Generative AI Leader Practice Exam 63 — 20 Questions

Question 1

A hospital network wants to transcribe clinician dictation and then have a generative model produce structured discharge summaries from those transcripts, all within Google Cloud. Which combination of offerings matches this workflow?

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

Accepted Answer

Cloud Speech-to-Text followed by Vertex AI Gemini. The workflow requires converting spoken dictation into text and then generating a structured summary from that text. Speech-to-Text handles the audio transcription, and Gemini on Vertex AI performs the generative summarization, so this pairing correctly sequences the two capabilities. The alternatives invert the audio direction or substitute document and image services that cannot transcribe dictation.

Answer

Document AI followed by Cloud Vision API

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Cloud Text-to-Speech followed by Vertex AI Gemini

Answer

Vertex AI Gemini followed by Cloud Speech-to-Text

Question 2

A media company is using a foundation model in Vertex AI to summarize long articles. They notice that summaries sometimes omit key details from the middle of very long articles. Which action should they take to improve summary completeness while staying within the model's context window?

Accepted Answer

Split the article into chunks, summarize each chunk, then combine the summaries in a final prompt.. Long articles can exceed a model's effective context, causing middle content to be overlooked. Chunking the article, summarizing each part, and then combining those summaries ensures all sections are processed and represented. This hierarchical approach stays within context limits and improves completeness, directly addressing the observed omission of key details.

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Add a system instruction telling the model to always include all key details.

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Switch to a smaller model variant to reduce latency and cost.

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Increase the model's temperature setting to encourage more creative output.

Question 3

A government agency is deploying a generative AI chatbot to answer citizen questions about public services. The chatbot must provide accurate and consistent information, scale to handle peak loads during tax season, and comply with strict data sovereignty laws that require all data to stay within the country. The agency has a moderate budget and in-house IT team but limited AI expertise. Which deployment architecture should they choose?

Accepted Answer

Deploy a pre-trained model on Vertex AI in the required region with auto-scaling. Deploying a pre-trained model on Vertex AI in the required region is the best architecture because it keeps data in-country, auto-scales for peak demand, and uses a managed service that minimizes AI operations burden. Option A is not suitable because on-premises hosting requires significant AI expertise and does not scale easily. Option C is not suitable because multi-region deployment can move data outside the required country and violate data sovereignty. Option D is not the best choice: while a third-party managed service may claim data residency, it adds a separate vendor relationship, potential integration and cost overhead, and less direct control over regional infrastructure and compliance than Vertex AI's explicit in-region deployment.

Answer

Build and host the model on-premises using open-source tools

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Deploy the model on Vertex AI across multiple regions for availability

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Use a third-party managed generative AI service that guarantees data residency

Question 4

A media company is using a large language model to generate scripts for animated shorts. They notice that the model sometimes produces content that closely resembles existing copyrighted scripts from well-known movies. Which technique should they implement to reduce the risk of generating such content?

Accepted Answer

Use a filtered decoding strategy that blocks outputs matching known copyrighted content.. Filtered decoding or output filtering is a proactive measure that scans generated text for matches with known copyrighted material and blocks or alters those outputs. This directly addresses the risk of the model reproducing protected content. Other techniques like temperature adjustment or repetition penalties do not specifically target copyright infringement and may not be effective.

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Apply a repetition penalty to discourage the model from repeating phrases.

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Increase the temperature to encourage more creative and original outputs.

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Fine-tune the model on the company's own original scripts to bias it toward unique content.

Question 5

A media company is deploying a generative AI application that must handle bursts of traffic while keeping costs predictable. They want to pay only for the compute resources actually consumed and avoid managing infrastructure. Which Google Cloud option best matches these requirements for serving a foundation model?

Accepted Answer

Use Vertex AI's pay-as-you-go, on-demand model serving endpoints. On-demand serving in Vertex AI charges based on actual consumption and scales automatically, so traffic bursts are handled without provisioning capacity in advance. This satisfies both the cost predictability and the no-infrastructure-management requirements. Dedicated nodes and self-managed clusters bill for reserved capacity regardless of use, and container platforms reintroduce operational responsibilities that the company wants to avoid.

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Host the model on Cloud Run with a GPU-enabled container

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Reserve a fixed number of dedicated prediction nodes in Vertex AI

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Provision a dedicated GPU cluster on Compute Engine with autoscaling node pools

Question 6

A media company is building an internal tool that generates first-draft marketing copy from campaign briefs. Legal insists that the tool never reproduce copyrighted third-party text verbatim, and the content team wants a measurable way to compare draft quality across prompt revisions. Which two-part approach best addresses both needs?

Accepted Answer

Enable Vertex AI safety filters and recitation checking, and use Vertex AI evaluation to score draft quality across prompt versions.. Recitation checking inspects generated output for passages that closely match training data and can block or flag them, which is the systematic safeguard legal requires. Vertex AI evaluation supplies repeatable quality scores, so prompt revisions can be compared with evidence rather than opinion. Together they cover both the compliance and the measurement objectives in one design.

Answer

Lower the model temperature to zero and require editors to sign off on every draft before it is used.

