Generative AI Leader Practice Test 23 — 15 Questions

Question 1

A team configures a Vertex AI prediction request as shown. Users report that the model sometimes produces incoherent or off-topic responses despite moderate settings. What is the most likely cause?

Accepted Answer

The temperature is too high for coherent responses.. The combination of high temperature (0.9) and high topK (40) increases randomness and diversity, leading to incoherent outputs. Lowering temperature and topK can improve coherence. Safety settings are unrelated, and maxOutputTokens is a limit, not a cause of incoherence.

Answer

The maxOutputTokens is too low.

Answer

The safety threshold blocks too much content.

Answer

The topK value is too low.

Question 2

After deploying a text-to-image model, the output images often contain distorted objects. The team suspects the prompt is too complex. Which prompt engineering technique should they try first?

Accepted Answer

Break the prompt into simpler, separate steps.. Option A is correct because breaking the prompt into simpler, separate steps reduces complexity and helps the model generate each part correctly. Option B (adding more adjectives) increases complexity. Option C (increasing guidance scale) can amplify distortions. Option D (using a negative prompt) helps but is not the first step for simplifying the prompt.

Answer

Increase the guidance scale.

Answer

Add more descriptive adjectives.

Answer

Use a negative prompt to exclude distortions.

Question 3

A data scientist fine-tunes a generative image captioning model to describe medical images. The model outputs safe but very generic captions (e.g., 'An image of cells'). The goal is to produce more specific, clinically relevant descriptions. Which approach is most effective?

Accepted Answer

Perform incremental fine-tuning on a curated dataset of detailed medical image captions.. Option A is correct because incremental fine-tuning on a high-quality dataset of specific medical captions directly teaches the model the desired level of detail. Option B is wrong because increasing temperature may add irrelevant words, not specific clinical terms. Option C is wrong because top-k sampling can reduce output space but does not guarantee medical accuracy. Option D is wrong because beam search is for diverse output but does not address specificity.

Answer

Use diverse beam search during decoding to generate multiple caption candidates.

Answer

Adjust top-k sampling to restrict the vocabulary to medical terms only.

Answer

Increase the temperature to encourage the model to output longer, more varied captions.

Question 4

A travel company fine-tuned a language model on customer chat logs to provide travel recommendations. After deployment, they receive complaints that the model sometimes generates inappropriate or offensive content. What is the most effective approach to improve output safety while preserving overall performance?

Accepted Answer

Add a post-processing safety classifier that filters or rewrites unsafe outputs. Option B is correct because applying a safety classifier as an output filter catches harmful content without retraining. Option A is wrong because retraining on more data may not address specific safety issues. Option C is wrong because lowering temperature reduces creativity but not necessarily offensive content. Option D is wrong because system instruction alone is often insufficient for robust safety.

Answer

Modify the system instruction to request polite responses only

Answer

Retrain the model on a larger dataset of chat logs

Answer

Reduce the temperature to 0.0

Question 5

A team is fine-tuning a model for a legal document summarization task. They need to ensure high accuracy and avoid hallucinations. Which TWO approaches should they combine? (Choose two.)

Accepted Answer

Use Retrieval-Augmented Generation to retrieve relevant legal texts. Correct: A and D. A (RAG) provides source material to ground summaries. D (human-in-the-loop validation) catches errors before final output. B (increase temperature) is counterproductive. C (early stopping) addresses overfitting but not factuality. E (character-level tokenization) is not relevant.

Answer

Increase temperature to 1.5 during inference

Answer

Implement early stopping during fine-tuning

Answer

Use character-level tokenization to improve spelling

Question 6

A healthcare chatbot must avoid hallucinations. Which TWO techniques should the team implement? (Choose two.)

Accepted Answer

Use chain-of-thought prompting. Grounding with a knowledge base ensures responses are based on retrieved facts, and chain-of-thought prompting improves reasoning steps. Higher temperature increases randomness, top_k increases diversity, and frequency penalty affects repetition, not hallucinations.

Answer

Set frequency penalty to 0.0

Answer

Use higher temperature

Answer

Increase top_k to 50

Question 7

A company uses a generative AI model to generate product descriptions. They notice variations in style and length across products. How can they enforce consistent formatting?

Accepted Answer

Set a system instruction specifying style and structure.. System instructions set tone and format rules for the model. Option B is wrong because temperature range increases randomness. Option C is wrong because random example selection reduces consistency. Option D is wrong because top-k sampling increases variability.

Answer

Adjust top-k sampling to include more token candidates.

Answer

Randomly select few-shot examples from a pool of descriptions.

Answer

Use a high temperature and vary the prompt slightly.

Question 8

A developer uses a generative AI model with the system instruction shown. The response is correct but very brief. Which parameter adjustment could encourage more detail without losing accuracy?

Accepted Answer

Add 'Provide a detailed response' to the system instruction.. Adding a length constraint in a system instruction (e.g., 'Provide detailed responses') is effective. Lower temperature may reduce creativity. Higher temperature could introduce errors. Changing topK doesn't directly control length.

