Generative AI Leader Practice Test 29 — 15 Questions

Question 1

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 The model sometimes returns outdated information.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 with a refreshed knowledge base is the correct technique because it directly connects the generative AI model to an external, up-to-date data source at inference time. This ensures responses are based on current information without retraining the model, addressing the problem of outdated outputs by retrieving fresh data from a vector database or API in real time.

Answer

Fine-tune the model on historical data.

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Extend the context window to include more tokens.

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Increase the model's temperature to encourage novelty.

Question 2

A data analyst wants to build a regression model in BigQuery to predict sales from historical data without writing any Python code. Which BigQuery ML statement should they use to define the model?

Accepted Answer

CREATE MODEL my_model OPTIONS(model_type='linear_reg') AS SELECT .... BigQuery ML uses the standard SQL DDL statement CREATE MODEL with an OPTIONS clause specifying model_type (e.g., 'linear_reg') and a training query supplied via AS SELECT. This lets analysts train and deploy ML models entirely in SQL without exporting data or writing Python. The linear_reg model type trains a linear regression on the selected features and label column.

Answer

INSERT INTO model my_model VALUES ...

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CREATE ML my_model AS (SELECT ...)

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SELECT ML.TRAIN('linear_reg', ...)

Question 3

A model response deployed on Vertex AI includes safety attributes with a toxicity score of 0.9 and an insult score of 0.3. The application must reject any prediction where the toxicity score exceeds 0.8. Based on the response, what action should the application take?

Accepted Answer

Reject the prediction because the toxicity score exceeds 0.8.. The response from the model includes a safety attribute with a toxicity score of 0.9, which exceeds the application's threshold of 0.8. Vertex AI safety attributes provide scores for categories like toxicity, insult, and sexual content, and the application must enforce its own rejection logic based on these scores. Since the toxicity score is above the defined threshold, the application should reject the prediction to comply with safety policies.

Answer

Retry the request with a lower temperature.

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Display the prediction because the insult score is below 0.8.

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Log the prediction but still display it.

Question 4

A financial services company needs to deploy an AI model that handles highly sensitive transaction data. They require that the model's predictions cannot be inspected by any third party, and the data must remain encrypted at all times, including during inference. Which Google Cloud feature should they use?

Accepted Answer

Confidential VMs. Confidential VMs (D) are the correct choice because they provide hardware-based memory encryption using AMD Secure Encrypted Virtualization (SEV), ensuring that data remains encrypted while in use (during inference). This meets the requirement that the model's predictions cannot be inspected by any third party, including Google Cloud operators, and that data stays encrypted at all times.

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Customer-Managed Encryption Keys (CMEK)

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Access Transparency logs

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VPC Service Controls

Question 5

A global e-commerce company wants to translate product descriptions into 50 languages with high accuracy. They need to handle domain-specific terms (e.g., 'size chart', 'return policy'). Which approach should they use?

Accepted Answer

Use Vertex AI Translation with custom glossaries. Vertex AI Translation with custom glossaries is specifically designed for high-accuracy, domain-specific translations. Custom glossaries allow you to define precise translations for terms like 'size chart' and 'return policy', ensuring consistency across 50 languages. This approach leverages Google's neural machine translation models while overriding generic translations with your business-specific terminology.

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Use the Gemini API with a prompt like 'Translate to French'

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Build a custom agent with Vertex AI Agent Builder

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Use Imagen to generate translated images

Question 6

A data scientist observes that a text generation model consistently produces outputs that stereotype certain genders. According to Google's AI Principles, what is the BEST first step?

Accepted Answer

Evaluate the model's bias using a diverse test set across genders. Google's AI Principles emphasize that the first step in addressing bias is to evaluate and measure it using appropriate tools and diverse datasets. This aligns with Principle #2: 'Avoid creating or reinforcing unfair bias,' which requires testing models across relevant demographic groups before taking corrective action. Without evaluation, any subsequent mitigation steps would lack a baseline and could be ineffective or counterproductive.

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Immediately stop using the model and delete it

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Fine-tune the model on a gender-balanced dataset

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Add a disclaimer that the model may exhibit bias

Question 7

A company wants to build a chatbot that answers questions using their internal knowledge base. Which approach is most suitable?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). RAG retrieves relevant passages from the internal knowledge base at query time and injects them into the model's context, so the chatbot answers with grounded, up-to-date, source-specific information without retraining. It is the standard pattern when the knowledge base changes frequently and citations or freshness matter. Fine-tuning, training from scratch, or zero-shot prompting do not reliably surface private, changing content.

Answer

Fine-tune a model on the knowledge base

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Train a new model from scratch

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Use zero-shot prompting with no context

Question 8

A company is choosing between Gemini Pro and Gemini Ultra for a document summarization task. Which THREE factors should they consider when deciding between the two model variants? (Choose THREE)

Accepted Answer

Model capability and accuracy. Model capability and accuracy (A) is correct because Gemini Pro is optimized for high-throughput, cost-efficient tasks with lower accuracy demands, while Gemini Ultra is designed for complex, high-accuracy reasoning. The choice directly impacts the quality of the summarization output, as Ultra uses a larger parameter count and more advanced attention mechanisms to handle nuanced context, whereas Pro may struggle with ambiguous or lengthy documents.

