Generative AI Leader Practice Exam 61 — 20 Questions

Question 1

A healthcare company is using a generative AI model to produce patient education materials. They want to ensure the output is accurate, avoids harmful advice, and adheres to medical guidelines. Which TWO techniques should they implement to improve the safety and reliability of the model's output? (Choose two.)

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

Accepted Answer

Implement retrieval-augmented generation (RAG) using a curated medical knowledge base.. Retrieval-augmented generation grounds the model in a trusted medical knowledge base, ensuring accuracy and up-to-date information. Safety filters screen out harmful content, adding a protective layer. Together, these techniques enhance both the reliability and safety of patient education materials.

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Reduce the maximum output token limit to shorten responses.

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Increase the model's temperature to encourage more diverse responses.

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Fine-tune the model on a dataset of patient education materials without any safety annotations.

Question 2

A healthcare company is using a generative AI model to produce patient education materials. They want to ensure the output is accurate, avoids harmful advice, and adheres to medical guidelines. Which TWO techniques should they implement to improve the safety and reliability of the model's output? (Choose two.)

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

Accepted Answer

Retrieval-augmented generation (RAG) with the guidelines database as the retrieval source.. Retrieval-augmented generation retrieves relevant, current information from an external database and includes it in the prompt, grounding the model's output in the latest guidelines. This approach avoids retraining and ensures the content is based on verified, up-to-date sources. Other methods like fine-tuning, temperature adjustment, or chain-of-thought do not provide the necessary external knowledge.

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Increasing the model's temperature to encourage more up-to-date responses.

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Fine-tuning the model on the latest guidelines.

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Using a chain-of-thought prompt to ask the model to reason about the latest guidelines.

Question 3

A financial services firm uses a generative AI model to summarize quarterly earnings calls. The summaries must consistently follow a strict format: an executive summary, key financial metrics, and forward-looking statements. The model sometimes omits sections or changes the order. Which technique should they use to enforce the structure?

Accepted Answer

Use controlled generation with a response schema (e.g., JSON schema) to constrain the model's output.. Controlled generation with a response schema constrains the decoding process so that the model can only produce tokens that satisfy a predefined structure. This guarantees that each required section appears and in the correct order, which is essential for consistent financial summaries. Unlike few-shot examples, schema-based control is deterministic and enforceable at the API level, making it the most reliable choice for strict formatting.

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Use few-shot prompting with examples that demonstrate the exact desired format.

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Increase the model's temperature to encourage more structured output.

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Reduce the max output tokens to force the model to include all sections.

Question 4

A marketing team uses a text generation model to create ad copy. They want the output to be more diverse and creative, exploring unusual angles. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature is the parameter that directly controls randomness in token selection. Increasing it flattens the probability distribution, making less likely words and ideas more probable, which yields more diverse and creative ad copy. While top-p and top-k also affect sampling, temperature is the primary and most intuitive knob for encouraging novel angles without changing other constraints.

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Top-k

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Max output tokens

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Top-p (nucleus sampling)

Question 5

A financial services firm is using a large language model to generate quarterly investment summaries from raw market data. The summaries occasionally contain fabricated statistics and sometimes omit key risk factors. The team wants to improve factual accuracy and completeness without retraining the model. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Retrieval-augmented generation (RAG) using a curated knowledge base of verified financial reports.. Retrieval-augmented generation supplies the model with verified, up-to-date information, reducing fabrication and improving coverage of key points. A fact-verification API adds a validation layer that catches any remaining inaccuracies. Together, they address both hallucination and omission without retraining the model.

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Increasing the temperature setting to encourage more creative wording.

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Fine-tuning the model on a small set of historical summaries to improve style.

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Using a chain-of-thought prompt that asks the model to reason step by step before writing the summary.

Question 6

A financial services firm uses a generative AI model to draft client emails. The drafts are accurate but sometimes use an overly casual tone. The firm wants to enforce a consistently formal tone across all drafts. Which approach is most reliable for this requirement?

Accepted Answer

Provide a system instruction that defines the desired formal tone and include a short example of an approved email.. System instructions provide persistent behavioral guidance, and an approved example shows the model the exact tone desired. This combination is more reliable than sampling changes, casual fine-tuning, or brittle post-processing. It directly enforces formality across all drafts and scales without retraining, making it the best fit for the firm's consistency requirement.

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Fine-tune the model on a large corpus of casual emails to broaden its style range.

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Add a post-processing step that replaces casual words with formal synonyms using a fixed dictionary.

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Set the temperature to a high value so the model produces more varied language.

Question 7

A retail analytics team uses a Gemini model to answer questions over a large product catalog stored in BigQuery. Answers are sometimes outdated because the model relies on its training data. They want responses to reflect the latest catalog rows and cite the source table. Which technique should they implement?

Accepted Answer

Use retrieval-augmented generation by querying BigQuery and inserting the retrieved rows into the prompt as grounding context.. Retrieval-augmented generation grounds the model in data fetched at request time, which keeps answers current and allows the source table to be cited. Fine-tuning freezes a stale snapshot, stuffing the whole catalog is impractical, and temperature changes only affect randomness. When answers must track a live system of record, retrieval is the appropriate pattern.

