Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 10 — 15 Questions

Question 1

A marketing team is using an LLM on Vertex AI to generate product descriptions. They want to consistently control the creativity and randomness of the output. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature is the primary parameter for controlling the randomness and creativity of generative AI output. Lower temperatures yield more predictable, focused responses, while higher temperatures yield more diverse and creative ones. For a marketing team seeking consistent control over creativity, temperature is the correct choice.

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Top-P

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Max output tokens

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Top-K

Question 2

A financial services firm is deploying a generative AI model to answer customer questions about investment products. They want to ensure the model's responses are compliant with regulations and do not provide personalized financial advice. Which TWO techniques can help achieve this? (Choose two.)

Accepted Answer

Fine-tune the model on a dataset of compliant customer interactions.. Combining a system prompt with explicit instructions and fine-tuning on compliant interactions provides both immediate guidance and long-term behavioral shaping. The system prompt ensures the model follows rules in real time, while fine-tuning helps the model internalize compliance patterns, making responses more reliably safe. Together, they address the need for regulatory adherence and avoidance of personalized advice.

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Deploy the model with a lower token limit to keep responses short and less detailed.

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Use a content filter to block any response that contains words like 'recommend' or 'should invest'.

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Increase the model's temperature to make responses more varied and less likely to give direct advice.

Question 3

A healthcare analytics team uses a Gemini model to generate patient-friendly discharge summaries from clinical notes. Clinicians report that summaries occasionally omit critical follow-up instructions. The team must improve recall of these instructions without retraining the model. Which two techniques should they apply? (Choose two.)

Accepted Answer

Add an explicit instruction to extract all follow-up actions into a mandatory checklist section of the summary.. Few-shot examples and an explicit mandatory checklist section both shape the model toward systematically extracting follow-up actions, improving recall without retraining. Temperature changes, token limits, and deterministic decoding affect randomness or length, not whether required clinical content is captured in the output.

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Reduce the maximum output tokens to force the model to be more concise and include only essentials.

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Increase the temperature so the model produces more varied summaries and captures more details.

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Enable deterministic decoding and assume the model will then always include every instruction.

Question 4

A marketing team uses a Gemini model to generate ad copy. They notice the outputs are repetitive and lack variety across multiple runs for the same prompt. They want more diverse creative options without sacrificing relevance. Which parameter adjustment should they make?

Accepted Answer

Increase the temperature to allow more varied token sampling.. Increasing temperature broadens token sampling, yielding more varied creative outputs while still following the prompt. Lower temperature, top-k of 1, and reduced max output tokens all make outputs more deterministic or shorter, not more diverse. Temperature is the primary control for balancing creativity and coherence in ad copy generation.

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Set top-k to 1 so the model always picks the single most likely token.

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Decrease the temperature to reduce repetition.

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Reduce the max output tokens to force the model to vary its wording.

Question 5

A healthcare company is using a generative AI model to draft patient education materials. The model sometimes generates content that includes specific medical advice, which could be harmful if inaccurate. The company wants to ensure that the model's outputs are safe and do not provide medical recommendations. Which technique should they implement?

Accepted Answer

Implement prompt engineering with explicit instructions to avoid providing medical advice.. Prompt engineering with explicit instructions is a direct and efficient way to constrain the model's output. By clearly stating that the model should not provide medical advice, the model is guided to generate only general educational content. This approach is flexible and can be updated as needed without retraining, making it suitable for the healthcare company's requirement to ensure safety.

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Apply a safety filter that blocks any output containing medical terminology.

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Reduce the model's temperature to make outputs more deterministic and less likely to include advice.

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Use reinforcement learning from human feedback (RLHF) to fine-tune the model to avoid giving advice.

Question 6

A financial analyst is using a large language model to generate executive summaries from lengthy earnings call transcripts. The summaries often miss key financial figures and include irrelevant details. Which technique should be used to improve the relevance and accuracy of the summaries?

Accepted Answer

Use few-shot prompting with examples of well-structured summaries.. Few-shot prompting is a powerful technique to guide the model's output by providing examples. In this scenario, showing the model examples of summaries that include key financial figures and exclude irrelevant details helps it learn the desired pattern, thereby improving relevance and accuracy.

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Increase the maximum output token limit.

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Fine-tune the model on a large corpus of general text.

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Increase the model's temperature setting.

Question 7

A product team at a retail company is using a foundation model on Vertex AI to generate short marketing taglines for new products. They find the outputs are often too long and sometimes include extra commentary. They want to constrain the model to produce only a single concise tagline. Which parameter should they adjust?