Answer

Availability in Google AI Studio

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Multimodal support

Question 9

A company is building a generative AI application that must adhere to strict data residency regulations. Which TWO Google Cloud features can help ensure that data does not leave a specific geographic region?

Accepted Answer

Using the regional endpoint for Vertex AI. To ensure data residency, you can use the regional endpoint for Vertex AI (A) which ensures that API calls and data processing stay within the specified region. Additionally, deploying models on dedicated VMs in a specific region (E) ensures that compute resources and data do not leave that region. Option B (Vertex AI Model Caching) does not enforce residency as caching may use regional resources but does not guarantee data stays within a region. Option C (Global load balancer with Cloud Armor) is a network security and load balancing service that operates globally. Option D (Cloud CDN) caches content at edge locations globally, which would move data outside the region.

Answer

Vertex AI Model Caching

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Global load balancer with Cloud Armor

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Cloud CDN for content delivery

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.. Constrained decoding (also called grammar-guided generation) enforces the syntax rules of the target language (e.g., Python) during token generation by restricting the model's output to only valid tokens according to a formal grammar (e.g., EBNF or context-free grammar). This directly prevents syntax errors like incorrect braces or indentation, which are structural, not semantic, issues. Techniques such as using a parser-based logit processor or a constrained beam search ensure every generated token sequence is syntactically valid.

Answer

Increase the temperature to introduce more varied token choices.

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

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

Question 11

A marketing agency uses gen AI for content generation. They need to brand consistently. What is a key business consideration?

Accepted Answer

Implement content moderation and brand guidelines. Consistent branding requires enforcing predefined guidelines on tone, style, and terminology across all generated content. Without content moderation and brand guidelines, a generative AI model may produce off-brand, inconsistent, or even harmful outputs, undermining brand identity. This is a core business strategy for deploying gen AI at scale, ensuring alignment with marketing objectives.

Answer

Use only generated content

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Use the most creative model

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Optimize for speed

Question 12

A machine learning engineer wants to convert text into numerical vectors for similarity search. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Embeddings API. The Vertex AI Embeddings API is the correct choice because it is specifically designed to convert text (and other data types) into dense numerical vectors (embeddings) that capture semantic meaning. These embeddings are the fundamental input for similarity search, enabling efficient comparison of text based on conceptual closeness rather than exact keyword matching.

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Natural Language API

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Vector Search

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Gemini API

Question 13

A data scientist is fine-tuning a large language model using Vertex AI. The training job fails with an out-of-memory error. Which action should they take to resolve this issue?

Accepted Answer

Reduce the batch size. Reducing the batch size decreases the memory footprint per training step, allowing the model to fit within the available GPU or TPU memory. Out-of-memory errors during fine-tuning on Vertex AI typically occur when the batch size is too large for the allocated accelerator memory, and lowering it directly resolves the issue without changing the model architecture or hardware.

Answer

Change the accelerator to TPU

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Use a larger model

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Increase the batch size

Question 14

A multinational corporation is deploying a generative AI chatbot for customer support across Europe, Asia, and North America. The legal team requires compliance with GDPR for EU users, data residency controls, and the ability to audit prompts and responses for safety. Which THREE actions should the company take?

Accepted Answer

Use Vertex AI with data residency controls to keep EU data within the EU.. Option B is correct because Vertex AI supports data residency and regional endpoints, allowing EU user data to be processed and stored within the EU to satisfy GDPR data residency requirements. Option D is correct because Vertex AI content safety filters help block harmful or policy-violating content, supporting the legal team's requirement to audit and enforce safety. Option E is correct because enabling prompt and response logging with user consent provides the audit trail needed for safety review while respecting GDPR consent and transparency obligations. Option A is incorrect because centralizing all user data in a US data center violates EU data residency and GDPR transfer requirements. Option C is incorrect because disabling logging for EU users would prevent the required auditing of prompts and responses and does not by itself resolve GDPR compliance.

Answer

Store all user data in a central US data center for consistency.

Answer

Disable all logging for EU users to avoid GDPR complications.

Question 15

A company wants to deploy a GenAI system for contract analysis that must produce structured output (e.g., JSON) for downstream processing. They need to ensure the output format is consistent. Which prompt engineering technique is MOST effective?

Accepted Answer

Provide a few-shot example with the desired JSON format. Providing a few-shot example with the desired JSON format explicitly demonstrates the exact structure, keys, and nesting required, which guides the model to generate consistent structured output. This technique leverages in-context learning, where the model infers the output pattern from the examples, making it more reliable than vague instructions or post-processing fixes.

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Post-process the output with a regex to fix formatting

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Use a system prompt that says 'output JSON'

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Use a larger model with better formatting capabilities