Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 8 — 15 Questions

Question 1

Which TWO techniques are most effective for improving factual accuracy in a generative AI model's responses? (Choose two.)

Accepted Answer

Retrieval-Augmented Generation (RAG) with curated datasets.. Grounding and RAG both provide external authoritative sources to enhance factual accuracy. Fine-tuning on general data doesn't guarantee accuracy, and increasing temperature hurts accuracy. Prompt engineering is helpful but not as robust as retrieval-based methods.

Answer

Increasing the model's temperature to 1.5.

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Using longer system prompts with multiple instructions.

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Fine-tuning on a large corpus of general text.

Question 2

Which TWO techniques can help reduce latency for a real-time generative AI application? (Choose two.)

Accepted Answer

Use streaming responses to send tokens as generated.. Streaming and model quantization directly reduce response time. Batching is for offline, and more deploy replicas can increase throughput but not necessarily reduce latency for a single request. Prompt caching can help if prompts repeat, but not generally.

Answer

Deploy more model replicas to handle load.

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Enable prompt caching for repeated queries.

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Batch multiple user requests together.

Question 3

Which THREE strategies should be combined to effectively reduce biased outputs in a generative AI model? (Choose three.)

Accepted Answer

Implement safety filters targeting hate speech and stereotypes.. Implementing safety filters targeting hate speech and stereotypes directly blocks the generation of biased or harmful content at the output layer. These filters use predefined rule sets or trained classifiers to detect and suppress language that reflects demographic or cultural biases, reducing the risk of the model producing offensive or stereotypical responses.

Answer

Raise the temperature to increase output variability.

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Fine-tune the model on a biased dataset to learn patterns.

Question 4

An enterprise uses a fine-tuned PaLM 2 model for code generation. They want to ensure the generated code passes security audits. Which combination of techniques would be most effective?

Accepted Answer

Integrate a static analysis tool in the pipeline and add a safety filter to reject code containing dangerous functions.. Integrating a static analysis tool (e.g., SonarQube, Checkmarx) into the pipeline provides automated, rule-based scanning for security vulnerabilities like SQL injection or buffer overflows, while a safety filter explicitly blocks generated code containing dangerous functions (e.g., eval(), exec()). This combination creates a defense-in-depth approach that catches both known vulnerability patterns and explicitly prohibited operations, which is essential for passing security audits.

Answer

Use a few-shot prompt with examples of secure code and set temperature to 1.0.

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Fine-tune the model on a dataset of insecure code and use top-p=0.9.

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Increase the model's context window and use a system instruction to 'be secure'.

Question 5

A development team is integrating a large language model into a healthcare application. They need to reduce the risk of generating harmful medical advice. Which THREE measures should they implement? (Choose three.)

Accepted Answer

Use a safety filter to block outputs containing harmful medical terminology.. Implementing a safety filter that blocks outputs containing harmful medical terminology directly mitigates the risk of generating dangerous advice. This acts as a post-processing guardrail, intercepting model outputs that include terms associated with diagnoses, dosages, or procedures that could lead to patient harm. It is a standard practice in high-stakes domains to layer such filters on top of the generative model.

Answer

Include a disclaimer in the system instruction that the model is not a doctor.

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Set the temperature to a very high value to ensure diverse outputs.

Question 6

A developer is building a customer support chatbot using a large language model. The chatbot frequently generates plausible-sounding but incorrect answers to product questions. Which technique should be applied to improve factual accuracy?

Accepted Answer

Enable Grounding with the company's product knowledge base.. Grounding (e.g., using Vertex AI Grounding with Search) retrieves relevant information from a trusted source in real time, reducing hallucination. Option A is wrong because few-shot prompting can help but only if examples are accurate and relevant; it does not dynamically look up facts. Option B is wrong because higher temperature increases randomness, worsening hallucinations. Option C is wrong because increasing context length may include more irrelevant information and does not guarantee accuracy.

Answer

Provide a few-shot example of correct answers in the prompt.

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Use a higher temperature setting to encourage more creative responses.

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Increase the model's context length to include more of the conversation history.

Question 7

A team uses a generative model to summarize lengthy legal documents. The summaries are accurate but often exceed the target length of 200 words, varying widely. Which simple adjustment should be applied to ensure consistent output length?

Accepted Answer

Set the max output tokens parameter to 200.. Setting the max output tokens parameter to 200 directly caps the number of tokens the model can generate, ensuring the summary cannot exceed the target length. This is a deterministic, model-level constraint that works regardless of prompt phrasing or training data, making it the most reliable adjustment for consistent output length.

Answer

Fine-tune the model on summaries that are exactly 200 words.

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Add a system prompt that says 'Summarize in exactly 200 words.'

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Lower the temperature to reduce variability in word choices.

Question 8

A content generation model for e-commerce product descriptions repeats the same phrases across multiple descriptions (e.g., 'high-quality', 'best-in-class'). The team wants more varied and engaging output. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty parameter to 1.0.. Increasing the frequency penalty to 1.0 penalizes tokens that have already appeared in the generated text, directly reducing repetition of phrases like 'high-quality' and 'best-in-class'. This encourages the model to use more diverse vocabulary and sentence structures, leading to varied and engaging product descriptions.

Answer

Decrease the max output tokens to 50.

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Increase the temperature parameter to 1.5.

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Set the top-p value to a very small number like 0.1.

Question 9

Which TWO methods are most effective for improving factual accuracy in a language model's responses? (Choose two.)

Accepted Answer

Use prompt engineering to instruct the model to rely on provided facts.. Prompt engineering can explicitly instruct the model to base its responses on provided facts, reducing reliance on parametric knowledge that may be outdated or incorrect. By including directives like 'Use only the information in the following text' or 'Answer based solely on the provided context,' the model is guided to prioritize given facts over its internal training data, which improves factual accuracy in the output.