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Increase the model's context window and paste the entire product catalog into every prompt so the model always has all data.

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Fine-tune the model on a recent export of the product catalog so the weights contain current product data.

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Enable a higher temperature so the model can combine its training knowledge with newer patterns and produce fresher answers.

Question 8

A company is using a generative AI model to create personalized email responses to customer inquiries. The responses sometimes contain factual errors or irrelevant information. The company wants to improve the accuracy and relevance of the responses. Which TWO techniques should they use? (Choose two.)

Accepted Answer

Use prompt engineering to provide clear instructions and examples of desired responses.. Retrieval-augmented generation (RAG) grounds the model's responses in a verified knowledge base, ensuring factual accuracy and relevance. Prompt engineering with clear instructions and examples further guides the model to produce desired outputs. Together, these techniques directly target the issues of factual errors and irrelevance without the overhead of fine-tuning.

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Fine-tune the model on a dataset of customer inquiries and responses.

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Increase the model's temperature to make responses more creative.

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Reduce the model's max output tokens to limit response length.

Question 9

A marketing team uses a text generation model to draft campaign copy. They want the output to consistently follow a specific brand voice and include a call to action at the end of every draft. Which technique should they apply?

Accepted Answer

Provide a system instruction that defines the brand voice and requires a call to action, plus one or two exemplar drafts.. System instructions plus exemplars are the standard way to enforce tone and mandatory content without training. The instruction defines persistent rules, and examples show the desired pattern. Sampling changes affect randomness, and shorter prompts remove guidance, so neither reliably produces consistent brand voice and a guaranteed call to action.

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Set the temperature to zero so the model always produces the same deterministic wording for every campaign.

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Increase the top-k value so the model samples from a broader set of words and naturally adopts a more distinctive voice.

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Shorten the prompt to only the product name so the model has maximum freedom to express the brand personality.

Question 10

A product team is using a generative AI model to summarize customer feedback from multiple sources. The summaries are sometimes missing key themes or including irrelevant details. The team wants to improve the quality of the summaries without retraining the model. (Choose two.)

Accepted Answer

Provide a structured prompt that specifies the sections to include, such as top themes, sentiment, and representative quotes.. A structured prompt and few-shot examples both guide the model to include the right themes and exclude irrelevant details. The structured prompt defines the required sections, while examples show the desired format and depth. Together they improve summarization quality without retraining, directly addressing the team's concerns about missing themes and extraneous content.

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Reduce the maximum output tokens so the model is forced to include only the most important points.

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Increase the temperature to encourage the model to explore more diverse themes.

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Fine-tune the model on a dataset of customer feedback summaries.

Question 11

A software company is using a large language model to generate code snippets from natural language descriptions. The generated code often contains syntax errors or uses deprecated functions. The team wants to improve the correctness of the code. Which technique should they use?

Accepted Answer

Few-shot prompting with examples of correct code and explanations of why deprecated functions are avoided.. Few-shot prompting supplies the model with examples of correct code and explanations, which guides it to generate syntactically valid and up-to-date code. This technique leverages in-context learning to improve accuracy without retraining. Other parameter adjustments like temperature, max output tokens, or top-p do not provide the necessary guidance for correctness.

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Increasing the temperature to allow more creative code solutions.

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Using a top-p value of 1.0 to consider all possible tokens.

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Reducing the max output tokens to force shorter code.

Question 12

A media company uses Gemini models on Vertex AI to draft news briefs from long press releases. Editors report that drafts sometimes invent quotes and statistics that do not appear in the source. The team wants to reduce these fabrications while keeping the model's fluent writing. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Enable grounding with Vertex AI Search over a curated corpus of approved press releases.. Combining an explicit instruction to answer only from the supplied text with retrieval grounding over an approved corpus gives the model both a behavioral constraint and authoritative evidence. Together they reduce invented quotes and statistics while preserving fluent composition. Sampling changes like higher top-k or temperature increase randomness and work against factual fidelity, and deleting source content removes the very evidence the model needs.

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Raise the top-k sampling value to let the model consider more candidate tokens.

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Shorten the press release by removing paragraphs before sending it to the model.

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Increase the model's temperature so it paraphrases the release more creatively.

Question 13

A global bank uses a Gemini model on Vertex AI to generate personalized investment summaries for clients in multiple regions. Compliance requires that the model never recommend products prohibited in a given region. The team wants a control that enforces these rules regardless of how the prompt is phrased. Which approach should they use?

Accepted Answer

Use a pre-call or post-call validation layer that checks the region against an allowed-product list and blocks violations.. Absolute compliance rules need deterministic enforcement rather than probabilistic guidance. A validation layer that evaluates the region against an allowed-product list before or after the model call blocks prohibited recommendations no matter how the request is phrased, and it produces an auditable record. System instructions, safety filters, and fine-tuning all influence behavior but cannot guarantee that a disallowed product is never surfaced.

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Configure safety filters and content moderation thresholds on the model endpoint.

