Generative AI Leader Practice Test 13 — 15 Questions

Question 1

A compliance officer requires that all AI-generated content in Google Workspace be reviewed before sharing externally. Which change management approach BEST supports this requirement while maintaining user adoption?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Roll out the feature to a pilot group with training and a feedback loop before company-wide deployment. A phased pilot with training and a feedback loop lets the compliance team validate that AI-generated content is reviewed before external sharing while giving users time to adapt, which preserves adoption. This change management approach balances governance with user enablement, reducing resistance and surfacing issues before broad rollout.

Answer

Allow all sharing but audit logs after the fact

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Disable AI features in Workspace for all users until a review tool is built

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Immediately block all external sharing of AI-generated content

Question 2

A research team is using a large language model to analyze medical research papers and generate summaries. They need to minimize hallucinations while retaining key details. They have access to a curated database of paper abstracts. Which approach is best?

Accepted Answer

Implement RAG to retrieve relevant abstracts and incorporate them into the prompt.. Retrieval-Augmented Generation (RAG) directly addresses hallucination by grounding the model's output in a curated database of paper abstracts. By retrieving relevant abstracts and injecting them into the prompt, the model generates summaries based on verified facts rather than relying solely on its parametric knowledge, which is the most effective way to minimize hallucinations while retaining key details.

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Fine-tune the model on the entire database of papers.

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Use chain-of-thought prompting to reason step-by-step.

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Use few-shot prompting with examples of accurate summaries and set temperature=0.0.

Question 3

A developer is using Vertex AI Studio to prototype a chat application. They want to provide the model with a system instruction to set the tone and style. How should they configure this in the Vertex AI Studio interface?

Accepted Answer

Use the 'System Instruction' field in the model configuration. Vertex AI Studio provides a dedicated 'System Instruction' field in the model configuration panel, which allows developers to set the tone, style, and behavioral guidelines for the model without mixing them into the user prompt. This field is specifically designed to hold system-level instructions that are prepended to the conversation context, ensuring consistent behavior across multiple turns.

Answer

Add the instruction as part of the prompt text

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Set the temperature parameter to a high value

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Add the instruction in the 'Context' parameter

Question 4

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 most common cause of irrelevant answers when a long document is provided is that the document exceeds the model's fixed context window (e.g., 8K tokens for PaLM 2, 128K for Gemini 1.0 Pro, or up to 1M for Gemini 1.5 Pro). When the input is truncated, critical details needed for accurate retrieval and generation are lost, leading to off-target responses. This is a fundamental limitation of transformer architectures, which cannot attend to tokens beyond their maximum sequence length.

Answer

Safety filters are blocking the relevant response.

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The model's temperature is too low, making it deterministic.

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The model is not generating any tokens.

Question 5

A large e-commerce company deploys a generative AI chatbot on Vertex AI for customer service. The chatbot is powered by a fine-tuned model on the company's historical support tickets. Despite high accuracy on training topics, the chatbot frequently gives irrelevant or off-topic answers when customers ask about new products or promotions. The company maintains a comprehensive product catalog and a knowledge base of current promotions. The chatbot's prompts include a system instruction to 'Answer based on your knowledge' and no other retrieval mechanism. The response time requirement is under 3 seconds. Which course of action should the team take?

Accepted Answer

Implement a RAG pipeline that retrieves relevant product and promotion data from the knowledge base and injects it into the prompt.. Implementing a RAG (Retrieval-Augmented Generation) pipeline directly addresses the chatbot's inability to answer questions about new products or promotions. By retrieving relevant, up-to-date information from the company's product catalog and knowledge base and injecting it into the prompt, the model gains access to current data beyond its training cutoff. This approach keeps response times under 3 seconds (as retrieval is fast) and avoids the need for costly retraining, while the system instruction 'Answer based on your knowledge' is replaced with grounded context.

Answer

Increase the temperature to encourage the model to generate more diverse answers.

Answer

Add additional safety filters to block irrelevant responses.