Accepted Answer

Max output tokens. The max output tokens parameter caps the total number of tokens generated, which directly enforces a limit on response length. By setting it appropriately, the team can ensure the model produces only a short tagline and cannot continue with extra commentary. Other parameters like temperature, top-k, and top-p affect randomness and diversity, not length.

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Temperature

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Top-p

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Top-k

Question 8

A customer support team uses a foundation model via the Gemini API to answer billing questions. Responses are often vague and sometimes omit required steps. The team wants to improve output quality without fine-tuning the model. Which approach should they use?

Accepted Answer

Use prompt engineering with few-shot examples and a clear output format that lists the required steps in order.. Prompt engineering with few-shot examples and an enforced output format is the fastest way to raise quality without changing the model weights. Examples demonstrate the expected depth, and a structured template guarantees that required steps appear. Sampling parameters alter randomness, and token limits alter length, but neither supplies missing procedural knowledge or enforces completeness.

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Lower the top-p value so the model only considers the most probable tokens, which should make the answers more accurate.

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Increase the maximum output tokens so the model has more room to produce a longer, more complete answer.

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Raise the temperature setting so the model can explore more possible answers and include more detail in its responses.

Question 9

A logistics company built a Gemini-powered assistant that answers driver questions about routes and hours-of-service rules. The assistant performs well on common questions but produces fabricated regulatory citations when asked about rare edge cases. The team has a curated set of correct answers for these edge cases and wants the model to adopt that behavior reliably. Which approach best fits?

Accepted Answer

Apply supervised fine-tuning on the curated edge-case examples to teach the desired response pattern.. Supervised fine-tuning uses the curated input-output pairs to adjust the model's weights so it reliably reproduces correct edge-case answers, which is the intended use of labeled examples. Prompt stuffing, sampling controls, and larger context windows do not durably change behavior for rare inputs.

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Lower the topP value so the model only considers the most likely tokens and avoids fabricating citations.

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Add the curated answers to the system instruction and rely on the model to generalize.

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Increase the model's context window by switching to a long-context variant and pasting the full regulations.

Question 10

A team is building a customer support assistant on Vertex AI using a foundation model. They notice the model occasionally invents policy details that don't exist in their internal documentation. They want the model to ground its answers in the company's knowledge base and provide citations. Which approach should they use?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with Vertex AI Search to retrieve relevant documents and include them in the prompt.. Retrieval-Augmented Generation retrieves relevant passages from an authoritative knowledge base and supplies them as context, so the model's answer is conditioned on real policy text rather than parametric memory. With Vertex AI Search, the system can also surface citations, letting users verify sources. This combination directly reduces fabricated policy details while preserving natural language fluency.

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

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Increase the temperature parameter to encourage more creative responses.

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Fine-tune the model on a dataset of general customer service conversations.

Question 11

A healthcare organization is using a generative AI model to summarize patient discharge instructions. They need to ensure the summaries are accurate and do not omit critical information. Which technique should they implement to reduce the risk of omissions?

Accepted Answer

Implement a retrieval-augmented generation (RAG) system that retrieves relevant sections of the discharge instructions and includes them in the prompt.. Retrieval-augmented generation (RAG) is the best choice because it grounds the model in the actual discharge instructions, reducing the chance of omissions. By retrieving and including relevant text in the prompt, the model can generate summaries that are comprehensive and accurate. Other techniques like fine-tuning or chain-of-thought do not directly ensure that all critical information from the source is captured.

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Increase the model's token limit to allow longer summaries that can include more details.

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Fine-tune the model on a large dataset of medical summaries to improve its ability to capture key points.

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Use chain-of-thought prompting to encourage the model to reason step by step.

Question 12

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.

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

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

Question 13

A marketing team uses a generative AI model to create short social media posts from long product briefs. The posts frequently include made-up statistics and product claims that are not in the brief. The team wants to reduce these unsupported statements without retraining the model. Which technique should they apply first?

Accepted Answer

Add grounding instructions that require the model to use only facts from the provided brief and to say when information is missing.. Grounding the prompt in the provided brief is the most direct way to stop unsupported statistics and claims. It tells the model exactly which facts are allowed and how to respond when a detail is missing. This approach avoids retraining costs and can be applied immediately, making it the right first step for the marketing team.

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Reduce the maximum output token limit so the model cannot include extra details.

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

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Fine-tune the model on a dataset of approved social media posts.

Question 14

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.)

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 15

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)