Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 5 — 15 Questions

Question 1

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 2

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 3

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 4

Refer to the exhibit. A team's IAM policy for Vertex AI includes the following binding. They can deploy models but cannot create tuning jobs. Which statement is true?

Accepted Answer

The aiplatform.user role lacks permission to create tuning jobs. The roles/aiplatform.user role does not include permission to create tuning jobs (aiplatform.tuningJobs.create). The modelUser role does not override the user role, admin role is not needed, and specialist role doesn't exist.

Answer

The developer needs the aiplatform.admin role

Answer

The aiplatform.user role overrides the modelUser role

Answer

The policy is missing the aiplatform.specialist role

Question 5

Refer to the exhibit. A data scientist sends a prediction request to a text generation model with the following parameters and receives repetitive output. Which parameter should be changed?

Accepted Answer

Increase temperature to 0.5. Temperature 0.0 makes the model deterministic, leading to repetitive text. Increasing temperature to 0.5 introduces randomness. Decreasing topP may help but temperature is the direct cause. Increasing topK adds diversity but less effect, decreasing max tokens doesn't fix repetition.

Answer

Decrease topP to 0.5

Answer

Increase topK to 100

Answer

Decrease maxOutputTokens

Question 6

A company uses a generative AI model to answer customer queries. The model sometimes returns outdated information. Which technique should they apply to ensure responses rely on current data?

Accepted Answer

Use grounding with a refreshed knowledge base.. Grounding the model with up-to-date source documents ensures responses are based on current information, reducing outdated outputs. Option A is wrong because prompt engineering alone cannot guarantee recency. Option B is wrong because fine-tuning with old data may perpetuate outdated patterns. Option D is wrong because temperature adjustment does not affect factual recency.

Answer

Fine-tune the model on historical data.

Answer

Extend the context window to include more tokens.

Answer

Increase the model's temperature to encourage novelty.

Question 7

A developer deployed a large language model on Vertex AI for real-time chat. Users report slow response times. The model generates sentences one word at a time. Which optimization should be applied to reduce latency?

Accepted Answer

Use streaming responses to start output earlier.. Enabling streaming allows the model to output tokens progressively, reducing perceived latency. Option A is wrong because prompt caching doesn't speed up generation. Option C is wrong because batching increases latency for real-time. Option D is wrong because more accelerators may actually increase overhead without optimization.

Answer

Batch multiple user queries together.

Answer

Deploy the model with more accelerators.

Answer

Enable prompt caching to reuse previous queries.

Question 8

A healthcare organization needs a generative AI model to answer medical questions using proprietary clinical guidelines. They have a large dataset of doctor-patient interactions. Should they fine-tune a pre-trained model or use Retrieval-Augmented Generation (RAG)?

Accepted Answer

Use RAG to retrieve relevant guidelines during inference, avoiding frequent retraining.. RAG is preferred because it can incorporate the latest guidelines without retraining, crucial for regulatory changes. Fine-tuning may cause overfitting to outdated interactions. Option B is wrong because fine-tuning requires continuous retraining. Option C is wrong because prompt engineering alone cannot inject proprietary knowledge. Option D is wrong because RAG does not inherently reduce cost.

Answer

Use RAG to reduce inference costs by skipping model updates.

Answer

Use prompt engineering to encode all guidelines into the system prompt.

Answer

Fine-tune the model on the clinical guidelines and interactions.

Question 9

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 10

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 11

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 12

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 13

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 14

A data scientist fine-tunes a model on a small proprietary dataset. After fine-tuning, the model repeats training examples verbatim. What is the most effective mitigation?

Accepted Answer

Add regularization like dropout and use a smaller learning rate.. Increasing the learning rate slightly or using dropout can reduce memorization. Option A is wrong because more epochs increase memorization. Option B is wrong because ground truth stopping doesn't prevent memorization. Option D is wrong because temperature during inference doesn't fix overfitting.

Answer

Reduce the temperature during inference to 0.

Answer

Train for more epochs to improve generalization.

Answer

Use early stopping based on validation loss.

Question 15

Which TWO techniques are most effective for improving factual accuracy in a generative AI model's responses? (Choose two.)

Accepted Answer

Retrieval-Augmented Generation (RAG) with curated datasets.. Grounding and RAG both provide external authoritative sources to enhance factual accuracy. Fine-tuning on general data doesn't guarantee accuracy, and increasing temperature hurts accuracy. Prompt engineering is helpful but not as robust as retrieval-based methods.

Answer

Increasing the model's temperature to 1.5.

Answer

Using longer system prompts with multiple instructions.

Answer

Fine-tuning on a large corpus of general text.