Generative AI Leader Practice Test 26 — 15 Questions

Question 1

A large enterprise is deploying a generative AI-powered code assistant for their developers. The solution uses Vertex AI with a fine-tuned Codey model. The security team requires that all prompts and responses be logged for audit purposes, but the logs must not contain sensitive information such as API keys or passwords. The operations team is concerned about high latency during peak usage. You need to design a solution that meets security requirements without compromising performance. Which approach should you take?

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

Accepted Answer

Enable Vertex AI model monitoring with Cloud Logging, and configure a log sink with a custom exclusion filter to redact sensitive patterns before storing. It uses Vertex AI model monitoring with Cloud Logging to capture prompts and responses, then applies a custom exclusion filter with a log sink to redact sensitive patterns (e.g., API keys, passwords) in real time before logs are stored. This meets the security requirement for audit logging without sensitive data while avoiding the latency overhead of post-processing or a custom proxy, thus satisfying the operations team's performance concern.

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Use Cloud Audit Logs to capture all API calls to Vertex AI, but do not log the actual prompts and responses

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Log all prompts and responses to Cloud Storage and use a Cloud DLP job to scan and redact sensitive data periodically

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Implement a custom proxy that logs all requests after stripping sensitive data, then forward to the model

Question 2

You are an ML engineer at a retail company. You have deployed a generative AI model on Vertex AI to generate product descriptions. The model uses a custom container and is deployed to a single endpoint. Recently, you noticed that inference latency has increased significantly during peak hours, causing timeouts. You have checked the logs and found that the CPU utilization on the deployed instances is consistently above 90% during peak hours. The model is currently deployed with a single machine type (n1-standard-4) and no scaling. You need to reduce latency without incurring excessive cost. What should you do?

Accepted Answer

Change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5. Upgrading to a larger machine (n1-standard-8) provides more CPU cores to handle the increased inference workload, while enabling autoscaling (min=1, max=5) allows the deployment to dynamically add replicas during peak hours to distribute the load and reduce latency. This combination addresses the high CPU utilization without over-provisioning during off-peak times, thus controlling cost.

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Optimize the model using quantization and reduce the number of replicas

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Switch to batch prediction instead of online prediction

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Add a GPU accelerator to the existing machine

Question 3

What is the purpose of grounding in Vertex AI?

Accepted Answer

To connect model outputs to verifiable sources. Grounding in Vertex AI connects model outputs to verifiable, external sources of information (such as Google Search, enterprise data sources, or third-party databases) to reduce hallucinations and improve factual accuracy. By referencing grounded sources, the model can provide citations and allow users to verify claims, which is critical for enterprise applications requiring trust and compliance.

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To improve training speed

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To reduce model size for faster inference

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To enable multi-modal inputs

Question 4

Which Google Cloud service is specifically designed for generating code from natural language descriptions?

Accepted Answer

Codey. Codey is the correct answer because it is Google Cloud's foundational code generation model, specifically designed to accept natural language prompts and produce source code in various programming languages. It powers features like code completion, code generation, and chat-based code assistance within Vertex AI, directly addressing the requirement of generating code from natural language descriptions.

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Imagen

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Chirp

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Gemini

Question 5

The exhibit shows the deployment configuration for a conversational AI model used in a finance application. Users report that responses are creative but often contain factually incorrect financial advice. Which parameter change would most improve factual accuracy?

Accepted Answer

Add grounding sources, such as "EnterpriseSearch" or "Web". Adding grounding sources like EnterpriseSearch or Web provides the model with access to authoritative, up-to-date financial data, which directly reduces hallucinations by anchoring responses in verified facts rather than relying solely on the model's parametric knowledge. This is the most effective technique for improving factual accuracy in a domain where correctness is critical.

Answer

Lower temperature to 0.1

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Increase topP to 1.0

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Increase maxOutputTokens to 1024

Question 6

A media company is using a generative AI model to create video captions. The model is deployed on Vertex AI with autoscaling. During peak hours, they observe high latency and request timeouts. Which action would most effectively address this issue?

Accepted Answer

Increase the minimum number of replicas in the autoscaling configuration. Increasing the minimum number of replicas ensures that during peak hours, the model already has a baseline of warm instances ready to handle requests, reducing cold-start latency and preventing timeouts. Autoscaling can take time to spin up new replicas, so a higher minimum replica count directly mitigates the latency spike by pre-provisioning capacity.

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Optimize the prompt to reduce output length

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Reduce the maximum number of replicas to limit resource usage

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Switch to a GPU-based machine type for faster inference

Question 7

Which TWO of the following are key differences between generative AI and discriminative AI? (Choose two.)