Answer

Decrease the temperature to make responses more deterministic.

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Increase top-k sampling to consider a wider range of tokens.

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Replace the model with a smaller, more focused model.

Question 10

A healthcare startup uses a generative model fine-tuned on general medical literature to provide preliminary diagnostic suggestions from patient text. The model frequently misses rare diseases and sometimes suggests common conditions that are unlikely given the symptoms. The startup has a curated dataset of rare disease case reports and wants to improve the model’s sensitivity to rare conditions without sacrificing overall accuracy. They cannot afford to retrain the entire model from scratch. The model is deployed on Vertex AI Prediction with low latency requirement. Which approach should they take?

Accepted Answer

Implement a human-in-the-loop system: for outputs with low confidence or suspected rare disease, route to a human expert.. Implementing a human-in-the-loop process for rare disease flags combines AI with expert review, catching misses while maintaining speed for common cases. Option A is wrong because prompt engineering alone may not teach the model about rare diseases. Option B is wrong because increasing top-p restricts vocabulary but doesn't inject knowledge. Option C is wrong because fine-tuning again might cause catastrophic forgetting of common conditions.

Answer

Perform continued fine-tuning on the rare disease dataset using a low learning rate.

Answer

Add a system prompt instructing the model to consider rare diseases more carefully.

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Reduce top-p sampling to focus on high-probability tokens, assuming rare diseases have lower probability.

Question 11

A healthcare startup has fine-tuned a Vertex AI PaLM 2 model on a dataset of medical records to generate patient summaries. The model produces fluent text but occasionally fabricates diagnoses not present in the input. The team has already tried increasing the training data size by 20% and adjusting the temperature from 0.7 to 0.2, but hallucinations persist. The summaries must be factually accurate for regulatory compliance. What should the team do next?

Accepted Answer

Implement a RAG pipeline using Vertex AI Search to retrieve relevant medical documents before generation.. Implementing a Retrieval-Augmented Generation (RAG) pipeline with Vertex AI Search grounds the model's output in retrieved, authoritative medical documents. This directly addresses the root cause of hallucination—the model's reliance on its parametric memory—by providing factual context at inference time, which is far more effective for regulatory compliance than adjusting generation parameters or training data size alone.

Answer

Increase the maximum output tokens to allow the model to generate more detailed summaries.

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Add more few-shot examples to the prompt for each generation.

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Switch the base model to Gemini 1.5 Pro without additional changes.

Question 12

A financial services firm is using a foundation model on Vertex AI to generate investment summaries from quarterly reports. The summaries are accurate but often miss key financial metrics and trends. The team cannot afford to fine-tune the model frequently. Which technique should they use to improve the completeness and relevance of the summaries without modifying the model?

Accepted Answer

Provide three few-shot examples in the prompt that highlight the desired metrics.. Few-shot prompting provides the model with concrete examples of desired output structure and content, guiding it to include key financial metrics and trends without retraining. This technique leverages in-context learning, where the model generalizes from the examples in the prompt to produce more complete and relevant summaries, while avoiding the cost and latency of fine-tuning.

Answer

Increase temperature to 0.9 to encourage more creative outputs.

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Set stop sequences to ['

'] to ensure the model finishes each paragraph.

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Lower top_p to 0.5 to reduce the sampling pool.

Question 13

An AI team is building a customer support chatbot for a telecom company using a fine-tuned LLM on Vertex AI. The model performs well on common issues but fails to answer correctly for rare or novel problems, often providing plausible-sounding but incorrect solutions. The team has a large corpus of internal troubleshooting documents. They want to minimize incorrect answers while keeping latency low. Which approach should they take?

Accepted Answer

Implement a retrieval-augmented generation (RAG) pipeline using Vertex AI Search to fetch relevant documents before generating answers.. Implementing a RAG pipeline with Vertex AI Search allows the chatbot to retrieve relevant troubleshooting documents from the internal corpus in real-time, grounding the LLM's responses in authoritative sources. This approach directly addresses the problem of plausible-sounding but incorrect answers for rare/novel issues without requiring retraining, and it keeps latency low by fetching only the most relevant documents before generation.

Answer

Switch to a larger base model (e.g., Gemini Ultra) without any retrieval.

Answer

Collect more data on rare issues and continue fine-tuning the model weekly.

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Use a few-shot prompt with 10 examples of rare problems and solutions.

Question 14

A developer is using the Gemini API to generate creative product taglines. The taglines are often bland and uncreative. The developer wants more variety and novelty in the outputs. Which parameter adjustment would most effectively increase the diversity of the generated taglines?

Accepted Answer

Increase temperature from 0.2 to 0.9.. Increasing temperature from 0.2 to 0.9 raises the randomness of token sampling, which directly increases the diversity and novelty of generated text. A low temperature (e.g., 0.2) makes the model highly deterministic, always picking the most probable next token, leading to bland outputs. A higher temperature (e.g., 0.9) allows less probable tokens to be selected more often, producing more creative and varied taglines.

Answer

Decrease top_p from 1.0 to 0.5.

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Set frequency_penalty to 2.0.

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Decrease temperature from 0.7 to 0.2.

Question 15

A company uses a text generation model for customer support but notices it occasionally provides outdated information. Which technique should they implement to improve output accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG). Retrieval-augmented generation (RAG) is the correct technique because it grounds the model's output in real-time, external knowledge sources (e.g., a vector database or document index) rather than relying solely on static training data. This directly addresses the problem of outdated information by allowing the model to retrieve and synthesize current facts at inference time, ensuring accuracy without requiring retraining.

Answer

Increase max output tokens

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Fine-tune the model with more historical support data

Answer

Increase model temperature to 1.0