Generative AI Leader Practice Test 24 — 15 Questions

Question 1

A company is using a large language model for automated translation of legal contracts. They find that the translations sometimes alter the meaning of specific clauses. Which TWO approaches would most effectively preserve the original meaning? (Choose two.)

Accepted Answer

Fine-tune the model on a parallel corpus of legal translations.. Options B and D are correct. Using a glossary of key legal terms with their translations ensures consistent terminology, and fine-tuning on a parallel corpus of legal translations adapts the model to the domain. Option A (full context in one prompt) may exceed token limits. Option C (high temperature) increases creativity and risk of altering meaning. Option E (low top-p) restricts vocabulary but does not preserve meaning of specific clauses.

Answer

Provide the full contract context in a single prompt.

Answer

Set top-p=0.1 to limit the vocabulary to the most likely tokens.

Answer

Increase the temperature to allow more creative phrasing.

Question 2

A product team uses a translation model to convert English product descriptions into French. The model mixes formal and informal French dialects. Which simple prompt modification likely solves this?

Accepted Answer

Add a system prompt specifying 'Use only formal French with no informal expressions.'. Option D is correct because adding a requirement in the prompt to 'Use formal French' directly instructs the model on the desired style. Option A is wrong because temperature is for creativity, not dialect. Option B is wrong because few-shot with formal examples could help, but it's not a simple modification; a system prompt is simpler. Option C is wrong because fine-tuning is overkill.

Answer

Increase the temperature to encourage more consistent output.

Answer

Fine-tune the model on a corpus of formal French texts.

Answer

Provide a few-shot example of a formal French translation in the prompt.

Question 3

An e-commerce company fine-tunes a model on customer reviews to generate product feedback summaries. They want to ensure the model does not reproduce toxic language from the training data. Besides filtering the training data, which additional technique is most effective at inference time?

Accepted Answer

Pass the model output through a toxicity detection model and conditionally regenerate or block. Option A is correct because a toxicity classifier (e.g., Perspective API) applied to the output can block or flag toxic content. Option B is wrong because temperature reduction does not guarantee avoidance of toxic patterns. Option C is wrong because beam search may produce repetitive, not safer, outputs. Option D is wrong because top-k sampling reduces randomness but does not filter toxicity.

Answer

Set temperature to 0.0 to reduce variance

Answer

Set top-k to 10 to limit token choices

Answer

Use beam search with a high beam width

Question 4

A company uses Vertex AI PaLM for code generation. The code often contains security vulnerabilities. Which improvement should be applied?

Accepted Answer

Include a security-focused system instruction. Including a security-focused system instruction (e.g., 'Write secure code that avoids SQL injection') directly guides the model to produce safer output. Increasing temperature worsens security, setting top_k to 1 reduces diversity but doesn't address security, and using Codey alone without instructions may not suffice.

Answer

Set top_k to 1

Answer

Use Codey model instead

Answer

Increase temperature to 0.8

Question 5

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 6

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 7

A data scientist is using a large language model to generate product descriptions. The descriptions are often too verbose. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty.. Option A is correct because increasing the frequency penalty discourages repetition and can make output more concise. Option B (decreasing temperature) reduces randomness but not verbosity. Option C (decreasing top-k) limits word choice but not length. Option D (increasing max tokens) makes descriptions longer.

Answer

Decrease the top-k value.

Answer

Increase the max output tokens.

Answer

Decrease the temperature.

Question 8

A user reports that the model's response to the same prompt varies significantly across different calls. Which parameter change would most likely reduce variability?

Accepted Answer

Decrease temperature to 0.2.. Option A is correct because decreasing temperature reduces randomness and makes outputs more deterministic. Option B (increase maxOutputTokens) does not affect variability. Option C (increase candidateCount) returns multiple candidates but each still varies. Option D (decrease topK) reduces diversity but temperature has a stronger effect.

Answer

Decrease topK to 10.

Answer

Increase candidateCount to 3.

Answer

Increase maxOutputTokens to 2000.

Question 9

Which TWO techniques effectively reduce bias in generative model outputs? (Choose two.)

Accepted Answer

Apply adversarial debiasing during training or fine-tuning.. Options A and C are correct. A: fine-tuning on a balanced dataset reduces sampling bias in training data. C: adversarial debiasing actively adjusts the model to remove spurious correlations. B is wrong because increasing temperature adds randomness without addressing bias. D is wrong because larger models may amplify biases. E is wrong because reducing max tokens does not affect bias.

Answer

Increase the temperature parameter to introduce more variability.

Answer

Use a larger model with more parameters.

Answer

Reduce max output tokens to limit the model's expression.

Question 10

A data scientist is using Vertex AI generative AI studio to create a chatbot. The chatbot gives inconsistent answers to similar questions. Which parameter should they adjust to make responses more consistent?

Accepted Answer

Decrease temperature to 0.2. Option C is correct because lowering temperature makes the model more deterministic, reducing randomness. Option A is wrong because top-p affects diversity but not as directly as temperature. Option B is wrong because presence penalty encourages new topics, increasing variability. Option D is wrong because frequency penalty reduces repetition but doesn't enforce consistency.

Answer

Increase top-p to 0.9

Answer

Increase presence penalty to 0.5

Answer

Decrease frequency penalty to 0.0

Question 11

After fine-tuning a model on customer support data, the model starts using profanity. What is the most effective mitigation?

Accepted Answer

Enable a safety attribute filter. Enabling a safety attribute filter blocks profanity at inference time. Reducing learning rate may not help, adding profanity as negative examples could help but is less immediate, and increasing temperature increases randomness, potentially worsening the issue.

Answer

Add profanity to training data as negative examples

Answer

Reduce learning rate and retrain

Answer

Increase temperature to reduce confidence

Question 12

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 13

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.

Question 14

A chatbot built with Vertex AI PaLM API often provides outdated information about company policies because the training data is months old. Which approach should the team use?

Accepted Answer

Implement grounding by connecting to a knowledge base of current policies.. Option A is correct because implementing grounding by connecting to a knowledge base of current policies ensures the chatbot retrieves up-to-date information at runtime. Option B (fine-tuning on latest documents) is time-consuming and requires continuous updates. Option C (prompting to say 'I don't know') doesn't provide correct info. Option D (increasing context window) doesn't update knowledge.

Answer

Use prompt engineering to instruct the model to say 'I don't know' if unsure.

Answer

Increase the context window to include more history.

Answer

Fine-tune the model on the latest policy documents.

Question 15

A developer is building a chatbot for a medical application that discusses sensitive health topics. The chatbot consistently gets its outputs blocked. What should the developer do?

Accepted Answer

Review and refine the system instructions to avoid triggering safety filters, and consider using a different model endpoint that allows medical contexts.. Option C is correct because reviewing and refining system instructions to avoid triggering safety filters, and possibly using a different model endpoint that allows medical contexts, directly addresses the filter blocks. Option A (disable safety filter) violates policy. Option B (adjust thresholds to allow VIOLENCE/SEXUAL) is inappropriate for medical context and may violate guidelines. Option D (increase token limit) does not help with safety blocks.

Answer

Disable the safety filter entirely to allow all topics.

Answer

Adjust the safety category thresholds to allow VIOLENCE and SEXUAL content since it's medical.

Answer

Increase the input token limit to 2000.