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Add a keyword blocklist that strips any phrase appearing in a public style guide from generated drafts.

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Deploy the largest available model with maximum output tokens and rely on human editors to catch any copied passages.

Question 7

A retail bank uses a generative AI assistant to answer customer questions about account policies. Compliance requires that every response cite the specific internal policy document section it used. Which approach best enforces this requirement?

Accepted Answer

Use retrieval-augmented generation to fetch policy passages and instruct the model to cite them.. A citation mandate is a grounding problem, not a sampling or behavior problem. Retrieval-augmented generation supplies authoritative policy passages at inference time and lets the prompt demand a citation for each. Temperature tuning, generic instructions, and fine-tuning either do not provide sources or cannot guarantee verifiable, current section references.

Answer

Fine-tune the model on the bank's policy manuals so it memorizes the sections.

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Add a system instruction telling the model to never guess and to be accurate.

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Lower the model's temperature to zero so responses are deterministic.

Question 8

A financial services firm is using a generative AI model to answer customer queries about account balances. The model sometimes provides outdated information because it relies on its training data. The firm wants to ensure the model always uses the most current account data. Which technique should they use?

Accepted Answer

Use retrieval-augmented generation (RAG) to fetch current account data from a database.. Retrieval-augmented generation (RAG) is designed to ground model outputs in external, up-to-date knowledge. By integrating a retriever that queries the firm's database for current account balances, the model can generate responses that reflect the latest information. This approach avoids the cost and latency of frequent fine-tuning and ensures accuracy for time-sensitive queries.

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Fine-tune the model daily with the latest account data.

Answer

Increase the model's context window to include more historical data.

Answer

Apply prompt engineering to instruct the model to use the latest data.

Question 9

A media company wants its editors to summarize long internal research documents. Legal requires that no document content be used to train or improve any model, and that data stays within the company's Google Cloud project. Which capability should the company verify before adopting a Gemini-based solution on Vertex AI?

Accepted Answer

That prompts and responses are excluded from model training and covered by Google Cloud's data processing terms.. Legal's requirements are about how data is governed, not about model features. Confirming that prompts and responses are excluded from training and processed under enterprise data terms addresses both the no-training condition and the residency condition. Fine-tuning contradicts the no-training rule, context window size is irrelevant to governance, and personal accounts bypass the enterprise controls the company depends on.

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That the summarization endpoint supports a 1-million-token context window for the longest documents.

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That the company's editors can access the Gemini web app with their personal Google accounts for convenience.

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That the model can be fine-tuned on the research documents so summaries match the editors' preferred style.

Question 10

A software company wants to build a generative AI application that can answer questions based on its internal documentation. The documentation is stored in Google Drive and Confluence. They need a managed solution that can index these sources, provide relevant answers with citations, and integrate with their existing identity provider for access control. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Search. Vertex AI Search is a fully managed service that can connect to Google Drive and Confluence, index the content, and provide generative AI answers with citations. It also integrates with identity providers to enforce document-level access control, making it the ideal solution for the software company's needs.

Answer

Vertex AI Feature Store

Answer

Cloud SQL

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Vertex AI Pipelines

Question 11

A marketing team is using a generative AI model to create ad copy. They notice that the outputs sometimes include made-up statistics and false claims about their products. They want to reduce these hallucinations without retraining the model. What should they do?

Accepted Answer

Use grounding with a source of truth such as a product database.. Grounding connects the model to verified external data, ensuring responses are based on facts rather than model assumptions. It is a no-retraining approach that directly targets hallucinations by providing context. Other methods like temperature or token limits do not reliably improve factual accuracy.

Answer

Decrease the maximum output tokens.

Answer

Increase the model's temperature parameter.

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Fine-tune the model on a small set of correct ad copies.

Question 12

A media company wants to automatically generate concise summaries of news articles using a generative AI model. They need a fully managed service that provides access to Google's foundation models without requiring machine learning expertise. Which Google Cloud offering should they choose?

Accepted Answer

Generative AI on Vertex AI (Gemini models). Generative AI on Vertex AI offers Gemini models as a fully managed service, enabling users to generate summaries via simple API calls without ML expertise. It provides pre-trained models that excel at summarization tasks. The other options are either model catalogs requiring configuration, custom ML training services, or specialized translation tools that do not perform summarization.

Answer

Vertex AI Model Garden

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AutoML Natural Language

Answer

Cloud Translation API

Question 13

A startup wants to experiment with Gemini prompts in a browser-based console, compare responses from different models, and save promising prompts for later reuse, without provisioning infrastructure. Which Google Cloud offering best fits this need?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides an interactive, code-light workspace for writing prompts, comparing model outputs, and storing prompts for reuse, which aligns precisely with the startup's experimentation goal. The other services are specialized for data warehousing, document extraction, or image analysis and lack the prompt design and comparison capabilities, so they cannot deliver the described experience.

Answer

BigQuery ML

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Cloud Vision API

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Document AI

Question 14

A telecom company wants to launch a generative AI assistant that summarizes support tickets for agents. Leadership asks how to measure whether the pilot is delivering business value before expanding it. Which approach best evaluates business impact?