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Fine-tune the model on examples of compliant and non-compliant recommendations.

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Add a system instruction listing prohibited products for each region.

Question 14

A research team uses a generative AI model to answer questions about internal technical documents. The model sometimes provides outdated information because it relies on its pretrained knowledge instead of the latest documents. The team wants the answers to be based on the current document set. Which technique should they implement?

Accepted Answer

Retrieval-augmented generation that fetches relevant passages from the current document set and includes them in the prompt.. Retrieval-augmented generation is the right technique because it dynamically fetches relevant passages from the current document set and includes them in the prompt. This grounds the model in up-to-date information without retraining, directly solving the outdated-answer problem. Other options either change randomness, require costly retraining, or rely on an instruction the model cannot fulfill without access to the documents.

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Fine-tune the model on the internal documents to update its knowledge.

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Increase the model's temperature so it relies less on memorized facts.

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Add a prompt instruction telling the model to always use the most recent information it knows.

Question 15

A financial services firm uses a Gemini model to generate quarterly risk summaries from internal reports. Reviewers note that summaries sometimes contradict the source tables. The team wants the model to reason step by step over the figures before writing the summary. Which technique should they use?

Accepted Answer

Use chain-of-thought prompting by instructing the model to compute and list intermediate values before producing the final summary.. Chain-of-thought prompting makes the model compute and compare intermediate values before writing, which is exactly what is needed to keep a summary consistent with source tables. Sampling changes and output limits affect randomness or length but not numerical reasoning. When a task requires multi-step reconciliation, prompting for explicit steps is the appropriate technique.

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Enable a higher top-p value so the model samples more broadly and can reconcile conflicting numbers.

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Set temperature to zero so the model always produces the same summary and cannot contradict the tables.

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Lower the maximum output tokens so the model is forced to be concise and avoid contradictory statements.

Question 16

A marketing team uses Gemini in Vertex AI to generate campaign taglines. The first drafts are generic and closely mirror the prompt wording. They want more distinctive, varied taglines from the same model without changing the model itself. Which parameter should they adjust?

Accepted Answer

Raise the temperature to increase randomness and diversity in token selection.. Temperature is the direct control over how sharply the model favors likely tokens. Raising it flattens the distribution and encourages less predictable word choices, which produces more distinctive taglines from the same model. Lowering temperature increases repetition, output token limits only change length, and top-p is a related but secondary truncation control rather than the primary diversity knob.

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Increase the maximum output tokens to allow longer taglines.

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Lower the temperature to make the output more deterministic.

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Enable a higher top-p value to consider more cumulative probability mass.

Question 17

A logistics company uses a generative AI model to draft incident reports from sensor logs. Reviewers find the reports are often incomplete, missing fields such as root cause and corrective action. The team wants to improve output completeness without retraining the model. Which TWO techniques should they use? (Choose two.)

Accepted Answer

Add few-shot examples of complete incident reports that show every required field filled in.. Completeness improves when the required output shape is made explicit and demonstrated. A structured schema enumerates every field so omissions become visible and correctable, while few-shot examples of complete reports show the model the expected coverage and detail. Sampling and length controls do not enforce required fields and can reduce or distort content.

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Shorten the input sensor logs to only the most recent readings.

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Increase the model's temperature to encourage the model to explore more content.

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Reduce the maximum output tokens to force the model to be concise.

Question 18

A financial services firm wants to deploy a generative AI assistant for employees. The assistant must not reveal any customer data and must comply with internal policies. Which Google Cloud capability should they use to control the assistant's responses based on defined safety and privacy rules?

Accepted Answer

Vertex AI safety filters and configurable thresholds in the generative AI API.. To enforce safety and privacy rules on generated content, the firm should use Vertex AI's safety filters and configurable thresholds. These settings allow blocking or adjusting responses that violate defined policies, providing a preventive control at the model output level. This directly supports compliance by reducing the chance of leaking customer data or violating internal rules.

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Cloud Audit Logs to record all API calls made to the assistant.

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Vertex AI Model Garden to browse and deploy open models.

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VPC Service Controls to create a perimeter around the project.

Question 19

A marketing team wants to generate product descriptions from a short bullet list of features. They need a Google Cloud service that provides a web-based console for writing prompts, comparing model responses, and adjusting parameters like temperature without writing code. Which service should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the Google Cloud environment built for prompt design and rapid experimentation with generative models. It provides a visual interface where users can enter prompts, compare outputs across models, and adjust sampling parameters. Because the marketing team wants to generate descriptions without coding, this console directly matches their workflow.

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Cloud Natural Language API

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Document AI

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BigQuery ML

Question 20

A marketing team wants to use a generative AI model to create blog post drafts from short product descriptions. They need the model to produce varied, creative text each time they run it. Which configuration parameter should they adjust to control the randomness of the output?

Accepted Answer

Temperature. Temperature is the parameter that scales the logits before softmax, directly controlling the randomness of the output distribution. Higher values flatten the distribution, making less likely tokens more probable, which increases creativity and variation. For generating diverse blog drafts, the team should increase the temperature setting.

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Top-K

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Top-P

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Max output tokens