Updated Question Set | Generative AI E1 | Competency Id 6618 | Quiz Answers

Hello everyone, here is the updated question set for Generative AI E1 iEvolve Competency Id 6618.

Question 1: Github CoPilot is useful for

a. Generate code

b. Generate test cases

c. Generate documentation for code

d. All are correct

Answer: All are correct

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Question 2: Rachel is a data scientist working on a data analysis project in R. She needs to create functions to preprocess the data before analysis. However, she is not familiar with the language. Which tool can Rachel use to assist her in writing R code efficiently?

a. Refer to R programming books

b. Take an online course on data analysis

c. Consult with a data science expert

d. Utilize GitHub Copilot to generate R code snippets

Answer: Utilize GitHub Copilot to generate R code snippets

---

Question 3: What is the purpose of the General Data Protection Regulation (GDPR)?

a. To ensure the responsible use of AI technologies.

b. To ensure the development of AI technologies.

c. To ensure the use of AI technologies.

d. To ensure personal data protection and privacy.

Answer: To ensure personal data protection and privacy.

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Question 4: Types of LLM applications

a. LLM RAG applications

b. Fine Tuning LLMs

c. Building LLMs from scratch

d. All are correct

Answer: All are correct

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Question 5: What is the role of Generative Model in RAG pipeline?

a. Reranks retrieved passages

b. Generates final output

c. Pulls relevant passages or documents from a knowledge source or database

d. None of the above

Answer: Generates final output

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Question 6: What would be an appropriate task to perform after retrieving passages using the Retriever in a RAG model?

a. Fine-tune the retriever model on a different dataset

b. Pass the retrieved passages to the generator for response generation.

c. Tokenize the retrieved passages using the Rag Tokenizer.

d. Generate responses directly using the RagRetriever.

Answer: Pass the retrieved passages to the generator for response generation.

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Question 7: A company is developing a chatbot using generative AI to assist customers with technical support queries. They want the chatbot to provide accurate and helpful responses. Which technique is suitable for ensuring the chatbot generates relevant and informative answers?

a. Crafting prompts with diverse variations

b. Using pre-trained prompts from a similar domain

c. Incorporating user feedback during model training

d. Adjusting prompt length and complexity based on query type

Answer: Incorporating user feedback during model training

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Question 8: Which of the following prompts might be an example for "Role prompting"?

a. Provide some pointers for a speech on International Yoga Day.

b. Generate some good marketing ideas that could be used by a Marketing Executive to drive a campaign on Blood donation.

c. Explain the concept of Stock Market risks to a youngster who is around 15-18 years of age

d. All are correct

Answer: Only B

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Question 9: In developing a Gen AI-driven financial advisory tool, how can the principle of Data Diversity be practically applied?

a. By exclusively using data from a single demographic for accuracy.

b. By gathering data from various socioeconomic backgrounds and geographic regions

c. By prioritizing data from a specific age group

d. By ignoring diverse data sources to simplify the analysis

Answer: By gathering data from various socioeconomic backgrounds and geographic regions

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Question 10: Mark is developing a web application using JavaScript. He encounters a problem while implementing a complex sorting algorithm and needs assistance to optimize the code. Which tool can provide code suggestions and improvements?

a. Use GitHub Copilot to generate optimized code snippets with blocking public code

b. Use GitHub Copilot to generate optimized code snippets without blocking public code

c. Seek guidance from a mentor

d. All are correct

Answer: Use GitHub Copilot to generate optimized code snippets without blocking public code

---

Question 11: A social media platform is implementing generative AI to generate personalized recommendations for users based on their interests. To ensure the recommendations are accurate and relevant, which aspect of prompt engineering should they focus on?

a. Customizing prompts based on user profiles and preferences

b. Using generative prompts with high linguistic diversity

c. Incorporating sentiment analysis to gauge user reactions

d. Adjusting the model architecture to handle large-scale data processing

Answer: Customizing prompts based on user profiles and preferences

---

Question 12: What is the role of vector databases in AI applications?

