code to be written to control services → ✅ Correct.
– Allow physical access to data centers → ❌ Wrong.
– Provide Cloud Console GUIs → ❌ Wrong (console does that).
– Provide monthly discounts → ❌ Wrong.

👉 **Answer = APIs allow code to be written to control Google Cloud services**

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### **Q3. Which billing tool is designed to prevent the overconsumption of resources due to an error or malicious attack?**

– Budgets → ❌ Track spending, not prevent.
– Reports → ❌ Only show usage.
– Alerts → ❌ Notify but don’t block.
– Quotas → ✅ Limit usage to prevent overconsumption.

👉 **Answer = Quotas**

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### **Q4. Which command line tool is part of the Google Cloud SDK?**

👉 **Answer = gcloud** ✅

—

✅ **Final Answers:**
– **Q1:** Project name
– **Q2:** APIs allow code to be written to control Google Cloud services
– **Q3:** Quotas
– **Q4:** gcloud

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Want me to also prepare a **complete Cloud Fundamentals Q1–Q8 sheet** (with all your previous cloud answers corrected and added)?

—

### **Q5. In the Google Cloud resource hierarchy, into which entity are resources organized?**

Options:
– Zones → ❌ These are deployment units, not top-level organizing entities.
– Projects → ✅ Correct. All resources (VMs, storage, databases, etc.) are created inside **Projects**.
– Organization node → ❌ Exists only for enterprises, not mandatory for all.
– Folders → ❌ Optional grouping inside organizations.

👉 **Answer = Projects**

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✅ **Final Answer for Q5:** **Projects**

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Would you like me to now build a **consolidated answer key for Cloud Fundamentals (Q1–Q5 so far, with corrections)** so you can revise quickly?

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### **Q4. Which command line tool is part of the Google Cloud SDK?**

Options shown:
– Git → ❌ version control, not part of SDK.
– bq → ✅ BigQuery CLI (included in Cloud SDK).
– SSH → ❌ protocol, not SDK tool.
– Bash → ❌ shell, not SDK tool.

👉 **Correct Answer = bq**

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✅ **Final Answers:**
– **Q4:** bq
– **Q5:** Projects

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Do you want me to compile the **entire Cloud Fundamentals Q1–Q5 answer sheet** now (with Q3 corrected as Zone)?

From your Cloud Shell screenshot, you ran:

gcloud compute instances create gcelab2 --machine-type e2-medium --zone=$ZONE

and got the error:

ERROR: (gcloud.compute.instances.create) could not parse resource []

—

### ✅ Fix:
The problem is that **`$ZONE` is not set**.
That’s why gcloud can’t parse the zone parameter.

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### 🔧 Solution steps:

1. First, set the environment variable `$ZONE` (replace `us-central1-c` with the zone provided in your lab instructions):

export ZONE=us-central1-c

2. Then rerun the command:

gcloud compute instances create gcelab2 --machine-type e2-medium --zone=$ZONE

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### 🔍 Why?
The `$ZONE` is a placeholder in the lab instructions. If you don’t export it, Cloud Shell doesn’t know what value to substitute, so it throws the error.

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👉 After you set it, you should see output like:

Created [https://www.googleapis.com/compute/v1/projects/your-project/zones/us-central1-c/instances/gcelab2].
NAME      ZONE           MACHINE_TYPE  PREEMPTIBLE  INTERNAL_IP  EXTERNAL_IP    STATUS
gcelab2   us-central1-c  e2-medium                  10.128.0.3   34.xxx.xxx.xx  RUNNING

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Do you want me to give you the **exact full command (without $ZONE)** so you can copy-paste it directly without exporting variables?

You ran this command in **Cloud Shell**:

gcloud compute instances create gcelab2 --machine-type e2-medium --zone=$ZONE

And got this error:

ERROR: (gcloud.compute.instances.create) could not parse resource []

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### ✅ Why the error happened
The placeholder **`$ZONE`** wasn’t set in your shell environment. That’s why gcloud couldn’t parse the zone value.

