Generative AI Leader Practice Test 29 — 15 Questions

Question 1

An ML engineer sees the above deployment output. The business wants to reduce inference cost. Which action should they take?

Accepted Answer

Change to a lower-cost machine type. Switching to a lower-cost machine type directly reduces the per-request compute cost without altering the model architecture or inference logic. This is a common cost-optimization strategy in cloud-based ML deployments, where instance types (e.g., from GPU to CPU or from a larger to a smaller GPU) can be selected based on latency and throughput requirements, provided the model fits within the machine's memory and compute constraints.

Answer

Use a larger model

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Deploy to multiple regions

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Increase traffic split

Question 2

A company is using Vertex AI to build a language model for generating legal documents. They need to ensure the model's outputs are accurate and verifiable. Which TWO features should they use?

Accepted Answer

Chain-of-thought reasoning. Grounding (citing sources) and chain-of-thought reasoning are the two features that directly improve accuracy and verifiability. Confidence indicators and safety filters are useful but do not directly address accuracy/verifiability.

Answer

Confidence indicators

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Safety filters for legal content

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Model Cards

Question 3

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 4

A data scientist is trying to get online predictions from a Vertex AI endpoint but receives the error shown. What is the most likely cause?

Accepted Answer

The model has not been deployed to the specified endpoint. The error indicates that the model is not deployed to the endpoint. In Vertex AI, an endpoint is a resource that hosts one or more deployed models. If a model has not been deployed to the endpoint, any prediction request to that endpoint will fail with a 'model not found' or similar error, even if the endpoint ID and region are correct.

Answer

The region in the request does not match the endpoint region

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The endpoint ID is incorrect

Answer

The model ID is incorrect

Question 5

A developer wants to build a RAG application using Vertex AI. Which vector database is natively integrated with Vertex AI for storing embeddings?

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search is the native vector database integrated with Vertex AI for storing and querying embeddings. It is purpose-built for high-dimensional vector similarity search, enabling efficient retrieval in RAG applications without requiring external infrastructure.

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Firestore

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Cloud SQL

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Bigtable

Question 6

Which THREE components are core to a typical Retrieval Augmented Generation (RAG) system?

Accepted Answer

Vector database. A vector database (B) is core to a RAG system because it stores and indexes embeddings of external knowledge chunks, enabling efficient similarity search to retrieve the most relevant context for a user query. This retrieved context is then provided to the LLM to ground its response in factual data, reducing hallucinations and improving accuracy.

Answer

Classifier

Answer

Rewriter

Question 7

A company wants to build a chatbot that answers questions based on internal documents. Which approach is most appropriate?

Accepted Answer

Use a prompt with the documents in the context. Retrieval-Augmented Generation (RAG) allows the chatbot to dynamically include relevant internal documents in the prompt context without modifying the underlying model. This approach leverages the pre-trained model's language understanding while grounding answers in specific, up-to-date internal data, avoiding the cost and latency of fine-tuning or retraining.

Answer

Use a pre-trained model without any customizations

Answer

Train a custom model from scratch

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Fine-tune a model on the documents

Question 8

Which TWO techniques are commonly used to control the style and tone of a generative model's output?

Accepted Answer

Fine-tuning on a dataset with desired style. Fine-tuning on a dataset that embodies the desired style directly adjusts the model's weights, making it consistently produce outputs with that specific tone and style. This is a fundamental technique for customizing generative models, as it teaches the model the exact patterns, vocabulary, and stylistic nuances present in the training data.

Answer

Adjusting the temperature

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Modifying the top_k value

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Changing the top_p value

Question 9

Which THREE are essential components of a responsible AI strategy for GenAI? (Select three.)

Accepted Answer

Human oversight for critical decisions. Human oversight for critical decisions (C) is essential because GenAI models can produce plausible but incorrect or harmful outputs. A responsible AI strategy mandates that a human-in-the-loop reviews high-stakes outputs, such as medical diagnoses or financial approvals, to prevent automated errors from causing real-world harm. This aligns with the principle of human accountability in AI governance frameworks like the NIST AI Risk Management Framework.

Answer

Use of only open-source models

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Maximum model size

Question 10

A company wants to generate a video from a text description using Google Cloud. Which service is designed for this?

Accepted Answer

Veo. Veo is Google Cloud's generative AI model specifically designed for creating high-quality videos from text or image prompts. It leverages advanced diffusion and transformer architectures to generate coherent video sequences, making it the correct choice for text-to-video generation.

Answer

Codey

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Chirp

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Imagen

Question 11

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.. 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.

Answer

Replace AI with entirely human-written summaries.

Question 12

A retail company wants to build an internal knowledge base chatbot using Vertex AI. They need to ensure the chatbot only answers from approved company documents and can handle updates without retraining. Which TWO components should they include? (Choose 2)

Accepted Answer

RAG Engine to connect the chatbot to a document index. RAG Engine retrieves answers from the indexed documents, satisfying the approved-source requirement. A vector store indexes the documents for retrieval. Fine-tuning is not needed. Model Garden is just a model hub. Apps Script is for Workspace automation.

Answer

Fine-tuned model on company documents

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Model Garden to select a pre-trained model

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Apps Script to update the document index

Question 13

A developer is using the Vertex AI PaLM API to generate code. They want to ensure the output is safe and adheres to company policies. Which THREE attributes can they configure in the safety_settings parameter?

Accepted Answer

Toxicity. The safety_settings parameter in the Vertex AI PaLM API allows developers to filter content based on predefined harm categories, including toxicity. This setting enables the API to block or adjust responses that contain toxic language, ensuring the generated code adheres to company safety policies by preventing harmful or offensive outputs.

Answer

Language detection

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Sentiment analysis

Question 14

A company fine-tunes a model using Vertex AI and notices the model's performance drops on the original training task (e.g., language understanding) after fine-tuning for a new task (e.g., summarization). What could be the cause?

Accepted Answer

Catastrophic forgetting. Catastrophic forgetting occurs when a neural network loses previously learned knowledge upon being fine-tuned on a new task. In this scenario, fine-tuning the model for summarization overwrites the weights responsible for language understanding, causing performance degradation on the original task. This is a well-known limitation of sequential fine-tuning in deep learning.

Answer

Data leakage

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Model quantization

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Underfitting

Question 15

A small marketing agency with 10 employees is exploring generative AI to create personalized ad copy for their clients. They have a limited budget of $5,000 per month and no in-house machine learning expertise. The CEO wants to have a working prototype within two weeks to show to a potential client. The agency's data is sensitive and cannot be shared with unauthorized third parties. Which strategy should they pursue?

Accepted Answer

Use Google's Generative AI Studio with pre-trained models via API. Google's Generative AI Studio provides pre-trained models via API, allowing the agency to quickly prototype personalized ad copy without needing in-house ML expertise. This approach respects the $5,000 budget (API usage is cost-effective for small-scale prototyping), meets the two-week timeline (no training required), and ensures data privacy by using Google Cloud's data governance controls (data is not shared with unauthorized third parties).

Answer

Hire a team of data scientists to fine-tune an open-source model

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Use a third-party platform that requires on-premise deployment

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Build a custom foundation model from scratch using their client data