Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 6 — 15 Questions

Question 1

A developer deployed a large language model on Vertex AI for real-time chat. Users report slow response times. The model generates sentences one word at a time. Which optimization should be applied to reduce latency?

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

Accepted Answer

Use streaming responses to start output earlier.. Streaming responses allow the model to send tokens to the client as they are generated, rather than waiting for the full sequence to complete. This reduces perceived latency significantly in real-time chat, as users see the first word appear almost immediately, even though the total generation time remains similar.

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Batch multiple user queries together.

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Deploy the model with more accelerators.

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Enable prompt caching to reuse previous queries.

Question 2

A healthcare organization needs a generative AI model to answer medical questions using proprietary clinical guidelines. They have a large dataset of doctor-patient interactions. Should they fine-tune a pre-trained model or use Retrieval-Augmented Generation (RAG)?

Accepted Answer

Use RAG to retrieve relevant guidelines during inference, avoiding frequent retraining.. RAG is preferred because it retrieves the most current clinical guidelines from an external knowledge base during inference, avoiding the need for constant retraining when guidelines change. This is especially important in healthcare where regulations update frequently. Fine-tuning a pre-trained model on proprietary interactions might lead to overfitting or outdated knowledge. Option A is incorrect because RAG does not necessarily reduce inference costs; it adds overhead for retrieval. Option C is incorrect because prompt engineering cannot encode all proprietary guidelines; it is limited by context window size. Option D is incorrect because fine-tuning requires retraining when guidelines change, which is less flexible than RAG.

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Use RAG to reduce inference costs by skipping model updates.

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Use prompt engineering to encode all guidelines into the system prompt.

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Fine-tune the model on the clinical guidelines and interactions.

Question 3

A social media company uses a generative AI model to moderate user posts. The model occasionally allows offensive content. Which safety technique should be implemented?

Accepted Answer

Configure safety filters on the model endpoint in Vertex AI.. Configuring safety filters on the model endpoint in Vertex AI directly blocks offensive content at inference time by applying predefined or custom safety thresholds (e.g., toxicity, harassment categories). This is the most reliable technique for real-time moderation, as it prevents harmful outputs regardless of prompt engineering or tokenization changes.

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Use a different tokenizer to avoid offensive words.

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Add few-shot examples of safe posts in the prompt.

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Reduce the temperature to 0.

Question 4

A company uses a generative AI model to generate product descriptions. They notice variations in style and length across products. How can they enforce consistent formatting?

Accepted Answer

Set a system instruction specifying style and structure.. Setting a system instruction explicitly defines the desired style, tone, and structure for the model's output. This is a fundamental technique in prompt engineering, particularly with instruction-tuned models like GPT-4 or Claude, where the system message acts as a persistent directive that overrides the model's default behavior, ensuring consistent formatting across all generated product descriptions.

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Adjust top-k sampling to include more token candidates.

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Randomly select few-shot examples from a pool of descriptions.

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Use a high temperature and vary the prompt slightly.

Question 5

A streaming platform uses a large generative model for personalized content suggestions. Budget constraints require minimizing inference costs without significantly degrading quality. Which approach is most effective?

Accepted Answer

Use a distilled version of the model.. Distillation trains a smaller 'student' model to mimic a larger 'teacher' model, reducing parameter count and inference latency while retaining most of the recommendation quality. This directly addresses the budget constraint by lowering compute and memory costs per inference, making it the most effective approach among the options.

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Deploy the model on higher-end accelerators to save time.

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Implement stronger safety filters to reduce output length.

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Cache frequent prompts to avoid regeneration.

Question 6

A user provides a long document as context for a question-answering task, but the model outputs irrelevant answers. What is the most likely cause?

Accepted Answer

The document exceeds the model's context window, truncating important details.. The most common cause of irrelevant answers when a long document is provided is that the document exceeds the model's fixed context window (e.g., 8K tokens for PaLM 2, 128K for Gemini 1.0 Pro, or up to 1M for Gemini 1.5 Pro). When the input is truncated, critical details needed for accurate retrieval and generation are lost, leading to off-target responses. This is a fundamental limitation of transformer architectures, which cannot attend to tokens beyond their maximum sequence length.

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Safety filters are blocking the relevant response.

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The model's temperature is too low, making it deterministic.

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The model is not generating any tokens.

Question 7

A legal firm uses a generative AI to draft contracts. They want the output to follow a specific clause structure. Which technique should they use in the prompt?

Accepted Answer

Include a system instruction that defines the required format.. A system instruction (or system message) sets the overall behavior and output format for the generative AI model, effectively constraining it to follow a specific clause structure. This is the most direct and reliable technique for enforcing a predefined format in the prompt, as it operates at the model's instruction-following layer.

