Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 7 — 15 Questions

Question 1

What is the primary purpose of a system instruction in the Gemini API?

Accepted Answer

Define the overall behavior and constraints for the model. The system instruction in the Gemini API is the primary mechanism to define the overall behavior, persona, constraints, and guardrails for the model across all interactions. Unlike per-query parameters, it sets a persistent context that shapes how the model interprets every user prompt, ensuring consistent adherence to rules such as tone, format, or safety policies.

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Set the model's temperature and top_p

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Provide few-shot examples for each query

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Set the maximum output length

Question 2

Which TWO techniques are effective for reducing bias in generative AI model outputs?

Accepted Answer

Training on diverse and representative datasets. Training on diverse and representative datasets directly reduces sampling bias and coverage gaps in the training distribution, which are primary sources of stereotypical or skewed outputs. By ensuring the model sees balanced examples across demographics, contexts, and edge cases, it learns more equitable representations and reduces the likelihood of generating biased content.

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Increasing model size to learn more patterns

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Relying solely on post-hoc filters

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Limiting the model to only factual prompts

Question 3

Refer to the exhibit. A user wants formal translations from a generative AI model, but the model outputs informal style inconsistently. Which prompt engineering technique would best ensure consistent formal translations?

Accepted Answer

Provide a few-shot example with formal and informal pairs. Providing a few-shot example that explicitly demonstrates the desired formal translation guides the model to follow that pattern. System instructions can help but are less direct.

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Use context caching

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Use a longer system prompt with detailed rules

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Set top_k to 1

Question 4

A company is using a generative AI model to generate product descriptions. They notice the outputs often include factual inaccuracies about product specifications. Which technique would best address this issue without modifying the model's architecture?

Accepted Answer

Implement a Retrieval-Augmented Generation (RAG) pipeline that retrieves product specs from a database. Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's output in factual, up-to-date product specifications retrieved from an external database. This directly addresses factual inaccuracies without modifying the model's architecture, as the model generates text based on retrieved context rather than relying solely on its parametric knowledge.

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Decrease the temperature parameter to 0.1

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Increase the max output tokens to 1024

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Use few-shot prompting with 5 examples of correct descriptions

Question 5

A travel company fine-tuned a language model on customer chat logs to provide travel recommendations. After deployment, they receive complaints that the model sometimes generates inappropriate or offensive content. What is the most effective approach to improve output safety while preserving overall performance?

Accepted Answer

Add a post-processing safety classifier that filters or rewrites unsafe outputs. A post-processing safety classifier acts as a guardrail that can detect and filter or rewrite unsafe outputs without altering the underlying model's weights or training data. This approach preserves the model's overall performance on safe, relevant recommendations while adding a dedicated safety layer that can be independently tuned and updated as new safety requirements emerge. Unlike prompt engineering or hyperparameter adjustments, a classifier provides a robust, policy-enforced mechanism to catch edge cases that the model might otherwise generate.

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Modify the system instruction to request polite responses only

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Retrain the model on a larger dataset of chat logs

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Reduce the temperature to 0.0

Question 6

A research lab is fine-tuning a large language model on a small dataset of medical records. They observe that the model overfits, memorizing specific patient details and producing outputs that violate privacy regulations. Which technique should they apply to improve generalization and reduce memorization?

Accepted Answer

Apply differential privacy (DP-SGD) during fine-tuning. Differential privacy (DP-SGD) is the correct technique because it directly addresses memorization of sensitive patient data by adding calibrated noise to the gradient updates during fine-tuning. This bounds the model's ability to encode any single individual's information, improving generalization and ensuring compliance with privacy regulations like HIPAA.

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Increase the batch size to 64

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Increase the number of training epochs

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Use early stopping based on validation loss

Question 7

A company uses a text-to-image model to generate marketing visuals. The results often misinterpret the prompt, e.g., 'a red car' generates a blue car. Which technique should they try first to align the output with the prompt?