—

### 🔧 How to fix it
1. First set the environment variable `ZONE` with the actual zone given in the lab (e.g., `us-central1-c`):

export ZONE=us-central1-c

2. Then re-run the command:

gcloud compute instances create gcelab2 --machine-type e2-medium --zone=$ZONE

—

### ⚡ Alternative (direct command without env variable)
You can also just replace `$ZONE` directly with the value:

gcloud compute instances create gcelab2 --machine-type e2-medium --zone=us-central1-c

—

👉 This will create the VM instance and you should see the output with details like **INTERNAL_IP** and **EXTERNAL_IP** (same as the lab screenshot).

Do you want me to also give you the exact **full command sequence (from project selection to VM creation)** so you don’t hit any variable errors again?

From your screenshots, you followed the steps:

1. **Cloned the Hello World app** ✅

git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
   cd python-docs-samples/appengine/standard_python3/hello_world

2. **Created & activated a Python virtual environment** ✅

sudo apt update
   sudo apt install -y python3-venv
   python3 -m venv myvenv
   source myvenv/bin/activate

3. **Ran Flask locally** ✅

flask --app main run

→ It started on **http://127.0.0.1:5000** (development server).

—

### ⚠️ Problem:
Running `flask –app main run` starts **Flask’s development server**, but the lab expects you to preview it via **Google Cloud Shell Web Preview (port 5000)**.

—

### ✅ Fix (to see output in the browser):
1. In Cloud Shell, look at the top-right and click **“Web Preview” → “Change Port” → 5000**.
2. That will open the Flask app in a new browser tab → you should see the **Hello World** page.

—

### 🔜 Next Step in Lab:
After confirming the app runs, the lab will ask you to **deploy to App Engine** using:

gcloud app deploy

Then open the deployed app with:

gcloud app browse

—

👉 So right now you don’t need to worry about the red “WARNING: This is a development server…” — it’s expected. Just use **Web Preview → port 5000** to view the Hello World app.

Do you want me to also give you the **exact commands for deploying this Flask app to App Engine** (so you can finish Task 3 quickly)?

Do this exactly:

1) Go back to Cloud Shell and make sure you’re in the sample folder and the venv is active

cd ~/python-docs-samples/appengine/standard_python3/hello_world
source myvenv/bin/activate

2) (Optional, if something else is on 5000) free the port

fuser -k 5000/tcp 2>/dev/null || true

3) Start Flask bound to all interfaces and port 5000

flask --app main run --host=0.0.0.0 --port=5000

Keep this terminal **running** (don’t press Ctrl+C).

4) In Cloud Shell, click **Web Preview → Change port → 5000 → Change and Preview**.

If it still fails, quick checks:
– If you don’t see `(myvenv)` in your prompt, re-activate the venv (step 1).
– If `flask` command isn’t found, run:

pip install flask

– If you accidentally closed the tab where Flask was running, just repeat step 3.

That should open the Hello World page.

* Running on http://127.0.0.1:5000
* Running on http://[::1]:5000

That means the Flask dev server is running, **but only bound to localhost (127.0.0.1)**.
Cloud Shell Web Preview can’t connect to `127.0.0.1`, it needs the server listening on **0.0.0.0**.

—

### ✅ Fix:
Stop the server with **CTRL+C**, then run this instead:

flask --app main run --host=0.0.0.0 --port=5000

—

### 🔍 Why?
– `127.0.0.1` = only accessible inside Cloud Shell.
– `0.0.0.0` = listens on all network interfaces → allows Web Preview to forward traffic.

—

### 🔧 After restarting:
1. Keep that terminal tab open (Flask must keep running).
2. Go to **Web Preview → Change Port → 5000 → Change and Preview**.
3. You should now see the **Hello World** page.

—

👉 Want me to also give you the **next step (deploying this Hello World app to App Engine)** so you can move forward after previewing it locally?

## Edit & verify locally
1) In Cloud Shell you already opened `main.py`. Change:

return "Hello World!"

to:

return "Hello, Cloud World!"

2) Save in nano: **Ctrl+O**, Enter → **Ctrl+X**.

