Generative AI Leader Practice Test 11 — 15 Questions

Question 1

A global e-commerce company uses generative AI to generate product descriptions in multiple languages. They want to ensure consistency across markets while respecting cultural nuances. Which THREE strategies should they adopt?

Accepted Answer

Develop region-specific prompt templates that incorporate local cultural references and legal requirements.. Options A, C, and E are correct. Region-specific prompts, local human review, and A/B testing ensure consistency and cultural sensitivity. Option B is wrong because direct translation may miss nuances. Option D is wrong because it reduces localization.

Answer

Standardize all descriptions to a neutral tone to avoid cultural issues.

Answer

Use a single global model with a translation layer to convert English descriptions.

Question 2

A company is using Vertex AI to generate marketing copy. They notice that the output sometimes contains factual inaccuracies. Which parameter adjustment is most likely to improve factual accuracy?

Accepted Answer

Decrease the temperature parameter.. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to generate creative but factually incorrect content. Lower temperature (e.g., 0.1) forces the model to choose higher-probability tokens, which aligns with more factual and consistent responses, especially in tasks like marketing copy where accuracy is critical.

Answer

Increase the max_output_tokens parameter.

Answer

Increase the top_p parameter.

Answer

Add a post-processing step to verify facts using a database.

Question 3

A company is using Vertex AI to generate email responses. They want to ensure sensitive customer data (PII) is not included in the output. What is the most effective approach?

Accepted Answer

Configure safety filters to block PII categories.. Option D is correct because safety filters in Vertex AI are specifically designed to block categories of harmful content, including PII, at the model's output layer. This provides a deterministic, automated guardrail that prevents sensitive data from being generated, unlike prompt-based instructions which can be overridden by the model's training. Safety filters operate on the model's response before it is returned, ensuring PII is caught even if the model attempts to generate it.

Answer

Use a system prompt instructing the model to avoid PII.

Answer

Fine-tune the model on a dataset that excludes PII.

Answer

Manually review each output before sending.

Question 4

A company is using Vertex AI for multimodal generative AI to analyze images and text. They need to ensure that the model's outputs are auditable and can be traced back to the input data. Which feature should they enable?

Accepted Answer

Vertex AI Model Monitoring with Explainable AI. Option D is correct because Vertex AI Model Monitoring with Explainable AI provides feature attributions that map model predictions back to specific input features (e.g., pixels in images or tokens in text). This creates an auditable trail by quantifying how each input contributed to the output, enabling traceability for compliance and debugging. The other options lack the direct input-to-output attribution required for auditability.

Answer

Vertex AI Feature Store

Answer

Vertex AI Experiments

Answer

Cloud Logging

Question 5

A data scientist wants to fine-tune a foundation model from Vertex AI Model Garden on their custom dataset. They want to choose a cost-effective method that updates only a small subset of parameters. Which fine-tuning approach should they use?

Accepted Answer

Parameter-Efficient Fine-Tuning (PEFT) like LoRA. Option C is correct because Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are specifically designed to update only a small subset of parameters (e.g., low-rank matrices injected into transformer layers) while keeping the majority of the foundation model frozen. This drastically reduces memory and compute costs compared to full fine-tuning, making it the most cost-effective choice for customizing a model from Vertex AI Model Garden on a custom dataset.

Answer

Full fine-tuning

Answer

Prompt tuning

Answer

Reinforcement Learning from Human Feedback (RLHF)

Question 6

What is the most likely cause of the error?

Accepted Answer

The predict schema must be stored in the same bucket as the model artifacts and referenced without the full gs:// URI. The error occurs because the Vertex AI Predict schema must be stored in the same Cloud Storage bucket as the model artifacts, and when referenced in the model upload request, it should use a relative path (without the full `gs://` URI). Using the full URI causes a parsing failure, as Vertex AI expects the schema to be co-located with the model artifacts for validation and deployment.

Answer

The display name contains a hyphen which is not allowed

Answer

The container image URI is incorrect

Answer

The region us-central1 does not support TensorFlow models

Question 7

A startup wants to generate product descriptions from a few keywords using a large language model. They have no prior ML experience and need the fastest time-to-market. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides a no-code/low-code environment with pre-trained foundation models and prompt templates, enabling rapid generation of product descriptions from keywords without any ML expertise. It offers the fastest time-to-market because it eliminates the need for custom model training, infrastructure setup, or coding, directly leveraging Google's generative AI capabilities through a simple interface.

Answer

Vertex AI Workbench with custom training

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 8

A retail company wants to build a customer service chatbot that can handle returns, order status, and FAQs. They need to integrate with their existing backend systems. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Agent Builder. Vertex AI Agent Builder is the correct choice because it provides a low-code platform specifically designed for building conversational AI agents (chatbots) that can be integrated with enterprise backend systems via APIs, connectors, and custom tools. It supports grounding in enterprise data, multi-turn dialogue management, and seamless integration with existing systems for handling returns, order status, and FAQs, making it the most suitable service for this use case.