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Fine-tune the model again on a larger dataset that includes recent support tickets.

Question 6

Which of the following is a best practice when using Vertex AI for prompt engineering?

Accepted Answer

Use consistent formatting and delimiters. Consistent formatting and delimiters (e.g., using triple backticks, XML tags, or clear section headers) help the model parse instructions and context reliably, reducing ambiguity and improving output quality. This is a core best practice in prompt engineering on Vertex AI because it leverages the model's attention mechanisms to focus on distinct prompt segments, leading to more predictable and accurate responses.

Answer

Always set temperature to 0

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Avoid using examples in the prompt

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Use very long prompts to include all possible instructions

Question 7

A company is building a GenAI chatbot that needs to answer questions using real-time data from their CRM and inventory systems. They want to ensure the model can access external data on demand. Which approach should they use?

Accepted Answer

Use Vertex AI Extensions to connect to CRM and inventory APIs. Vertex AI Extensions allow the GenAI chatbot to connect to external APIs (like CRM and inventory systems) in real time, enabling on-demand data retrieval without retraining the model. This approach uses a retrieval-augmented generation (RAG) pattern where the model queries live data sources via API calls, ensuring responses are based on current information rather than static snapshots.

Answer

Fine-tune the model on historical CRM and inventory data

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Prompt the model to guess the data based on general knowledge

Answer

Export CRM data to BigQuery and use that static snapshot

Question 8

According to Google's AI Principles, which of the following is a key commitment regarding privacy?

Accepted Answer

AI systems should incorporate privacy design principles, including notice and consent. Google's AI Principles explicitly commit to incorporating privacy design principles, such as notice and consent, into AI systems. This means that AI systems should be designed with privacy safeguards from the outset, ensuring users are informed about data collection and have control over their data, aligning with frameworks like GDPR and privacy-by-design.

Answer

AI systems should share all data with third parties for transparency

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AI systems should collect as much data as possible to improve accuracy

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AI systems should store data indefinitely for future analysis

Question 9

A data scientist wants to train a model using BigQuery ML. Which two statements are true about BigQuery ML? (Choose two.)

Accepted Answer

Models can be trained using SQL directly on BigQuery data. Option D is correct because BigQuery ML's core design lets you create and train models directly on data stored in BigQuery using standard SQL statements such as CREATE MODEL, without moving or exporting the data. Option E is correct because BigQuery ML supports supervised learning (for example, linear regression, logistic regression, and boosted trees) as well as unsupervised learning (for example, k-means clustering and matrix factorization). Option A is wrong because BigQuery ML performs training inside BigQuery itself and does not require a separate Vertex AI training cluster. Option B is wrong because BigQuery ML supports many model types beyond linear regression, including logistic regression, k-means, and deep neural networks. Option C is wrong because training happens directly on BigQuery tables, so exporting data to Cloud Storage first is unnecessary.

Answer

It requires a separate Vertex AI training cluster

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It supports only linear regression models

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Data must be exported to Cloud Storage before training

Question 10

A large enterprise has deployed generative AI assistants in three separate departments (HR, Marketing, and Customer Support) using different tools and models. Over the past quarter, the company has observed escalating cloud costs, inconsistent user experiences, and reports of data leakage in Customer Support logs. The CTO wants to address these issues while maintaining innovation velocity. As the Generative AI Leader, what course of action should you recommend?

Accepted Answer

Implement a centralized AI governance platform with cost monitoring, model registry, and security guardrails.. A centralized AI governance platform directly addresses the CTO's concerns by providing cost monitoring to control escalating cloud costs, a model registry to ensure consistent user experiences across departments, and security guardrails to prevent data leakage. This approach maintains innovation velocity by allowing departments to continue using different tools and models while enforcing enterprise-wide policies, rather than restricting them to a single platform or eliminating valuable services.

Answer

Standardize on a single model and tool across all departments, restricting usage to one platform.

Answer

Discontinue the Customer Support assistant to eliminate data leakage risk and reduce costs.

