Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 3 — 15 Questions

Question 1

Refer to the exhibit. A team runs 'gcloud ai models list --filter=displayName:qa-chat-v1' and sees the output. The model was tuned using supervised fine-tuning (SFT) but shows 'state: DEPLOYING' for days. What is the most likely issue?

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

Accepted Answer

The model is stuck in deployment due to insufficient quota. The model shows 'state: DEPLOYING' for days, which indicates the deployment process is stuck rather than failing with an error. In Vertex AI, a model stuck in DEPLOYING state for an extended period is typically caused by insufficient quota for the selected machine type (e.g., GPU or TPU accelerators). The deployment process cannot complete because the requested resources are not available, causing it to hang indefinitely.

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The evaluation metrics are missing, causing deployment to hang

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The training pipeline failed silently

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The model has no errors, so it is fine

Question 2

A company uses a generative AI model to answer customer queries. The model sometimes returns outdated information. Which technique should they apply to ensure responses rely on current data?

Accepted Answer

Use grounding with a refreshed knowledge base.. Grounding with a refreshed knowledge base is the correct technique because it directly connects the generative AI model to an external, up-to-date data source at inference time. This ensures responses are based on current information without retraining the model, addressing the problem of outdated outputs by retrieving fresh data from a vector database or API in real time.

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Fine-tune the model on historical data.

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Extend the context window to include more tokens.

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Increase the model's temperature to encourage novelty.

Question 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

A team configures a Vertex AI prediction request as shown. Users report that the model sometimes produces incoherent or off-topic responses despite moderate settings. What is the most likely cause?

Accepted Answer

The temperature is too high for coherent responses.. A is correct because temperature controls the randomness of token selection; a high temperature (e.g., >0.8) increases the probability of sampling low-probability tokens, leading to incoherent or off-topic responses even with moderate settings. Vertex AI's default temperature is 0.0 for deterministic output, and raising it without careful tuning often causes semantic drift.

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The maxOutputTokens is too low.

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The safety threshold blocks too much content.

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The topK value is too low.

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.

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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

A data scientist is using a large language model to generate product descriptions. The descriptions are often too verbose. Which parameter adjustment is most appropriate?

Accepted Answer

Increase the frequency penalty.. Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases or ideas, which directly addresses verbosity by discouraging repetitive or overly detailed descriptions. This parameter penalizes tokens that have already appeared in the generated text, promoting more concise and varied output. Other adjustments like temperature or top-k affect randomness and diversity but do not specifically target repetition or length.

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Decrease the top-k value.

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Increase the max output tokens.

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Decrease the temperature.