Generative AI Leader Techniques to Improve Generative AI Model Output Practice Test 4 — 15 Questions

Question 1

To ensure that a generative AI model uses the most current information from the web for answering user queries, which Vertex AI feature should be enabled?

Accepted Answer

Grounding with Google Search. Grounding with Google Search is the correct feature because it enables the model to retrieve and reference real-time information from the web, ensuring responses are based on the most current data available. This is achieved by integrating Google Search results directly into the model's generation process, allowing it to cite live sources and reduce hallucinations from outdated training data.

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Safety filters

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Context caching

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Model tuning

Question 2

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 3

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 4

Which THREE are best practices for designing prompts for a generative AI model?

Accepted Answer

Provide few-shot examples for complex tasks. Providing few-shot examples (e.g., 2-5 input-output pairs) helps the model infer the desired pattern, reducing ambiguity for complex tasks like classification or structured extraction. This technique leverages in-context learning, where the model uses the examples as a template without fine-tuning.

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Use negative prompts to avoid undesired outputs

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Always set temperature to 1.0 for creativity

Question 5

Which TWO are advantages of using Retrieval-Augmented Generation (RAG) over fine-tuning?

Accepted Answer

No need to retrain the base model. RAG retrieves relevant external knowledge at inference time without modifying the base model's parameters, eliminating the need for retraining (option A). This contrasts with fine-tuning, which requires updating model weights through additional training cycles. Because the knowledge lives in an external, updatable index rather than in the model weights, RAG is also better suited for rapidly changing knowledge bases (option E): documents can be added, updated, or removed without any retraining, whereas fine-tuning would require a new training run each time the knowledge changes. RAG thus preserves the original model while augmenting its output with up-to-date information.

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Requires less data preparation

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Lower inference latency

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More secure because model weights are not modified

Question 6

Refer to the exhibit. A team attempted to start a model tuning job but received the error 'Quota limit exceeded for tuning jobs in region us-central1'. What is the most appropriate action?

Accepted Answer

Request a quota increase for tuning jobs in us-central1. The error 'Quota limit exceeded for tuning jobs in region us-central1' indicates that the project has reached its predefined resource quota for model tuning operations in that specific region. The most appropriate action is to request a quota increase from Google Cloud, as this directly resolves the capacity limitation without altering the job's configuration or data. Quotas are per-region limits enforced by the AI Platform to ensure fair resource allocation, and increasing the quota is the standard procedure when legitimate tuning needs exceed the default allowance.

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Change the region to us-west1 and retry

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Reduce the size of the training data

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Use a different base model

Question 7

Refer to the exhibit. A Vertex AI endpoint configured with the above deployment is returning HTTP 429 (Too Many Requests) errors during peak traffic. The current CPU utilization reaches 80% consistently. What should the team adjust to resolve this?

Accepted Answer

Increase maxReplicaCount to 10. Increasing maxReplicaCount to 10 allows the Vertex AI endpoint to scale out to more instances during peak traffic, distributing the load and reducing HTTP 429 errors. Since CPU utilization is at 80%, the current maxReplicaCount is insufficient to handle the demand, and raising this limit enables the horizontal pod autoscaler to add replicas up to the new maximum, directly addressing the capacity bottleneck.

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Increase scaleTarget to 0.9

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Change machineType to n1-highmem-2

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Increase minReplicaCount to 2

Question 8

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. Few-shot prompting provides the model with concrete input/output examples that demonstrate the desired style, which is far more effective at controlling tone and register than abstract instructions. By including formal and informal pairs, the model can infer the transformation pattern and apply it consistently to new translations. This anchors the model's behavior through in-context learning rather than relying on the model to interpret vague stylistic rules.

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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 9

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 10

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 11

A developer is using the Gemini API to generate code snippets. They notice the outputs often contain deprecated API calls. Which parameter adjustment or prompt strategy would most effectively encourage the model to use current APIs?

Accepted Answer

Add a system instruction specifying 'Use the most recent API version and avoid deprecated functions.'. Adding a system instruction that explicitly directs the model to 'Use the most recent API version and avoid deprecated functions' directly influences the model's behavior at the prompt level. The Gemini API supports system instructions that act as persistent, high-level guidance, steering the model toward preferred output patterns—in this case, avoiding deprecated API calls. This is the most effective and direct method to enforce current API usage without altering sampling parameters or relying on limited examples.

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Set top-p to 0.5 to reduce output diversity

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Provide one few-shot example of a correct API call

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Set temperature to 1.5 to increase creativity

Question 12

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 13

An e-commerce company fine-tunes a model on customer reviews to generate product feedback summaries. They want to ensure the model does not reproduce toxic language from the training data. Besides filtering the training data, which additional technique is most effective at inference time?

Accepted Answer

Pass the model output through a toxicity detection model and conditionally regenerate or block. It directly addresses the safety requirement at inference time by introducing a secondary guardrail. A toxicity detection model (e.g., a classifier trained on the Jigsaw Toxic Comment dataset) can score the generated output in real time; if the score exceeds a threshold, the system can either block the response or trigger a regeneration with adjusted parameters. This is the only technique that actively filters for toxic language after generation, rather than merely reducing output variance or exploring alternative sequences.

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Set temperature to 0.0 to reduce variance

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Set top-k to 10 to limit token choices

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Use beam search with a high beam width

Question 14

A data scientist is using Vertex AI generative AI studio to create a chatbot. The chatbot gives inconsistent answers to similar questions. Which parameter should they adjust to make responses more consistent?

Accepted Answer

Decrease temperature to 0.2. Decreasing the temperature to 0.2 reduces the randomness of the model's token sampling, making the output more deterministic and consistent. Temperature controls the probability distribution over tokens; lower values make the model more likely to choose the highest-probability token, reducing variability in responses to similar questions.

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Increase top-p to 0.9

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Increase presence penalty to 0.5

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Decrease frequency penalty to 0.0

Question 15

A developer uses the Gemini API to summarize long articles. The summaries often miss key points from the end of the article. Which technique specifically addresses this length-based loss of information?

Accepted Answer

Break the article into sections and ask the model to summarize each section, then combine. The Gemini API, like many LLMs, has a limited context window and exhibits a 'lost-in-the-middle' effect where information at the beginning and end of long inputs is retained better, but the middle and far end can be dropped. By breaking the article into sections, summarizing each independently, and then combining those summaries, you ensure that key points from the end are captured in their own focused summary, bypassing the length-based information loss. This technique is a form of 'chunking' and 'recursive summarization' that directly addresses the model's tendency to lose context over long sequences.

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Increase the max output tokens to 2048

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Truncate the article to the first 2000 tokens

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Use a different model with a larger context window