Answer

Allow each department to continue independently but require monthly cost and compliance reports.

Question 11

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.. Configuring safety filters on the model endpoint in Vertex AI directly blocks offensive content at inference time by applying predefined or custom safety thresholds (e.g., toxicity, harassment categories). This is the most reliable technique for real-time moderation, as it prevents harmful outputs regardless of prompt engineering or tokenization changes.

Answer

Use a different tokenizer to avoid offensive words.

Answer

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

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Reduce the temperature to 0.

Question 12

A data scientist fine-tunes a generative image captioning model to describe medical images. The model outputs safe but very generic captions (e.g., 'An image of cells'). The goal is to produce more specific, clinically relevant descriptions. Which approach is most effective?

Accepted Answer

Perform incremental fine-tuning on a curated dataset of detailed medical image captions.. Incremental fine-tuning on a high-quality dataset of specific medical captions directly teaches the model the desired level of detail. Option B is wrong because diverse beam search generates varied outputs but does not inherently improve specificity; it may produce multiple candidates but does not ensure clinical relevance. Option C is wrong because top-k sampling can reduce the vocabulary but does not guarantee medical accuracy or detail. Option D is wrong because increasing temperature adds randomness, which may produce longer or more varied captions but often introduces irrelevant words rather than specific clinical terms.

Answer

Use diverse beam search during decoding to generate multiple caption candidates.

Answer

Adjust top-k sampling to restrict the vocabulary to medical terms only.

Answer

Increase the temperature to encourage the model to output longer, more varied captions.

Question 13

A data scientist wants to improve the performance of a text classification model for customer feedback. They have a small labeled dataset of 500 examples and a large unlabeled corpus of 100,000 feedback messages. Which TWO strategies would be most effective? (Choose 2)

Accepted Answer

Apply semi-supervised learning by pseudo-labeling the unlabeled data. Option B is correct because semi-supervised learning via pseudo-labeling leverages the large unlabeled corpus: the model is first trained on the 500 labeled examples, then used to predict labels on the 100,000 unlabeled messages, and high-confidence predictions are added back as training data, effectively expanding the labeled set and improving classification performance. Option E is correct because using a pre-trained LLM such as Gemini and fine-tuning it on the small labeled dataset exploits transfer learning: the model already encodes broad language understanding from large-scale pre-training, so only a small amount of task-specific labeled data is needed to adapt it to customer feedback classification, which is far more sample-efficient than training from scratch. Option A is not appropriate because increasing the context window affects how much input text the model can attend to, not the model's ability to learn from limited labels, and does not address the small-labeled-data problem. Option C is not appropriate because RAG retrieves relevant documents to augment generation at inference time; it does not train or improve the classification model's parameters and is not a training strategy for a text classifier. Option D is not appropriate because training a model from scratch on only 500 labeled examples will almost certainly overfit and underperform compared to transfer learning or semi-supervised approaches.

Answer

Increase the context window of the model

Answer

Use RAG to retrieve similar examples from the unlabeled corpus during inference

Answer

Train a model from scratch on the labeled data only

Question 14

Which Google Cloud service allows you to run machine learning models directly using SQL queries on data in BigQuery?

Accepted Answer

BigQuery ML. BigQuery ML enables users to create, train, and deploy ML models using standard SQL, eliminating the need to move data to a separate environment.

Answer

Cloud Functions

Answer

Vertex AI

Answer

Dataflow

Question 15

A data analyst wants to experiment with ML models using a free, cloud-based Jupyter notebook environment with GPU support. Which Google tool should they use?

Accepted Answer

Google Colab. Google Colab is a free, cloud-based Jupyter notebook environment that provides access to GPUs (e.g., NVIDIA T4 or V100) without requiring any setup or billing. It is specifically designed for experimentation and learning with ML models, making it the correct choice for a data analyst seeking a free, GPU-enabled notebook environment.

Answer

Cloud Shell

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BigQuery Studio

Answer

Vertex AI Workbench