Generative AI Leader Practice Test 31 — 15 Questions

Question 1

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 2

Refer to the exhibit. The endpoint is experiencing high latency during traffic spikes. The team wants to improve response time by reducing queueing. Which change to the configuration would be most effective?

Accepted Answer

Increase maxReplicaCount to 10. Increasing maxReplicaCount to 10 allows the autoscaler to provision more replicas during traffic spikes, distributing the incoming requests across additional endpoints. This directly reduces queueing at each replica because the load is spread over more instances, lowering per-instance latency. The change targets the root cause—insufficient capacity to handle peak load—rather than adjusting thresholds or model versions.

Answer

Decrease minReplicaCount to 0

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Change the model version to '2'

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Decrease the target value in autoscaling metric to 50

Question 3

A retail company wants to deploy a generative AI chatbot to assist customers with product recommendations. The chatbot must align with the company's brand voice and provide accurate, up-to-date information. Which strategy should the company prioritize when developing this solution?

Accepted Answer

Ground the model with proprietary product data and brand guidelines in a retrieval-augmented generation (RAG) architecture.. Option A is correct because retrieval-augmented generation (RAG) allows the chatbot to ground its responses in the company's proprietary product data and brand guidelines, ensuring factual accuracy and brand consistency. By retrieving relevant information from a curated knowledge base at inference time, the model can provide up-to-date recommendations without requiring retraining, which is critical for a retail environment with frequently changing inventory.

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Use a generic pre-trained model without customization to reduce development time.

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Deploy a large language model with a feedback loop to iteratively improve responses.

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Train the model on public customer reviews to capture common preferences.

Question 4

A team has developed a generative AI model for real-time translation. The evaluation metrics and business requirements are shown. Which business decision is most appropriate given the trade-offs?

Accepted Answer

Optimize the model for cost efficiency, even if accuracy drops slightly to 90%.. Option B is correct because the business requirements prioritize cost efficiency as the primary constraint, and the model currently exceeds the cost target. A slight accuracy drop to 90% (still within acceptable limits) allows cost to be reduced, aligning with the core business goal. The trade-off is acceptable since latency and other metrics remain within bounds, and accuracy at 90% still meets the minimum threshold for real-time translation quality.

Answer

Accept the model as-is because all other metrics are within limits.

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Prioritize latency reduction even if it increases cost.

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Reduce accuracy to 85% to achieve both latency and cost targets.

Question 5

A company is evaluating whether to build a custom generative AI solution from scratch or use a pre-built API from a cloud provider. Which factor most strongly supports the build-from-scratch approach?

Accepted Answer

The solution requires deep integration with proprietary data and unique domain-specific outputs.. Building a custom generative AI solution from scratch is most strongly supported when deep integration with proprietary data and unique domain-specific outputs is required. Pre-built APIs are typically trained on general data and may not capture the nuances of specialized domains, whereas a custom model can be fine-tuned or trained from scratch on proprietary datasets to achieve higher accuracy and relevance for unique business needs.

Answer

The team has limited machine learning expertise.

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Speed to market is the top priority.

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Minimizing initial development cost is critical.

Question 6

A media company uses generative AI to produce personalized news summaries. They notice that summaries occasionally contain factual errors and biased language. What business strategy should they implement to address these issues while maintaining user engagement?

Accepted Answer

Implement a human review layer for high-risk topics and use automated fact-checking for all content, with a feedback loop for model improvement.. Option C is correct because it balances accuracy and engagement by combining automated fact-checking with human review for high-risk topics. This hybrid approach reduces factual errors and biased language while maintaining the personalization that drives user engagement. The feedback loop continuously improves the model, addressing root causes rather than just symptoms.

Answer

Disable personalization and serve generic summaries to all users.

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Allow users to flag errors and manually correct summaries in real-time.

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Replace AI with entirely human-written summaries.

Question 7

A retail company plans to use Vertex AI's generative AI to create product descriptions. They need to ensure descriptions are factually accurate and do not misrepresent products. Which strategy should they prioritize?

Accepted Answer

Implement human-in-the-loop review. Human-in-the-loop (HITL) review is the correct strategy because it directly addresses the need for factual accuracy and prevention of misrepresentation. While generative AI can produce fluent text, it lacks a reliable grounding mechanism for product-specific facts, making human oversight essential to catch hallucinations, verify claims, and ensure compliance with advertising standards. This approach aligns with responsible AI practices and is a core recommendation for high-stakes content generation.

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Use prompt engineering

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Use a larger model

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Increase temperature parameter

Question 8

A financial institution wants to use generative AI to generate personalized investment advice. They face strict regulatory requirements on explainability and bias. Which approach should they take?

Accepted Answer

Use a RAG system with curated proprietary data. Option C is correct because a Retrieval-Augmented Generation (RAG) system allows the financial institution to ground generative AI outputs in curated, proprietary data sources (e.g., regulatory guidelines, client risk profiles, historical performance). This approach enhances explainability by enabling traceable citations back to specific documents, and reduces bias by controlling the data fed to the model, which is critical for meeting strict regulatory requirements like GDPR or SEC rules on algorithmic fairness.

