Generative AI Leader Practice Test 44 — 15 Questions

Question 1

An enterprise is concerned about the cost of using a large LLM for a high-volume customer support chatbot. They want to reduce token consumption while maintaining response quality. Which strategy would be MOST effective?

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

Accepted Answer

Implement response caching for common questions and batch similar requests. Caching frequent queries reduces costs because the response is served from cache without model inference. Batching requests also saves on per-request overhead. Choosing a smaller model may reduce quality.

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Use a multimodal model to handle both text and images

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Always use the largest available model to ensure best quality

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Increase the max output tokens to capture more detail

Question 2

A company is piloting a GenAI code review assistant. Developers report that the assistant often suggests incorrect or insecure code snippets. The team wants to improve the assistant's reliability before expanding the pilot. Which approach should they prioritize?

Accepted Answer

Fine-tune the base model on a curated dataset of secure code review examples. Fine-tuning on a curated dataset of secure code review examples directly addresses the assistant's tendency to suggest incorrect or insecure code by adapting the model's behavior to the specific patterns and standards of secure coding. Unlike retrieval or parameter adjustments, this approach modifies the model's weights to prioritize security and correctness in its outputs, making it the most effective method for improving reliability in a targeted domain.

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Use RAG to retrieve code snippets from a database of known vulnerabilities

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Increase the model's temperature parameter to generate more diverse suggestions

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Switch to a larger foundation model without additional tuning

Question 3

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the model. By indexing the documents in a vector store and retrieving relevant chunks at inference time, RAG ensures the model's responses are grounded in the current data, accommodating monthly updates efficiently.

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Fine-tune a base LLM on the policy documents monthly

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Train a custom model from scratch on the policy documents each month

Question 4

A company is deploying a GenAI system that generates product descriptions. During A/B testing, the new system shows a 20% increase in click-through rate (CTR) but a 15% increase in average cost per query due to the model size. The team wants to optimize cost without sacrificing the CTR gain. Which THREE actions should they take? (Choose three.)

Accepted Answer

Batch similar requests to reduce per-request overhead. Batching similar requests reduces the per-request overhead by combining multiple inference calls into a single batch, which amortizes the fixed costs (e.g., model loading, token processing) across more outputs. This directly lowers the average cost per query while preserving the model architecture and CTR gains, as the model's output quality remains unchanged.

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Use a larger model with higher accuracy to further increase CTR

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Increase the number of few-shot examples in the prompt

Question 5

A team uses Vertex AI Generative AI Studio to tune a model via RLHF. After tuning, the model outputs are bland. What likely went wrong?

Accepted Answer

Reward model overfits to generic responses. When the reward model overfits to generic responses, it assigns high rewards to safe, non-committal outputs, causing the RLHF-tuned model to converge toward bland, uninformative text. This happens because the reward model learns to prefer patterns that are statistically common in the training data rather than genuinely high-quality or diverse responses, directly leading to the 'bland' output described.

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Insufficient training data

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Too many training steps

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Low temperature during evaluation

Question 6

A travel company fine-tuned a language model on customer chat logs to provide travel recommendations. After deployment, they receive complaints that the model sometimes generates inappropriate or offensive content. What is the most effective approach to improve output safety while preserving overall performance?

Accepted Answer

Add a post-processing safety classifier that filters or rewrites unsafe outputs. A post-processing safety classifier acts as a guardrail that can detect and filter or rewrite unsafe outputs without altering the underlying model's weights or training data. This approach preserves the model's overall performance on safe, relevant recommendations while adding a dedicated safety layer that can be independently tuned and updated as new safety requirements emerge. Unlike prompt engineering or hyperparameter adjustments, a classifier provides a robust, policy-enforced mechanism to catch edge cases that the model might otherwise generate.

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Modify the system instruction to request polite responses only

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Retrain the model on a larger dataset of chat logs

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Reduce the temperature to 0.0

Question 7

An enterprise uses a fine-tuned PaLM 2 model for code generation. They want to ensure the generated code passes security audits. Which combination of techniques would be most effective?

Accepted Answer

Integrate a static analysis tool in the pipeline and add a safety filter to reject code containing dangerous functions.. Integrating a static analysis tool (e.g., SonarQube, Checkmarx) into the pipeline provides automated, rule-based scanning for security vulnerabilities like SQL injection or buffer overflows, while a safety filter explicitly blocks generated code containing dangerous functions (e.g., eval(), exec()). This combination creates a defense-in-depth approach that catches both known vulnerability patterns and explicitly prohibited operations, which is essential for passing security audits.