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Increase temperature to encourage variance.

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Use grounding to pull from a database of contracts.

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Set stop sequences to end generation at certain points.

Question 8

A data scientist fine-tunes a model on a small proprietary dataset. After fine-tuning, the model repeats training examples verbatim. What is the most effective mitigation?

Accepted Answer

Add regularization like dropout and use a smaller learning rate.. Overfitting on a small dataset causes the model to memorize training examples rather than generalize. Adding dropout introduces noise that forces the model to learn more robust features, while a smaller learning rate prevents the model from over-optimizing on the limited data. Together, these regularization techniques reduce the model's capacity to memorize verbatim outputs.

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Reduce the temperature during inference to 0.

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Train for more epochs to improve generalization.

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Use early stopping based on validation loss.

Question 9

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.. Option A, Retrieval-Augmented Generation (RAG) with curated datasets, is correct because RAG retrieves relevant, up-to-date passages from an external curated corpus at inference time and injects them into the context, so the model's answer is anchored to verifiable source content rather than relying solely on parametric memory, which directly reduces hallucination and improves factual accuracy. Option C, grounding with a trusted knowledge base, is correct because grounding constrains generation to authoritative, domain-validated sources (e.g., enterprise knowledge bases or vetted document stores), letting the model cite or align with established facts and thereby improving correctness and traceability. Option B is wrong because raising temperature to 1.5 increases sampling randomness and diversity, which makes outputs less deterministic and more prone to fabrication, not more factual. Option D is wrong because longer system prompts with many instructions can dilute attention, introduce conflicting directives, and do not supply new factual evidence, so they are unreliable for accuracy. Option E is wrong because fine-tuning on a large general-text corpus mainly adapts style and broad language patterns and can even reinforce outdated or incorrect parametric knowledge, whereas accuracy gains require targeted, high-quality, task-specific data or retrieval.

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 10

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.

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Raise the temperature to increase output variability.

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

Question 11

A team monitors their generative AI model on Vertex AI. They notice output quality declining. Which metric is most likely the root cause?

Accepted Answer

Input token count per request is increasing.. A is correct because an increasing input token count per request can degrade output quality by diluting the model's attention across a longer context window. In transformer-based models like those on Vertex AI, the attention mechanism has a fixed capacity; as input tokens grow, the model may lose focus on critical information, leading to less coherent or relevant outputs. This is a common issue in production systems where users gradually add more context without trimming irrelevant tokens.

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Output token count is decreasing.

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Prediction latency is stable.

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Error rate is less than 1%.

Question 12

A developer uses a generative AI model with the system instruction shown. The response is correct but very brief. Which parameter adjustment could encourage more detail without losing accuracy?

Accepted Answer

Add 'Provide a detailed response' to the system instruction.. Modifying the system instruction to explicitly request a detailed response directly influences the model's output behavior without altering its underlying probability distribution. This approach preserves accuracy by keeping temperature, topK, and other sampling parameters at their default values, ensuring the model remains faithful to the training data while simply prompting for more elaboration.

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Set temperature to 0 to make output deterministic.

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Set topK to 1 to focus on most likely tokens.

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Increase temperature to 1.5 to encourage creativity.

Question 13

A company is using Vertex AI to generate customer support summaries from chat logs. They notice that the summaries sometimes include irrelevant details from the conversation. Which technique should they use to reduce irrelevant details?

Accepted Answer

Add a system instruction to focus on key points.. Adding a system instruction to focus on key points is the most direct and effective technique for reducing irrelevant details in generated summaries. System instructions act as a persistent, high-level directive that guides the model's attention and output structure without altering the underlying model weights. This allows the model to filter out extraneous information from the chat logs by explicitly prioritizing key points, which is a standard practice in prompt engineering for Vertex AI.

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Use a higher top-k value.

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

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Increase the temperature parameter.

Question 14

A team is deploying a text generation model for legal document review. They observe that the model occasionally generates factually incorrect legal citations. Which approach best reduces this issue?

Accepted Answer

Implement retrieval-augmented generation (RAG) with a verified legal database.. Retrieval-augmented generation (RAG) with a verified legal database grounds the model in factual, up-to-date sources, directly addressing incorrect citations. Option B (lowering temperature) reduces randomness but does not prevent hallucination. Option C (using a larger model) may not guarantee correctness without proper grounding. Option D (increasing max tokens) has no effect on factual accuracy.

Answer

Lower the temperature to 0.0.

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Use a larger base model.

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Increase the max output tokens.

Question 15

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.

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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'.