Answer

Use a foundation model with prompt engineering

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Use a custom model trained from scratch

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Use a closed-source model with vendor lock-in

Question 9

A healthcare company wants to use generative AI to summarize patient records but must comply with HIPAA. Which deployment option should they choose?

Accepted Answer

Use an on-premises deployment of open-source model. Option C is correct because an on-premises deployment of an open-source model ensures that all patient data remains within the organization's controlled infrastructure, never leaving the local network. This eliminates any risk of data transmission to external cloud services, which is critical for HIPAA compliance where protected health information (PHI) must be safeguarded against unauthorized access or breaches. On-premises solutions allow the organization to implement its own security controls, encryption, and audit trails without relying on a third-party's compliance posture.

Answer

Use Vertex AI on Google Cloud with data residency

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Use Google Workspace AI

Answer

Use a third-party API

Question 10

A startup wants to leverage Google Cloud's generative AI but has limited ML expertise. Which Google Cloud service allows them to build generative AI applications without deep ML knowledge?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is a managed service that provides a low-code/no-code interface for building, testing, and deploying generative AI applications using pre-trained foundation models. It abstracts away the complexities of model training, infrastructure management, and ML pipeline orchestration, enabling teams with limited ML expertise to leverage generative AI capabilities through simple prompts and visual workflows.

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Cloud TPU

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TensorFlow

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Apigee

Question 11

A large enterprise is evaluating gen AI for internal knowledge management. They need to ensure accuracy and reduce hallucinations. Which strategy is most effective?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) is the most effective strategy because it grounds the model's responses in an external, authoritative knowledge base, retrieving relevant documents at inference time to provide factual context. This directly reduces hallucinations by ensuring the generated output is based on retrieved evidence rather than relying solely on the model's parametric memory, which is critical for enterprise knowledge management where accuracy is paramount.

Answer

Fine-tune a model on domain-specific data

Answer

Increase model temperature

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Use a larger model without customization

Question 12

A developer is using Vertex AI with an API key and gets the above error. What is the likely cause?

Accepted Answer

Expired API key. The error indicates that the API key used to authenticate with Vertex AI is no longer valid. API keys can expire due to a configured expiration policy or if they have been revoked in the Google Cloud Console. Since the developer is using an API key directly (rather than a service account or OAuth token), an expired key is the most direct cause of an authentication failure.

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Wrong endpoint

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Excessive quota

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Insufficient permissions

Question 13

A healthcare company wants to use Gemini to analyze patient records and summarize findings. Which data privacy practice is most critical when using the Gemini API on Vertex AI?

Accepted Answer

Enable Vertex AI Data Governance to mask or redact PII before sending to the API.. Option C is correct because Vertex AI Data Governance allows you to configure data masking or redaction of personally identifiable information (PII) before the data is sent to the Gemini API, ensuring compliance with healthcare regulations like HIPAA. This is the most critical practice because it prevents PHI from being exposed to the model or stored in logs, directly addressing the core privacy requirement. Disabling logging alone (Option B) does not prevent PHI from being processed by the model, and fine-tuning with PHI (Option A) introduces significant compliance risks.

Answer

Fine-tune Gemini using PHI to improve accuracy.

Answer

Disable request-response logging in Vertex AI to ensure data is not stored.

Answer

Use the text-davinci-003 model instead of Gemini, as it is more private.

Question 14

A retailer wants to generate personalized product descriptions using PaLM API. They have concerns about data privacy. What is the best practice to mitigate these concerns?

Accepted Answer

Enable data residency and use prompt engineering to avoid including personally identifiable information. Option D is correct because data residency ensures customer data is processed and stored within a specific geographic region, addressing regulatory compliance, while prompt engineering allows the retailer to avoid sending PII to the PaLM API entirely. This combination mitigates privacy risks without requiring custom model training or relying solely on encryption, which does not prevent the API from processing sensitive data.

Answer

Train a custom model from scratch on proprietary data stored on-premise

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Use the PaLM API directly with anonymized customer data

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Encrypt all data in transit and at rest using customer-managed encryption keys

Question 15

A media company uses generative AI to produce personalized news summaries for subscribers. They notice that the summaries sometimes contain factual inaccuracies, leading to customer complaints. The team needs to improve accuracy without slowing down the generation speed. They are using a pre-trained model via Vertex AI. What strategy should they implement?

Accepted Answer

Implement retrieval-augmented generation (RAG) with a trusted knowledge base. Option C is correct because retrieval-augmented generation (RAG) grounds the model's output in a trusted, external knowledge base, allowing it to retrieve verified facts in real time without retraining. This directly addresses factual inaccuracies while maintaining generation speed, as the pre-trained model remains unchanged and only the retrieval step is added. RAG avoids the latency of human review and the computational cost of fine-tuning or switching models.

Answer

Switch to a larger, more accurate foundation model

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Fine-tune the model on a dataset of verified news articles

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Add a human-in-the-loop review for every summary