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Use a few-shot prompt with examples of secure code and set temperature to 1.0.

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Fine-tune the model on a dataset of insecure code and use top-p=0.9.

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Increase the model's context window and use a system instruction to 'be secure'.

Question 8

Which feature in Vertex AI allows users to browse over 300 foundation models and deploy them with minimal code?

Accepted Answer

Vertex AI Model Garden. Vertex AI Model Garden, is correct because it provides a centralized repository where users can browse, discover, and deploy over 300 foundation models—including first-party, open-source, and third-party models—with minimal code. It abstracts away infrastructure complexity by offering pre-built deployment templates and one-click integration with Vertex AI endpoints, enabling rapid experimentation and production deployment without deep ML engineering overhead.

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Vertex AI Studio

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Vertex AI Agent Builder

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Vertex AI Pipelines

Question 9

A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on their internal knowledge base. Which feature should they use?

Accepted Answer

Grounding with enterprise data stores. Vertex AI Agent Builder supports grounding with enterprise data stores, which allows the agent to retrieve and answer questions based on the company's internal knowledge base (e.g., documents, PDFs, websites) without relying on public web search. This ensures responses are grounded in proprietary, controlled data, making it the correct choice for a customer support chatbot that needs to reference internal policies or product documentation.

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Grounding with Google Search

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Model tuning

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Prompt engineering

Question 10

A company is adopting generative AI for customer support. Which TWO strategies should they implement to manage risks related to brand reputation?

Accepted Answer

Establish a human-in-the-loop escalation process for sensitive interactions.. A human-in-the-loop escalation process ensures that sensitive or ambiguous customer interactions are reviewed by a human agent before an AI-generated response is sent. This directly mitigates brand reputation risk by preventing the AI from inadvertently making offensive, legally problematic, or factually incorrect statements that could go viral. The human reviewer acts as a safety net, catching edge cases that automated filters might miss, such as nuanced sarcasm or cultural insensitivity.

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Publish a disclaimer that the AI may make mistakes.

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Deploy the model without any content filters to maximize helpfulness.

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Disable customer support AI entirely to avoid any risk.

Question 11

During a load test, a Vertex AI endpoint serving a large language model experiences high latency and increased error rates. The endpoint is configured with autoscaling. What is the most likely cause?

Accepted Answer

The autoscaling metric is based on CPU utilization but the model is GPU-bound. When a model is GPU-bound, CPU utilization remains low even under heavy inference load, so autoscaling based on CPU metrics fails to trigger additional replicas. This leads to queued requests, increased latency, and eventual error rates as the existing GPU instances become saturated. Vertex AI endpoints default to CPU-based autoscaling unless explicitly configured with GPU metrics like 'gpu_utilization' or custom metrics.

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There is a network bottleneck

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The model size is too large for the machine type

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The endpoint is using a global load balancer

Question 12

Which Google AI research organization is responsible for AlphaFold, a breakthrough in protein structure prediction?

Accepted Answer

Google DeepMind. Google DeepMind is the research lab behind AlphaFold, AlphaCode, and other notable achievements.

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Google Research

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Google Brain

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X Development

Question 13

A financial analyst wants to run a regression model inside BigQuery using SQL, without moving data to a separate ML environment. Which Google Cloud service allows this directly?

Accepted Answer

BigQuery ML. BigQuery ML enables creating and running ML models using standard SQL queries directly in BigQuery. Vertex AI and AI Platform require data to be moved, and AutoML is a higher-level service but still not SQL-based.

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AI Platform

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AutoML Tables

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Vertex AI

Question 14

A company is using Vertex AI generative models for a high-volume text summarization service. Which two strategies can reduce operational costs?

Accepted Answer

Use batch prediction instead of online prediction.. Batch prediction reduces costs by processing multiple requests in a single batch job, which avoids the per-request overhead and idle compute time associated with online prediction. This is especially cost-effective for high-volume, non-real-time workloads like text summarization, as you pay only for the compute time used during the batch job rather than for each individual inference.

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Increase the model's max output tokens to 2048.

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Implement retry logic with exponential backoff.

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Lower the temperature parameter to 0.

Question 15

A company wants to ground Gemini responses with real-time Google Search results to improve accuracy of current events. Which feature enables this?

Accepted Answer

Google Search grounding. Google Search grounding connects Gemini to live search results to reduce hallucinations and provide up-to-date information.

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BigQuery ML

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Vertex AI Vector Search

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Gemini API function calling