Generative AI Leader Practice Test 26 — 15 Questions

Question 1

A social media company uses a generative AI model to moderate user posts. The model occasionally allows offensive content. Which safety technique should be implemented?

Accepted Answer

Configure safety filters on the model endpoint in Vertex AI.. Safety filters explicitly block harmful content categories. Option A is wrong because lower temperature may produce bland but not necessarily safe outputs. Option B is wrong because few-shot examples may not cover all offensive patterns. Option D is wrong because changing tokens does not inherently filter content.

Answer

Use a different tokenizer to avoid offensive words.

Answer

Add few-shot examples of safe posts in the prompt.

Answer

Reduce the temperature to 0.

Question 2

A team monitors their generative AI model on Vertex AI. They notice output quality declining. Which metric is most likely the root cause?

Accepted Answer

Input token count per request is increasing.. The increasing input token count suggests users are providing more context, which may exceed the model's effective context window or dilute relevant information, degrading quality. Latency and error rate are fine.

Answer

Output token count is decreasing.

Answer

Prediction latency is stable.

Answer

Error rate is less than 1%.

Question 3

A company is using a fine-tuned LLM for generating financial reports. They need to ensure that the output complies with regulatory standards and does not include speculative content. Which combination of techniques should they implement?

Accepted Answer

Fine-tune the model on historical compliant reports, use RAG with a regulatory database, and implement a human-in-the-loop review.. Option A is correct because fine-tuning on historical compliant reports, using RAG with a regulatory database, and implementing a human-in-the-loop review provides multiple layers of compliance. Option B (system instruction with low temperature and keyword filter) is insufficient for complex regulations. Option C (safety settings and low top-p) may block non-speculative content and doesn't ensure compliance. Option D (larger model) does not guarantee compliance.

Answer

Increase the model's safety settings to maximum, use a low top-p value, and limit output tokens.

Answer

Use a larger model with more parameters and rely on its inherent knowledge.

Answer

Use a system instruction to adhere to regulations, set temperature to 0.0, and apply a keyword filter.

Question 4

A team uses a generative model to summarize lengthy legal documents. The summaries are accurate but often exceed the target length of 200 words, varying widely. Which simple adjustment should be applied to ensure consistent output length?

Accepted Answer

Set the max output tokens parameter to 200.. Option B is correct because setting a maximum token limit directly controls the output length. Option A is wrong because temperature affects creativity, not length. Option C is wrong because prompt engineering can request a specific length but the model may not strictly follow it; token limit is more reliable. Option D is wrong because fine-tuning is heavy and may not be needed when a simple constraint works.

Answer

Fine-tune the model on summaries that are exactly 200 words.

Answer

Add a system prompt that says 'Summarize in exactly 200 words.'

Answer

Lower the temperature to reduce variability in word choices.

Question 5

A financial analyst uses generative AI to summarize earnings reports. The summaries vary in style. Which THREE methods can improve consistency? (Choose three.)

Accepted Answer

Set temperature to 0.2. Fine-tuning on curated data aligns the model to a desired style, few-shot prompting provides consistent examples, and low temperature reduces randomness. Increasing max tokens does not affect style, and citation mode adds references but not style consistency.

Answer

Increase max output tokens

Answer

Enable citation mode

Question 6

A streaming platform uses a large generative model for personalized content suggestions. Budget constraints require minimizing inference costs without significantly degrading quality. Which approach is most effective?

Accepted Answer

Use a distilled version of the model.. Using a smaller distilled model reduces compute cost with minimal quality loss for recommendation tasks. Option A is wrong because stronger safety filters don't reduce cost. Option B is wrong because caching is limited. Option D is wrong because more accelerators increase cost.

Answer

Deploy the model on higher-end accelerators to save time.

Answer

Implement stronger safety filters to reduce output length.

Answer

Cache frequent prompts to avoid regeneration.

Question 7

A company is using Vertex AI to generate customer support summaries from chat logs. They notice that the summaries sometimes include irrelevant details from the conversation. Which technique should they use to reduce irrelevant details?

Accepted Answer

Add a system instruction to focus on key points.. Option A is correct because adding a system instruction to focus on key points directly guides the model to omit irrelevant details. Option B (increasing temperature) would increase randomness and potentially introduce more irrelevant content. Option C (using a higher top-k value) increases diversity of word choices, not relevance. Option D (fine-tuning on general conversations) is not targeted and may not resolve the specific issue.

Answer

Use a higher top-k value.

Answer

Fine-tune the model on a large dataset of general conversations.

Answer

Increase the temperature parameter.

Question 8

A development team is integrating a large language model into a healthcare application. They need to reduce the risk of generating harmful medical advice. Which THREE measures should they implement? (Choose three.)

Accepted Answer

Use a safety filter to block outputs containing harmful medical terminology.. Options A, B, and C are correct. A safety filter blocks outputs containing harmful terminology, fine-tuning on a curated medical dataset improves domain knowledge and safety, and RAG with trusted sources grounds outputs in verified information. Option D (high temperature) increases randomness and risk. Option E (disclaimer) does not reduce the generation of harmful advice.