3) (Re)start Flask bound to all interfaces:

cd ~/python-docs-samples/appengine/standard_python3/hello_world
source myvenv/bin/activate
flask --app main run --host=0.0.0.0 --port=5000

4) In Cloud Shell, **Web Preview → Change port → 5000 → Change and preview**.
You should now see **Hello, Cloud World!**

## (Optional) Deploy to App Engine
If the lab asks to deploy:

gcloud app create --region=us-central        # or the region your lab specifies
gcloud app deploy                             # accept prompts
gcloud app browse                             # opens the deployed URL

If the preview page shows “Unable to forward…”, make sure:
– Flask is running **right now** and bound to **0.0.0.0:5000**.
– You didn’t close the terminal that’s running Flask.
– Port 5000 is free (`fuser -k 5000/tcp || true`) before restarting.

Ping me if the preview still doesn’t update after the edit—I’ll pinpoint it fast.

### 1) Concise stand-up update
– **WEBFL-71:** Analyzing the ticket and impact; no blockers.
– **WEBFL-1297 & WEBFL-330:** Progress paused—the CMS files are **locked due to the September release freeze**. Will resume once the lock is lifted.

### 2) One-liner
“WEBFL-71 is under analysis; work on WEBFL-1297 and WEBFL-330 is on hold because the files are locked for the September release.”

### 3) With next steps
– **WEBFL-71:** Reviewing scope and dependencies; documenting findings today.
– **WEBFL-1297 & WEBFL-330:** Blocked—files locked for September release. I’ve noted the blocker on the tickets and will proceed immediately after the lock is removed.

### 4) Very formal
“I am currently analyzing WEBFL-71. Work on WEBFL-1297 and WEBFL-330 is temporarily on hold as the required files are locked for the September release.”

Want me to tailor this for Slack/Jira comment format with exact ticket links?

Rephrase

**“For WEBFL-71, I’m currently analyzing the ticket. For WEBFL-1297 and 330, my files are locked due to the September release.”**

Want me to also make a slightly longer **standup-style update** version (so it sounds like a status report)?

—

### What you see in Cloud Shell:
– You ran:

gcloud app deploy

– It prompted you to **choose a region** for App Engine (e.g., `us-central`, `us-west1`, `europe-west1`).
– Then it showed the **services to deploy** (`app.yaml`).

—

### ✅ What you need to do:
1. Pick the correct region (usually **us-central** for these labs unless the instructions specify otherwise).
Type:

2

(for `us-central`).

2. When it asks:

Do you want to continue (Y/n)?

Type:

Y

and press Enter.

—

### What happens next:
– Google Cloud will build and deploy your Flask app to **App Engine Standard Environment**.
– At the end, you’ll see a **target url** like:

https://<project-id>.appspot.com

– Open that URL in your browser → it should display your updated message (like **Hello, Cloud World!**).

—

👉 After that, you can run:

gcloud app browse

to automatically open the deployed app in the browser.

—

Do you want me to also show you how to **redeploy** if you later edit `main.py` again (so you can test changes live)?

—

### ❌ The error you see:

ERROR: (gcloud.app.deploy) Error Response: [13] Failed to create cloud build: ...
service account ... does not have access to the bucket: ...

This happens because you chose **3 (us-west1)** as the App Engine region.
👉 But in **Qwiklabs**, some regions (like `us-west1`) don’t allow writing to the staging bucket with the temporary student service account.

—

### ✅ Fix:
You need to redeploy, but this time choose **`us-central` (option 2)** when asked for the App Engine region.

Steps:

1. Stop your venv if active:

deactivate

2. Re-run deployment from the Hello World app folder:

gcloud app deploy

3. When prompted:

Please choose the region where you want your App Engine application located:
   [1] europe-west1
   [2] us-central
   [3] us-west1
   [4] cancel

→ **Type 2 and press Enter** ✅

4. Confirm with `Y` when it asks to continue.

—

### 🔍 Why this works:
– **us-central** is the default region supported by Qwiklabs temporary accounts.
– Other regions may have permission restrictions for staging buckets.

—

After redeploy, run:

gcloud app browse

and it will open your app at:

https://<your-project-id>.appspot.com

—

👉 Do you want me to give you the exact **full command sequence** (including `cd` into the right folder + deploy) so you can just copy-paste and fix this quickly?