Accepted Answer

Generative models can create new data samples, while discriminative models only assign labels to existing data.. Generative AI models learn the underlying distribution of the data, enabling them to generate new, realistic samples (e.g., images, text) from the learned distribution. In contrast, discriminative models learn decision boundaries to classify or label existing data without the ability to create new data instances. This fundamental difference in capability—creation versus discrimination—is a core distinction between the two paradigms.

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Generative models require less training data than discriminative models.

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Generative models cannot be used for supervised learning tasks like classification.

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Discriminative models always outperform generative models on tasks like image classification.

Question 8

A generative AI model for code generation sometimes produces syntactically incorrect code. The team wants to reduce syntax errors without retraining the entire model. Which approach is most effective?

Accepted Answer

Implement constrained decoding with grammar rules. Constrained decoding with grammar rules directly enforces the syntax of the target programming language during token generation, preventing the model from producing invalid constructs. This approach modifies the decoding process (e.g., using a context-free grammar or a formal syntax specification) to mask or forbid tokens that would lead to a syntax error, without altering the underlying model weights. It is the most effective method because it guarantees syntactically correct output at generation time, rather than relying on post-hoc fixes or probabilistic adjustments.

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Run a syntax checker after generation and regenerate

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Add a system prompt that instructs the model to produce valid code

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Increase beam search width

Question 9

A company wants to run a large-scale training job for a 175B parameter model. They need to minimize training time and cost. Which TPU version and configuration should they choose?

Accepted Answer

TPU v5e-256. The TPU v5e-256 offers the best performance-per-dollar for large-scale training of a 175B parameter model. With 256 chips in a pod, it provides massive parallelism and high memory bandwidth, significantly reducing training time compared to earlier generations while maintaining cost efficiency through optimized architecture.

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TPU v2-8

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TPU v3-32

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TPU v4-64

Question 10

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.

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Vertex AI Prediction

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Vertex AI Pipelines

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Cloud Functions

Question 11

What is the primary function of embeddings in the context of generative AI?

Accepted Answer

To convert tokens into a fixed-length vector that captures semantic meaning. Embeddings are numerical representations of data that capture semantic meaning, enabling vector search and semantic similarity. They are not used for token generation directly, and they are not the same as temperature or context window.

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To control the creativity of the model's output

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To define the maximum length of the generated text

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To determine the next token in a sequence during generation

Question 12

A machine learning engineer wants to reduce the latency of a Gemini-based chatbot running in production. Which TWO strategies would be MOST effective?

Accepted Answer

Switch from Gemini Pro to Gemini Flash. Gemini Flash is a lighter, more efficient model variant designed for lower latency and higher throughput compared to Gemini Pro. By switching to Flash, the engineer reduces the computational overhead per request, directly decreasing response time for the chatbot.

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Enable streaming mode

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Fine-tune the model on the specific task

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Increase the temperature to 1.0

Question 13

A team is selecting a foundation model for a text summarization use case. They need to consider factors that affect both model performance and production deployment. Which THREE factors are most critical? (Choose three.)

Accepted Answer

Inference latency and throughput capabilities.. Inference latency and throughput are critical for production deployment because they directly determine the user experience and operational cost. A model with high latency may be unsuitable for real-time summarization, while low throughput limits the number of concurrent requests the system can handle, affecting scalability and cost-efficiency.

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Model parameter count (billions of parameters).

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Training data provenance and licensing.

Question 14

A financial services company wants to use Vertex AI Grounding with enterprise data to power a regulatory compliance chatbot. They have strict data residency requirements: data must remain in the EU. What should they do?

Accepted Answer

Enable Data Residency by selecting a EU region during data store creation. Vertex AI Grounding with enterprise data requires a data store, and when creating that data store in Vertex AI Search, you can select a EU region (e.g., europe-west1) to enforce data residency. This ensures all indexed enterprise data and grounding operations remain within the EU, satisfying strict regulatory requirements. The data store region determines where the data is stored and processed, independent of the Vertex AI endpoint region used for model inference.

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Use a VPN to the US region

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Convert data to private tokens

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Use a Vertex AI endpoint in a European region

Question 15

A company has a generative AI chatbot on Vertex AI that shows high response latency. They want to reduce latency without significantly increasing cost. Which TWO actions should they take? (Choose two.)

Accepted Answer

Enable streaming responses using server-sent events.. Enabling streaming responses using server-sent events (SSE) allows the chatbot to send tokens incrementally as they are generated, rather than waiting for the full response. This reduces the perceived latency for the end user, as the first token appears much sooner, even though the total generation time may remain similar. This approach directly addresses high response latency without increasing compute cost, as it does not require additional infrastructure or model changes.

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Increase the min_replica_count to keep more instances always warm.

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Use machine types with GPUs.

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Switch to a larger model like Gemini 1.5 Pro for better accuracy.