Answer

Vertex AI Model Garden

Answer

Vertex AI Search

Answer

Vertex AI Codey API

Question 9

A media company uses Vertex AI to generate video captions. The generated captions sometimes contain factual errors about named entities (e.g., actor names). Which technique would most likely reduce these errors?

Accepted Answer

Use Vertex AI grounding with a knowledge base of verified entities. Option C is correct because Vertex AI grounding connects the model to a knowledge base of verified entities, allowing it to retrieve authoritative facts during generation. This reduces hallucinations about named entities by constraining outputs to validated data rather than relying solely on the model's parametric knowledge.

Answer

Enable response caching

Answer

Increase the temperature parameter

Answer

Decrease top_p to 0.3

Question 10

A company is using Vertex AI Gemini API to analyze customer feedback. They notice that the model occasionally generates offensive content. They have already set safety settings to block high-probability harmful content. What additional step should they take to further reduce offensive outputs?

Accepted Answer

Adjust safety settings to block medium-probability harmful content. Option B is correct because the company has already blocked high-probability harmful content, but offensive outputs can still occur at lower probability thresholds. By adjusting safety settings to block medium-probability harmful content, they tighten the filter to catch more borderline cases without requiring model retraining or sacrificing output diversity. This leverages Vertex AI's configurable safety filters, which operate on likelihood categories (e.g., high, medium, low) rather than just binary blocking.

Answer

Set the temperature to 0.0

Answer

Enable context caching

Answer

Fine-tune the model on customer feedback data

Question 11

An organization is using Vertex AI Agent Builder to create a customer service agent. They want the agent to be able to hand off to a human agent when it cannot answer a question. What should they configure in the agent's design?

Accepted Answer

Add an 'Escalation' intent that triggers a human handoff. Agent Builder supports 'Escalation' intent to hand off to human agents. Option B is wrong because fallback intent is for unrecognized input but not necessarily human handoff. Option C is wrong because confirmation is for confirming actions. Option D is wrong because slot filling is for collecting parameters.

Answer

Configure 'Slot filling' to collect more info

Answer

Implement a 'Confirmation' prompt for the user

Answer

Use a 'Fallback' intent to route to a human

Question 12

A company is building a generative AI chatbot for customer support using Vertex AI. They want to ground the model responses with their internal knowledge base stored in Cloud Storage and BigQuery. Which feature should they use to ensure the model only answers from the provided data and avoids hallucination?

Accepted Answer

Vertex AI Grounding with Vertex AI Search. Vertex AI Grounding with Vertex AI Search is the correct feature because it allows the model to retrieve and cite information from a specified data source (such as Cloud Storage and BigQuery) to generate responses. This process, known as grounding, ensures the model's output is based solely on the provided authoritative data, effectively reducing hallucinations by constraining the model to factual, retrieved content rather than relying on its internal parametric knowledge.

Answer

Vertex AI Prediction

Answer

Vertex AI Pipelines

Answer

Cloud Functions

Question 13

A team notices their text generation model repeats phrases excessively. Which technique would most directly reduce repetition?

Accepted Answer

Apply a repetition penalty of 1.2. Using a repetition penalty during decoding discourages the model from repeating tokens. Option A is wrong because it increases randomness, which might reduce repetition but could also reduce coherence. Option B is wrong because beam search can increase repetition. Option D is wrong because temperature reduction makes output more deterministic, potentially increasing repetition.

Answer

Use beam search with a beam width of 5

Answer

Increase top_k to 100

Answer

Lower temperature to 0.5

Question 14

Which TWO techniques can help improve the factual accuracy of a language model's outputs? (Choose two.)

Accepted Answer

Fine-tune on a domain-specific curated dataset.. Fine-tuning on a domain-specific curated dataset (C) directly adjusts the model's weights using high-quality, verified examples, teaching it to produce factually correct outputs for that domain. This reduces hallucinations by grounding the model in accurate, relevant data rather than relying solely on its pre-training distribution.

Answer

Decrease the max output tokens.

Answer

Increase the temperature parameter.

Answer

Use top-k random sampling.

Question 15

Refer to the exhibit. The team changed the generation parameters to reduce output variability. However, summaries now often repeat the same phrases. Which parameter change is most likely causing the repetition?

Accepted Answer

Reducing temperature from 0.7 to 0.2. Lowering temperature to 0.2 makes the model more deterministic, increasing repetition. Option A is wrong because top_k reduction also contributes to determinism. Option B is wrong because top_p reduction also narrows token selection. Option D is wrong because the model is the same.

Answer

Reducing top_p from 0.95 to 0.85

Answer

Using the same model text-bison@002

Answer

Reducing top_k from 40 to 10