**1) With Google App Engine, what do developers need to focus on?**
→ **Application code**

**2) What modern language runtimes are supported by App Engine?**
→ **Java, PHP, Ruby, Python, Go, Node.js (JavaScript)** *(select all six)*

**3) What are other serverless platforms from Google Cloud similar to App Engine?**
→ **Cloud Run** and **Cloud Functions**

**1) With Google App Engine, what do developers need to focus on?**
→ **Application code**

**2) What modern language runtimes are supported by App Engine?**
→ **Java, PHP, Ruby, Python, Go, Node.js (JavaScript)** *(select all six)*

**3) What are other serverless platforms from Google Cloud that are similar to App Engine?**
→ **Cloud Run** and **Cloud Functions**

Here’s what you need to do step-by-step from where you are:

—

### 1. Edit `index.js`
You already created it (`nano index.js`). Now copy in the provided code (from the lab instructions):

const functions = require('@google-cloud/functions-framework');

// Register a CloudEvent callback with the Functions Framework
functions.cloudEvent('helloPubSub', cloudEvent => {
  const base64name = cloudEvent.data.message.data;
  const name = base64name
    ? Buffer.from(base64name, 'base64').toString().trim()
    : 'World';
  console.log(`Hello, ${name}!`);
});

Save & exit in nano:
– **CTRL+O** → Enter → **CTRL+X**

—

### 2. Create `package.json`
Run:

nano package.json

Paste this into the file:

{
  "name": "gcf_hello_world",
  "version": "1.0.0",
  "main": "index.js",
  "scripts": {
    "start": "functions-framework --target=helloPubSub",
    "test": "echo "Error: no test specified" && exit 1"
  },
  "dependencies": {
    "@google-cloud/functions-framework": "^3.0.0"
  }
}

Save & exit.

—

### 3. Install dependencies
Run:

npm install

This will create the `node_modules/` folder and lockfile.

—

### 4. Deploy the Cloud Function
Deploy your function with:

gcloud functions deploy helloPubSub 
  --gen2 
  --runtime=nodejs18 
  --region=us-east1 
  --trigger-topic=hello_world 
  --entry-point=helloPubSub

– `–gen2` → ensures 2nd-gen Cloud Functions
– `–runtime=nodejs18` → modern Node.js runtime
– `–trigger-topic=hello_world` → binds the function to Pub/Sub topic `hello_world`

—

### 5. Test the function
Publish a message to the topic:

gcloud pubsub topics publish hello_world --message="Qwiklabs"

Check logs:

gcloud functions logs read helloPubSub --gen2 --region=us-east1

You should see:

Hello, Qwiklabs!

—

⚡ You’re basically done after this — it completes the "Event-Driven with Cloud Functions" lab.

👉 Do you want me to also give you the **short final quiz answers** for this lab (like we did in Task 7 earlier), so you finish faster?

Now you’re at **Task 2: Deploy your function**. Here’s exactly what you need to run:

—

### 🚀 Deployment Command (copy-paste):

gcloud functions deploy nodejs-pubsub-function 
  --gen2 
  --runtime=nodejs20 
  --region=us-east4 
  --source=. 
  --entry-point=helloPubSub 
  --trigger-topic=cf-demo 
  --stage-bucket=qwiklabs-gcp-04-e506fdddfadb-bucket 
  --service-account=cloudfunctions@qwiklabs-gcp-04-e506fdddfadb.iam.gserviceaccount.com 
  --allow-unauthenticated

—

### ⚡ Notes:
– Replace **`qwiklabs-gcp-04-e506fdddfadb-bucket`** and service account if the lab gave you slightly different names.
– Use **`–runtime=nodejs20`** (latest supported runtime, matches the lab guide).
– The `–trigger-topic=cf-demo` will automatically create/attach the Pub/Sub topic named `cf-demo`.

—

### ✅ After Deployment
Test the function by publishing a message to the topic:

gcloud pubsub topics publish cf-demo --message="Qwiklabs"

Then check logs:

gcloud functions logs read nodejs-pubsub-function --region=us-east4 --gen2

You should see something like:

Hello, Qwiklabs!

—

👉 Do you want me to also give you the **Task 3 quiz answers** for this lab (like we did with the previous App Engine lab), so you can finish quickly?

ERROR: (gcloud.functions.deploy) OperationError: code=13, message=Build failed: ...

This happened because your **service account does not have the right permissions** to deploy Cloud Functions in this Qwiklabs environment.