a. To store metadata

b. To store scalar embeddings for fast retrieval and similarity search

c. To store vector embeddings for fast retrieval and similarity search

d. To store vector embeddings for fast retrieval and dissimilarity search

Answer: To store vector embeddings for fast retrieval and similarity search

---

Question 13: In a scenario where you want to generate a paragraph discussing a specific topic, which component of a RAG model would you primarily rely on?

a. Generator

b. Retriever

c. Discriminator

d. Encoder

Answer: Generator

---

Question 14: Consider the following Python code snippet:

```python

from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")

retriever = RagRetriever.from_pretrained("facebook/rag-token-base")

generator = RagTokenForGeneration.from_pretrained("facebook/rag-token-base")

query = "What is the capital of France?"

context = "France is a country located in Western Europe. Its capital is Paris."

input_dict = tokenizer.prepare_seq2seq_batch(query, return_tensors="pt")

generated = generator.generate(input_ids=input_dict['input_ids'])

print(tokenizer.decode(generated[0], skip_special_tokens=True))

```

What is the purpose of the `tokenizer.prepare_seq2seq_batch()` method in this code?

a. To prepare the input query for the sequence-to-sequence generation model by tokenizing the query and converting it into the appropriate tensor format.

b. To preprocess the retrieved passages for the RAGRetriever

Answer: To prepare the input query for the sequence-to-sequence generation model by tokenizing the query and converting it into the appropriate tensor format.

---

Question 15: What is AI governance?

a. AI governance is a range of policies, frameworks and practices that organizations and governments implement to ensure the responsible use of AI technologies.

b. AI governance is a range of policies, frameworks and practices that organizations and governments implement to ensure the rapid development of AI technologies.

c. AI governance is a range of policies, frameworks and practices that organizations and governments implement to ensure the use of AI technologies.

d. AI governance is a range of policies, frameworks and practices that organizations and governments implement to ensure the efficient use of AI technologies.

Answer: AI governance is a range of policies, frameworks and practices that organizations and governments implement to ensure the responsible use of AI technologies.

---

Question 16: What is the purpose of Re-ranking step in RAG pipeline?

a. Filters and re-orders the k retrieved passages to pick the most relevant ones for the query

b. Concatenates the top re-ranked m passages with the original query

c. Generates final output

d. None of the above

Answer: Filters and re-orders the k retrieved passages to pick the most relevant ones for the query

---

Question 17: How can finetuning the embedding model benefit retrieval performance?

a. By reducing the size of the model

b. By allowing for more meaningful embedding representations over a training distribution of data

c. By translating data into multiple languages

d. By encrypting the data for security purposes

Answer: By allowing for more meaningful embedding representations over a training distribution of data

---

Question 18: In the following Python code snippet, what is the purpose of the `skip_special_tokens=True` argument?

```python

from transformers import RagTokenizer, RagTokenForGeneration

tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")

generator = RagTokenForGeneration.from_pretrained("facebook/rag-token-base")

input_ids = tokenizer.encode("What is the capital of France?", return_tensors="pt")

generated = generator.generate(input_ids=input_ids)

print(tokenizer.decode(generated[0], skip_special_tokens=True))

```

a. Skips tokenization of special characters.

b. Skips the generation of special tokens

c. Skips the decoding of special tokens.

Answer: Skips the decoding of special tokens.

---

Question 19: Which of the following is missing in the prompt given below? Prompt: You are an assistant who can speak only in a language called 'Spices'. This is a new language which always begins as 'Chilli...'. Use this language to respond to any question asked.

a. Context is missing

b. Examples for the language is missing

c. Prompt is very much clear and concise

d. All are correct

Answer: Examples for the language is missing

---

Question 20: What is the role of Guardrails in governing the usage of AI systems?

a. Guardrails serve as protective barriers to mitigate risks and threats associated with using GenAI

b. Guardrails do not aim to prevent malicious or harmful exploits of GenAI models

c. Guardrails can enhance GenAI model weights

d. Guardrails are used to safeguard data privacy while training GenAI models

Answer: Guardrails serve as protective barriers to mitigate risks and threats associated with using GenAI.