Answer

Set temperature to 0 to make output deterministic.

Answer

Set topK to 1 to focus on most likely tokens.

Answer

Increase temperature to 1.5 to encourage creativity.

Question 9

A team is using a language model for customer feedback analysis. They want to improve the accuracy of sentiment extraction. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Provide few-shot examples of correctly labeled sentiment in the prompt.. Options A and C are correct. Few-shot prompting provides examples of correct sentiment labeling, and fine-tuning on a labeled dataset adapts the model to the domain. Option B (increasing temperature) adds randomness and reduces accuracy. Option D (using a different API) is not a technique for improving the current model. Option E (JSON formatting) helps structure output but does not improve sentiment accuracy.

Answer

Increase the temperature to 0.8 to allow more creative interpretations.

Answer

Use the model's built-in sentiment analysis API instead of prompting.

Answer

Add a system instruction that asks the model to strictly follow JSON output format.

Question 10

A developer uses a code generation model to write Python functions. The output frequently contains syntax errors due to incorrect braces and indentation. Which technique should be used to produce syntactically valid code?

Accepted Answer

Apply constrained decoding techniques that enforce a grammar for the target programming language.. Option A is correct because constrained decoding (e.g., with guidance or grammar) forces the output to match a formal grammar, preventing syntax errors. Option B is wrong because few-shot helps with format but does not enforce grammar. Option C is wrong because temperature changes do not fix syntax. Option D is wrong because fine-tuning is heavy; constrained decoding is a lighter real-time fix.

Answer

Increase the temperature to introduce more varied token choices.

Answer

Fine-tune the model on a large corpus of syntactically correct Python code.

Answer

Provide a few-shot example of correct Python function in the prompt.

Question 11

A developer is using the Gemini API to generate code snippets. They notice the outputs often contain deprecated API calls. Which parameter adjustment or prompt strategy would most effectively encourage the model to use current APIs?

Accepted Answer

Add a system instruction specifying 'Use the most recent API version and avoid deprecated functions.'. Option A is correct because including context in the system instruction (e.g., 'Use only the latest stable version of the API') directly guides the model. Option B is wrong because temperature affects randomness, not temporal awareness. Option C is wrong because few-shot examples can help if they show current APIs, but the instruction is more direct. Option D is wrong because top-p is about nucleus sampling, not freshness.

Answer

Set top-p to 0.5 to reduce output diversity

Answer

Provide one few-shot example of a correct API call

Answer

Set temperature to 1.5 to increase creativity

Question 12

A company wants to build a customer support chatbot that answers based on internal documentation. They use Vertex AI Search and want to ensure the model only uses retrieved documents. What should they do?

Accepted Answer

Enable grounding with Vertex AI Search. Grounding with Vertex AI Search restricts the model to the retrieved context, preventing it from using internal knowledge. Setting temperature to 0.0 reduces creativity but doesn't enforce retrieval. Fine-tuning is unnecessary, and increasing max tokens doesn't affect retrieval.

Answer

Fine-tune the model on the documentation

Answer

Increase max output tokens

Answer

Set temperature to 0.0

Question 13

A team notices the RAG pipeline sometimes retrieves irrelevant documents. Which THREE improvements should they consider? (Choose three.)

Accepted Answer

Add a reranking step. Using a higher quality embedding model improves semantic understanding, adding a reranking step refines results, and reducing the number of retrieved documents reduces noise. Increasing chunk size can dilute relevance, and using exact keyword matching loses semantic context.

Answer

Use exact keyword matching instead of embedding similarity

Answer

Increase chunk size of documents

Question 14

A user provides a long document as context for a question-answering task, but the model outputs irrelevant answers. What is the most likely cause?

Accepted Answer

The document exceeds the model's context window, truncating important details.. The model's context window may be exceeded, causing loss of relevant information. Option B is wrong because temperature is unrelated to context length. Option C is wrong because safety filters would block content, not cause irrelevance. Option D is wrong because tokens are always generated.

Answer

Safety filters are blocking the relevant response.

Answer

The model's temperature is too low, making it deterministic.

Answer

The model is not generating any tokens.

Question 15

A team is deploying a text generation model for legal document review. They observe that the model occasionally generates factually incorrect legal citations. Which approach best reduces this issue?

Accepted Answer

Implement retrieval-augmented generation (RAG) with a verified legal database.. Option C is correct because retrieval-augmented generation (RAG) with a verified legal database grounds the model in factual, up-to-date sources, directly addressing incorrect citations. Option A (lowering temperature) reduces randomness but does not prevent hallucination. Option B (increasing max tokens) has no effect on factual accuracy. Option D (using a larger model) may not guarantee correctness without proper grounding.

Answer

Lower the temperature to 0.0.

Answer

Use a larger base model.

Answer

Increase the max output tokens.

Generative AI Leader Practice Test 23 — 15 Questions

Exhibit

Generative AI Leader Practice Test 23 — 15 Questions

Exhibit