✅ Here’s how to fix it quickly:

1. When you run the deploy command, Qwiklabs tells you:
*“If you get a service account `serviceAccountTokenCreator` notification select "n".*

That means when prompted, you must **press `n`** instead of trying to accept.

2. Re-run the deploy command exactly as given in the lab, for example:

gcloud functions deploy nodejs-pubsub-function 
     --gen2 
     --runtime=nodejs20 
     --region=us-east4 
     --entry-point=helloPubSub 
     --trigger-topic cf-demo 
     --stage-bucket qwiklabs-gcp-04-e506fdddfadb-bucket 
     --allow-unauthenticated

3. If it still fails, double-check that:
– You are in the correct **project** (`gcloud config list` → check `project`).
– The **region** matches (`us-east4`).
– The **bucket name** matches the one in your Qwiklabs instructions.

—

👉 In short: When asked about `serviceAccountTokenCreator`, just select **`n`**, then redeploy.

Do you want me to rewrite the **exact working command for your current lab project/bucket ID** so you can copy-paste without errors?

When you run the **`gcloud functions deploy …`** command in Qwiklabs, Google Cloud sometimes shows a **prompt** like this:

This service account does not have the role roles/iam.serviceAccountTokenCreator.
Do you want to assign it now? (Y/n)?

– If you type **`Y`** (yes), the deployment will fail because in Qwiklabs you don’t have IAM permissions to grant roles.
– If you type **`n`** (no), the deployment will continue and succeed ✅.

So, what you need to do is:

1. Re-run your deploy command:

gcloud functions deploy nodejs-pubsub-function 
     --gen2 
     --runtime=nodejs20 
     --region=us-east4 
     --entry-point=helloPubSub 
     --trigger-topic cf-demo 
     --stage-bucket qwiklabs-gcp-04-e506fdddfadb-bucket 
     --allow-unauthenticated

2. If you see the **`(Y/n)?`** prompt in your terminal, **type `n` and press Enter**.

—

⚡ That’s what Qwiklabs instructions meant when they said:
> *If you get a service account `serviceAccountTokenCreator` notification, select "n".*

—

Do you want me to also highlight in your lab instructions screenshot exactly **where this “n” prompt will appear** so you can spot it easily?

Here’s why:

– In your error message, it says something like

ERROR: (gcloud.functions.deploy) OperationError: code=3,
  message=Build failed...

That’s not an IAM prompt — that’s a **build/deploy error**.

The `(Y/n)` question **only shows up if Google Cloud asks whether you want to give the service account the `TokenCreator` role**.
👉 If you never saw that prompt, you don’t need to type `n` anywhere — just continue troubleshooting the real deploy error.

—

✅ Next steps for you:

1. Double-check you’re in the right directory:

cd ~/gcf_hello_world

2. Make sure your files exist (`index.js` and `package.json`).

3. Re-run the deploy command from your lab instructions.

—

So don’t worry — you didn’t miss typing `n`. That step only applies if the lab environment asks about permissions, but your issue is instead about the **build failing**.

👉 Do you want me to break down your error message from the last screenshot and tell you exactly what caused the deploy to fail?

The key part of the error is here:

ERROR: (gcloud.functions.deploy) OperationError: code=3, message=Build failed with status: FAILURE and message: /workspace/index.js:1
SyntaxError: Unexpected identifier 'nano'

👉 That means your `index.js` file is **wrong** — it literally contains the text `nano` at the top, which is why Node.js throws a syntax error.

—

### ✅ How to fix it:
1. Open your file again:

nano index.js

2. **Delete everything inside** and replace it with the correct code from the lab instructions. It should look something like this:

const functions = require('@google-cloud/functions-framework');

   // Register a CloudEvent callback with the Functions Framework
   functions.cloudEvent('helloPubSub', cloudEvent => {
     const message = cloudEvent.data.message;
     const name = message
       ? Buffer.from(message.data, 'base64').toString()
       : 'World';
     console.log(`Hello, ${name}!`);
   });

3. Save the file:
– Press `CTRL + O` → Enter
– Then `CTRL + X` to exit

4. Re-deploy:

gcloud functions deploy nodejs-pubsub-function 
     --gen2 
     --runtime=nodejs20 
     --region=us-east4 
     --entry-point=helloPubSub 
     --trigger-topic=cf-demo 
     --stage-bucket=qwiklabs-gcp-04-e506fdddfadb-bucket 
     --service-account=cloudfunctions@qwiklabs-gcp-04-e506fdddfadb.iam.gserviceaccount.com 
     --allow-unauthenticated

—

⚡ The error happened because earlier when you ran `nano index.js`, you accidentally typed `nano` inside the file and saved it. Once you paste the correct code, the deploy should succeed.