Answer

Include a disclaimer in the system instruction that the model is not a doctor.

Answer

Set the temperature to a very high value to ensure diverse outputs.

Question 9

A law firm uses a generative model to analyze contracts and extract key clauses. The model often outputs irrelevant clauses or misses important ones. They want to improve the relevance of the outputs without retraining the entire model. Which approach is best?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a curated legal clause database and a reranker to select the most on-topic passages.. Option C is correct because RAG with a legal corpus retrieves clause-specific paragraphs, and reranking prioritizes relevant content, improving precision. Option A is wrong because temperature adjustment does not improve relevance. Option B is wrong because increasing context length may dilute focus. Option D is wrong because fine-tuning requires significant data and resources.

Answer

Increase the input token limit to provide the entire contract in the prompt.

Answer

Decrease the temperature to make outputs more deterministic.

Answer

Fine-tune the base model on a labeled dataset of contract-clause pairs.

Question 10

Refer to the exhibit. A team runs 'gcloud ai models list --filter=displayName:qa-chat-v1' and sees the output. The model was tuned using supervised fine-tuning (SFT) but shows 'state: DEPLOYING' for days. What is the most likely issue?

Accepted Answer

The model is stuck in deployment due to insufficient quota. The model is stuck in DEPLOYING state with no errors, suggesting a resource issue like hitting the deployment quota. Silent failures would show errors, missing evaluation metrics don't block deployment, and no errors doesn't mean it's fine.

Answer

The evaluation metrics are missing, causing deployment to hang

Answer

The training pipeline failed silently

Answer

The model has no errors, so it is fine

Question 11

A legal firm uses a generative AI to draft contracts. They want the output to follow a specific clause structure. Which technique should they use in the prompt?

Accepted Answer

Include a system instruction that defines the required format.. System instructions set overarching rules for output structure. Option A is wrong because temperature doesn't enforce structure. Option C is wrong because grounding retrieves facts, not structure. Option D is wrong because adjusting stop sequences only ends generation.

Answer

Increase temperature to encourage variance.

Answer

Use grounding to pull from a database of contracts.

Answer

Set stop sequences to end generation at certain points.

Question 12

An enterprise uses a fine-tuned PaLM 2 model for code generation. They want to ensure the generated code passes security audits. Which combination of techniques would be most effective?

Accepted Answer

Integrate a static analysis tool in the pipeline and add a safety filter to reject code containing dangerous functions.. Option D is correct because integrating a static analysis tool and a safety filter provides automated, real-time security checks. Option A (few-shot with high temperature) increases risk due to randomness. Option B (fine-tuning on insecure code) is counterproductive. Option C (increasing context window) does not directly address security.

Answer

Use a few-shot prompt with examples of secure code and set temperature to 1.0.

Answer

Fine-tune the model on a dataset of insecure code and use top-p=0.9.

Answer

Increase the model's context window and use a system instruction to 'be secure'.

Question 13

A developer wants to improve the factual accuracy of the model's summaries. Based on the exhibit, what should they do?

Accepted Answer

Configure grounding with a knowledge base.. Option A is correct because GROUNDING_CONFIG is NONE, so enabling grounding with a knowledge base would allow the model to retrieve factual information. Option B (enable support engine) is a different feature. Option C (re-train) is possible but more resource-intensive. Option D (increase context window) does not directly improve factual accuracy.

Answer

Enable the support engine.

Answer

Increase the model's context window.

Answer

Re-train the model with a dataset of facts.

Question 14

A content generation model for e-commerce product descriptions repeats the same phrases across multiple descriptions (e.g., 'high-quality', 'best-in-class'). The team wants more varied and engaging output. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty parameter to 1.0.. Option B is correct because increasing the frequency penalty discourages the model from repeating tokens, directly reducing repetition. Option A is wrong because higher temperature increases randomness but may not specifically target repetition. Option C is wrong because focusing top-p only on a small set may increase repetition. Option D is wrong because decreasing max tokens truncates output but doesn't reduce repetition.

Answer

Decrease the max output tokens to 50.

Answer

Increase the temperature parameter to 1.5.

Answer

Set the top-p value to a very small number like 0.1.

Question 15

A marketing agency uses a generative AI model to create slogans for ad campaigns. The model outputs generic slogans like 'Quality you can trust' that lack originality. The agency has a library of past award-winning slogans and wants to generate more creative and brand-specific outputs. They have a requirement that the model must not produce slogans longer than 15 words. Which technique should they prioritize?

Accepted Answer

Use few-shot prompting with 3-5 examples of award-winning slogans in the prompt.. Option C is correct because providing few-shot examples of award-winning slogans in the prompt directly inspires creativity and style matching. Option A is wrong because increasing temperature may produce nonsense. Option B is wrong because fine-tuning is heavy and not needed. Option D is wrong because token limit only truncates length, doesn't improve creativity.

Answer

Set max tokens to 15 to force shorter, potentially more punchy slogans.

Answer

Increase the temperature to 1.2 to encourage more creative word combinations.

Answer

Fine-tune the model on the library of award-winning slogans.