Accepted Answer

Refine the prompt with more adjectives and context, e.g., 'bright red sports car'. Refining the prompt with more adjectives and context directly addresses the root cause of misalignment: insufficient specificity in the text description. Text-to-image models rely on the semantic richness of the prompt to guide the latent diffusion process; adding 'bright red sports car' provides stronger conditioning signals that steer the model's cross-attention layers toward the intended color and object attributes. This is the most efficient first step before adjusting hyperparameters like guidance scale.

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Use a negative prompt to exclude blue

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Upscale the image resolution to 1024x1024

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Increase the guidance scale to 20

Question 8

A developer is tuning a text-generation model for creative writing. They want the outputs to be more diverse and less repetitive. Which THREE parameters/changes can help? (Choose three.)

Accepted Answer

Increase temperature to 0.9. Increasing temperature to 0.9 raises the randomness of the probability distribution over the vocabulary, making the model more likely to sample less probable tokens. This directly increases output diversity and reduces repetitiveness by flattening the softmax curve, which is a standard technique for creative generation.

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Reduce top-k to 10

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Reduce frequency penalty to 0.0

Question 9

A developer is using Vertex AI PaLM 2 to generate product descriptions. The output is often too verbose and includes irrelevant details. Which technique should the developer apply?

Accepted Answer

Use few-shot prompting with examples of concise descriptions. The developer needs to constrain the model's output to be concise and relevant. Few-shot prompting provides the model with explicit examples of the desired output format (concise descriptions), guiding it to mimic that style and length. This directly addresses verbosity and irrelevant details without altering the model's fundamental randomness or safety settings.

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Set top_p to 0.1

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Enable safety filters

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Increase temperature to 0.9

Question 10

A team deployed a fine-tuned model for code generation. After training, the model produces syntactically correct but functionally wrong code. What is the most likely cause?

Accepted Answer

Overfitting to training data. Overfitting to training data causes the model to memorize specific code patterns and syntax from the training set without learning the underlying logic or functional requirements. This results in syntactically correct outputs that fail to generalize to new, unseen coding tasks, producing functionally wrong code despite proper syntax.

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Incorrect prompt format

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Low temperature setting

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Insufficient training epochs

Question 11

A team uses Vertex AI Generative AI Studio to tune a model via RLHF. After tuning, the model outputs are bland. What likely went wrong?

Accepted Answer

Reward model overfits to generic responses. When the reward model overfits to generic responses, it assigns high rewards to safe, non-committal outputs, causing the RLHF-tuned model to converge toward bland, uninformative text. This happens because the reward model learns to prefer patterns that are statistically common in the training data rather than genuinely high-quality or diverse responses, directly leading to the 'bland' output described.

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Insufficient training data

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Too many training steps

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Low temperature during evaluation

Question 12

A team notices the RAG pipeline sometimes retrieves irrelevant documents. Which THREE improvements should they consider? (Choose three.)

Accepted Answer

Add a reranking step. Using a higher quality embedding model improves semantic understanding, adding a reranking step refines results, and reducing the number of retrieved documents reduces noise. Increasing chunk size can dilute relevance, and using exact keyword matching loses semantic context.

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Use exact keyword matching instead of embedding similarity

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Increase chunk size of documents

Question 13

Refer to the exhibit. A team's IAM policy for Vertex AI includes the following binding. They can deploy models but cannot create tuning jobs. Which statement is true?

Accepted Answer

The aiplatform.user role lacks permission to create tuning jobs. The roles/aiplatform.user role does not include permission to create tuning jobs (aiplatform.tuningJobs.create). The modelUser role does not override the user role, admin role is not needed, and specialist role doesn't exist.

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The developer needs the aiplatform.admin role

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The aiplatform.user role overrides the modelUser role

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The policy is missing the aiplatform.specialist role

Question 14

Refer to the exhibit. A data scientist sends a prediction request to a text generation model with the following parameters and receives repetitive output. Which parameter should be changed?

Accepted Answer

Increase temperature to 0.5. Temperature 0.0 makes the model deterministic, leading to repetitive text. Increasing temperature to 0.5 introduces randomness. Decreasing topP may help but temperature is the direct cause. Increasing topK adds diversity but less effect, decreasing max tokens doesn't fix repetition.

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Decrease topP to 0.5

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Increase topK to 100

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Decrease maxOutputTokens

Question 15

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?

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.