Accepted Answer

Measure average handle time, ticket resolution rate, and agent satisfaction before and after deployment.. Business value from a generative AI pilot is demonstrated through operational outcomes such as reduced handle time, higher resolution rates, and improved agent experience, measured against a pre-deployment baseline. Technical indicators like perplexity, token counts, and parameter size describe model behavior or cost, not the value delivered to the support organization. Leadership needs outcome metrics tied to the workflow being augmented.

Answer

Track the model's perplexity score on a held-out sample of support tickets.

Answer

Compare the number of model parameters against competing open-source models.

Answer

Count the total number of tokens processed by the model each day.

Question 15

A healthcare organization is evaluating Google Cloud's generative AI offerings for building a patient triage assistant. They need a model that can process text and images from patient records and generate text responses. They also require the ability to fine-tune the model on their own data. Which two Google Cloud services or features should they use? (Choose two.)

Accepted Answer

Vertex AI Fine-Tuning. Gemini on Vertex AI provides the multimodal capabilities to process text and images, and Vertex AI Fine-Tuning enables customization on the organization's data. Together, they form the foundation for a tailored patient triage assistant that can handle diverse patient records.

Answer

Vertex AI Pipelines

Answer

Vertex AI Model Garden

Answer

Vertex AI Feature Store

Question 16

A healthcare analytics team uses Gemini on Vertex AI to extract structured data from clinical notes. The model occasionally outputs invalid JSON, breaking downstream processing. The team wants to enforce a strict output schema. Which approach should they use?

Accepted Answer

Use controlled generation with a response schema in the Gemini API request to constrain output to the defined JSON structure.. Controlled generation with a response schema constrains decoding so the model emits JSON that conforms to the defined structure, guaranteeing parseable output. Prompt hints and low temperature reduce but do not eliminate format errors, fine-tuning is heavier and still not absolute, and regex repair is fragile. Schema-constrained decoding is the correct tool for strict structured output.

Answer

Fine-tune the model on a dataset of clinical notes paired with valid JSON outputs.

Answer

Post-process the model output with a regular expression to fix malformed JSON before parsing.

Answer

Add 'return JSON' to the prompt and set temperature to 0.2 to encourage valid formatting.

Question 17

A marketing analyst at a retail company wants to use a generative AI model to draft product descriptions for 500 new items. The analyst expects the model to understand the context of each product from a short prompt and produce varied, coherent text. Which core capability of generative AI is the analyst primarily relying on?

Accepted Answer

The model's ability to generate novel, contextually relevant text by predicting likely sequences of words.. The analyst needs to produce original, varied, and contextually appropriate text for each product. This is achieved through the generative capability of large language models, which predict and generate sequences of words based on the input prompt. Other options describe classification, retrieval, or sentiment analysis—tasks that do not create novel product descriptions.

Answer

The model's ability to perform sentiment analysis on customer reviews to gauge product reception.

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The model's ability to retrieve exact matches from a product database and insert them into a template.

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The model's ability to classify products into predefined categories based on their attributes.

Question 18

A marketing team wants to generate product descriptions from a short list of features using a Google Cloud generative AI model. They have no labeled examples and want to avoid any model training. Which approach should they use?

Accepted Answer

Zero-shot prompting with a foundation model in Vertex AI. Zero-shot prompting lets a pretrained foundation model perform a new task from instructions alone, with no examples or training. Since the team has no labeled data and wants to avoid training, this is the only approach that meets all constraints. Fine-tuning, training from scratch, and rule-based templates either require training or are not generative AI.

Answer

Training a new model from scratch using the team's feature list

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Fine-tuning the model on a dataset of existing product descriptions

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Using a traditional rule-based template system to assemble descriptions

Question 19

A bank is deploying a Gemini-powered assistant on Vertex AI to answer questions about loan products. Compliance requires that every answer be traceable to an approved internal source and that the assistant refuse to answer when no approved source supports the response. Which configuration should the bank implement?

Accepted Answer

Ground the model with Vertex AI Search over approved sources and use grounding confidence thresholds. Grounding the assistant in approved sources through Vertex AI Search makes each answer retrievable from a vetted document and provides citations for audit. Using grounding confidence thresholds lets the bank define when support is too weak and instruct the assistant to refuse. Fine-tuning, temperature changes, and safety filters do not deliver source traceability or principled abstention.

Answer

Raise the model temperature and add a system instruction to be accurate

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Enable a higher safety filter threshold so harmful content is blocked

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Fine-tune Gemini on historical chat logs from the bank's contact center

Question 20

A developer is experimenting with a Gemini model in Vertex AI Studio and notices that for the same prompt, the model produces different answers each time. The developer needs the output to be as consistent and deterministic as possible for a classification task. Which parameter change should they make?

Accepted Answer

Set the temperature to 0. Sampling parameters govern how the model picks the next token. Temperature 0 selects the most probable token deterministically, which is ideal when the same input should map to the same output, as in classification. Other parameters such as token limits and streaming affect output length and delivery, not the randomness of selection, so they cannot deliver the consistency the developer needs.

Answer

Increase the maximum output tokens

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Enable streaming responses

Answer

Set the temperature to 2