---

Question 21: An e-commerce platform is deploying generative AI to generate product descriptions. They want the descriptions to be informative and persuasive. Which prompt engineering technique would be most effective in achieving this objective?

a. Incorporating customer reviews and feedback into prompt generation

b. Utilizing sentiment analysis to gauge the emotional impact of descriptions

c. Fine-tuning the model with product-specific prompts and attributes

d. Increasing the model's vocabulary size to improve descriptive capabilities

Answer: Fine-tuning the model with product-specific prompts and attributes

---

Question 22: You are a new joinee in TCS and you are given access to the TCS AI Experience Zone for Azure. From the given options below choose the correct option?

a. Do not share your credentials with any other individual for accessing the TCS AI Experience Zone for Azure VDI as well as for subsequent access to Enterprise Playground App and Enterprise Playground API (Hands-on lab).

b. Share your credentials with your friends and colleagues for accessing the TCS AI Experience Zone for Azure

c. Share the TCS device from which you are logged into VDI or TCS AI Experience Zone for Azure apps to another individual.

d. Engage in screen share and provide access to other individuals via screen share

Answer: Do not share your credentials with any other individual for accessing the TCS AI Experience Zone for Azure VDI as well as for subsequent access to Enterprise Playground App and Enterprise Playground API (Hands-on lab).

---

Question 23: If you want more non-deterministic response from any text generating tools, which value of the temperature parameter would you choose?

a. 0

b. 1

c. 0.5

d. None of the above

Answer: 1

---

Question 24: What is "data drift" in the context of GenAI accountability?

a. Monitoring the performance of AI models

b. Ensuring data quality and integrity

c. Checking if the model's performance is declining over time

d. All are correct

Answer: Checking if the model's performance is declining over time

---

Question 25: Can code generated by Generative AI tools owned by others lead to any legal issues for the organization?

a. Yes, it can lead to legal issues hence it is crucial to read and understand the product terms

b. No, it will not lead to any legal issues.

c. It depends upon the situation

d. None of the above

Answer: Yes, it can lead to legal issues hence it is crucial to read and understand the product terms.

---

Question 26: Sarah is working on a project in Python and needs to implement a function to calculate the Fibonacci sequence. However, she is not sure about the syntax and the most efficient algorithm to use. Which tool can Sarah leverage to assist her in this situation?

a. Use GitHub Copilot to generate optimized code snippets with blocking public code.

b. Use Google Duet to generate optimized code snippets

c. Use AWS CodeWhisperer to generate optimized code snippets.

d. All are correct

Answer: All are correct

---

Question 27: How does RAG ensure the generated responses are contextually relevant to the query and retrieved passages?

a. By using a reinforcement learning-based reward system

b. By fine-tuning the model on a specific domain dataset

c. By integrating the retrieved passages as input during generation

d. By applying post-processing techniques to filter irrelevant responses

Answer: By integrating the retrieved passages as input during generation

---

Question 28: What benefits do Guardrails offer?

a. Mitigating Bias and Fairness

b. Preventing Misuse and Harm

c. Ensuring Data Privacy and Security

d. All of the above

Answer: All of the above

---

Question 29: Which of the following is missing in the prompt given below? Prompt: You are an assistant who can speak only in a language called 'Spices'. This is a new language which always begins as 'Chilli...'. Use this language to respond to any question asked.

a. Context is missing

b. Examples for the language is missing

c. Prompt is very much clear and concise

d. All are correct

Answer: Examples for the language is missing

---

Question 30: A research team is training a generative AI model to produce text summaries of news articles. They want to ensure the generated summaries are accurate and do not propagate misinformation. What approach should they take?

a. Train the model on a diverse dataset without fact-checking the generated summaries

b. Incorporate fact-checking mechanisms to validate the accuracy of the generated summaries

c. Increase the model's complexity to minimize the chances of generating misinformation.

d. Rely on post-publication correction mechanisms to address any misinformation generated by the model.

Answer: Incorporate fact-checking mechanisms to validate the accuracy of the generated summarie