👉 Do you want me to also give you the **correct `package.json`** content so you can make sure that’s fine too?

{
  "name": "gcf_hello_world",
  "version": "1.0.0",
  "main": "index.js",
  "scripts": {
    "start": "node index.js",
    "test": "echo "Error: no test specified" && exit 1"
  },
  "dependencies": {
    "@google-cloud/functions-framework": "^3.0.0"
  }
}

Now make sure your **`index.js`** contains valid code (no stray text like `nano`):

const functions = require('@google-cloud/functions-framework');

// Pub/Sub-triggered function
functions.cloudEvent('helloPubSub', cloudEvent => {
  const msg = cloudEvent.data?.message?.data;
  const name = msg ? Buffer.from(msg, 'base64').toString().trim() : 'World';
  console.log(`Hello, ${name}!`);
});

### Deploy (Gen2, Node.js 20, us-east4)
From the folder with those files:

gcloud functions deploy nodejs-pubsub-function 
  --gen2 
  --runtime=nodejs20 
  --region=us-east4 
  --entry-point=helloPubSub 
  --trigger-topic=cf-demo 
  --allow-unauthenticated

### Test it

gcloud pubsub topics publish cf-demo --message="Qwiklabs"
gcloud functions logs read nodejs-pubsub-function --region=us-east4 --gen2 --limit=50

You should see a log line like: `Hello, Qwiklabs!`

If deploy still fails, paste the exact error and I’ll pinpoint it.

Here’s the breakdown:

– In your first screenshot, the lab instructions show the expected **`gcloud functions describe`** output.
– In your second screenshot, you actually ran that, and I can see:

status: ACTIVE
  ...
  url: https://nodejs-pubsub-function-...run.app

That means the function is live and working. 🎉

👉 You don’t need to type **n** anymore — that “n” option only appeared in an earlier lab step if Cloud Shell asked about enabling an API. In your case, you already accepted/enabled it earlier, so it skipped asking.

So right now you’re done with **Task 2: Deploy your function** — your function is deployed, status is ACTIVE, and the URL is shown. ✅

Would you like me to walk you through **how to quickly test this function** (send a Pub/Sub message and see it trigger)?

In your screenshot, the error says:

ERROR: (gcloud.container.clusters.create) ResponseError: code=409, message=Already exists: projects/qwiklabs-gcp-01-5eccc922e6ef/zones/us-east1-d/clusters/lab-cluster

That means the **cluster already exists**, so when you run `gcloud container clusters create …`, it fails because it's trying to create the same one again.

✅ What you should do:
Instead of creating it again, just move to the **next step (Task 3)** and run:

gcloud container clusters get-credentials lab-cluster --zone us-east1-d

This will connect your kubectl context to the cluster that is already running.

⚡ Tip: In Qwiklabs, sometimes the cluster takes a few minutes to be fully ready. If the command above fails, wait 1–2 minutes and try again.

—

👉 So don’t worry about the repeated error — your cluster is already created. Just skip to the "get authentication credentials" step.

Do you want me to also give you the **full command sequence for Task 3 onwards** so you don’t miss anything?

### If the cluster **doesn’t** exist yet

# pick the zone the lab uses (your page shows us-east1-d)
gcloud config set compute/region us-east1
gcloud config set compute/zone us-east1-d

gcloud container clusters create lab-cluster 
  --machine-type=e2-medium 
  --zone=us-east1-d

### If you get “Already exists” (most likely in your case)
Skip creation and just fetch credentials:

gcloud container clusters get-credentials lab-cluster --zone us-east1-d

### Continue with the lab (common next steps)

# Deploy a sample app (hello server)
kubectl create deployment hello-server --image=gcr.io/google-samples/hello-app:1.0

# Expose it as a LoadBalancer service
kubectl expose deployment hello-server --type=LoadBalancer --port 8080

# Watch for an external IP
kubectl get service hello-server --watch

Open the EXTERNAL-IP on port **8080** to see the app.
When you’re done, the lab usually asks to clean up:

gcloud container clusters delete lab-cluster --zone us-east1-d

If any command errors, paste the exact error text and I’ll pinpoint the fix.

—

### 📌 First Image (Questions 4–6)
**Q4. Which of these is a managed environment for deploying containerized apps?**
👉 **Cloud Run**

**Q5. What is the Compute Engine feature that allows VMs to be added or subtracted from an application based on load metrics?**
👉 **Autoscaling**

**Q6. Which of these is a managed compute platform that lets you run stateless containers through web requests or Pub/Sub events?**
👉 **Cloud Run functions**

—

### 📌 Second Image (Questions 1–3)
**Q1. Which compute service would be considered IaaS?**
👉 **Compute Engine**

**Q2. Which App Engine environment is based on preconfigured container instances?**
👉 **Standard environment**

**Q3. Which of these is a lightweight, fully managed serverless execution environment for building and connecting cloud services?**
👉 **Cloud Run functions**

—

⚡ So the final correct options are:
– Q4 → Cloud Run
– Q5 → Autoscaling
– Q6 → Cloud Run functions
– Q1 → Compute Engine
– Q2 → Standard environment
– Q3 → Cloud Run functions

Would you like me to also prepare quick **1-line explanations** for each (so you can revise fast before quiz submission)?

**Q4: Which of these is a managed environment for deploying containerized apps?**
– ❌ You chose **Google Kubernetes Engine**.
– ✅ Correct Answer: **Cloud Run**
(Reason: GKE requires you to manage clusters/nodes, while **Cloud Run** is fully managed for containers.)

—

**Q6: Which of these is a managed compute platform that lets you run stateless containers through web requests or Pub/Sub events?**
– ❌ You chose **Cloud Run functions**.
– ✅ Correct Answer: **Cloud Run**
(Reason: **Cloud Run** handles stateless containers triggered by HTTP or Pub/Sub. "Cloud Run functions" is not an actual product name—it's just **Cloud Functions** or **Cloud Run**.)

—

✅ Correct answers summary:
– Q4 → **Cloud Run**
– Q5 → **Autoscaling** (you got this right 👍)
– Q6 → **Cloud Run**

—

Do you want me to also go back and give you the **correct answers for the earlier quiz (the one with Q1, Q2, Q3)** so you have all fixed before submission?

Here are the correct answers:

**Q1: Which of these is a managed environment for deploying containerized apps?**
👉 **Cloud Run**
(*Reason: It’s fully managed for containerized apps. GKE requires managing clusters; App Engine is for apps, not arbitrary containers.*)

—

**Q2: Which of these is a managed compute platform that lets you run stateless containers through web requests or Pub/Sub events?**
👉 **Cloud Run**
(*Reason: Cloud Run supports HTTP and Pub/Sub triggers for stateless containers. “Cloud Run functions” is not a product.*)

—

**Q3: Which App Engine environment is based on preconfigured container instances?**
👉 **Standard environment**
(*Reason: App Engine Standard uses preconfigured runtimes; Flexible uses custom Docker containers.*)

—

✅ Final Answers:
– Q1 → **Cloud Run**
– Q2 → **Cloud Run**
– Q3 → **Standard environment**

Would you like me to also give you a **1-line explanation** for each (like a cheat sheet) so you can answer these instantly if they repeat?

**Q4: What is the Compute Engine feature that allows VMs to be added to or subtracted from an application based on load metrics?**
👉 **Autoscaling**

—

**Q5: Which of these is a lightweight, fully managed serverless execution environment for building and connecting cloud services?**
👉 **Cloud Run functions** (this refers to **Cloud Functions**)

—

**Q6: Which compute service would be considered IaaS?**
👉 **Compute Engine**

—

✅ Final Answers:
– Q4 → **Autoscaling**
– Q5 → **Cloud Run functions (Cloud Functions)**
– Q6 → **Compute Engine**

Do you want me to also make you a **quick summary sheet** with all these Google Cloud fundamentals Q&A so you don’t have to re-think during the next quiz?