Generative AI Leader Fundamentals of Generative AI Practice Test 2 — 15 Questions

Question 1

A company is using Vertex AI to generate personalized marketing emails. The model sometimes produces biased content. What is the most effective way to detect and mitigate bias?

Accepted Answer

Use Vertex AI Explainable AI to analyze predictions and detect bias in training data. Vertex AI Explainable AI provides feature attributions and model explanations that help identify which input features (e.g., demographic attributes, phrasing patterns) contribute most to biased outputs. By analyzing these attributions against training data, you can pinpoint and mitigate bias at the source, rather than relying on post-hoc manual review or model swapping.

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Add more diverse training data

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Manually review all generated emails before sending

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Switch to a different generative model

Question 2

Which TWO options are best practices for deploying generative AI models on Vertex AI? (Choose two.)

Accepted Answer

Enable automatic scaling to handle variable traffic. Option B is correct because Vertex AI's automatic scaling dynamically adjusts the number of serving instances based on incoming traffic, ensuring low latency during spikes and cost efficiency during lulls. This is a best practice for production workloads where traffic patterns are unpredictable, as it eliminates the need for manual capacity planning.

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Disable logging to reduce cost

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Manually scale instances based on expected load

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Serve the model directly without optimization

Question 3

Which THREE factors should be considered when choosing between fine-tuning and prompt engineering for a generative AI task? (Choose three.)

Accepted Answer

Availability of labeled training data. Option A is correct because fine-tuning requires a labeled dataset specific to the target task to adjust model weights via supervised learning, whereas prompt engineering relies on the model's existing knowledge without additional training data. Without sufficient labeled data, prompt engineering is often the only viable approach, as fine-tuning would risk overfitting or poor generalization.

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Cost of API calls per request

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Size of the base model

Question 4

A data scientist sees the above error when trying to deploy a model to an endpoint. What is the most likely cause?

Accepted Answer

The model import into Vertex AI Model Registry is still in progress. The error indicates that the model is not yet fully imported into the Vertex AI Model Registry. Deploying a model to an endpoint requires the model resource to be in an 'ACTIVE' state; if the import is still in progress, the deployment request will fail. This is a common timing issue when a model is uploaded but not yet registered.

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The IAM permissions are insufficient

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The endpoint does not exist

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The model is already deployed to another endpoint

Question 5

A developer receives the above JSON response from a Vertex AI PaLM API call for a medical advice application. What should the developer be most concerned about?

Accepted Answer

The output falls under the 'health' category, which may require compliance with regulations. Option C is correct because the JSON response includes a 'category' field with the value 'health', which triggers stringent regulatory compliance requirements such as HIPAA in the US or GDPR in Europe. For a medical advice application, the developer must ensure data handling, model transparency, and output validation meet these legal standards, as failure could result in severe penalties. The PaLM API's safety attributes and category labels are designed to flag such sensitive domains, making compliance the primary concern over other technical issues.

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The safety score is very low (0.01)

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The deployed model ID is not recognized

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The prediction content is incorrect

Question 6

You are an ML engineer at a retail company. You have deployed a generative AI model on Vertex AI to generate product descriptions. The model uses a custom container and is deployed to a single endpoint. Recently, you noticed that inference latency has increased significantly during peak hours, causing timeouts. You have checked the logs and found that the CPU utilization on the deployed instances is consistently above 90% during peak hours. The model is currently deployed with a single machine type (n1-standard-4) and no scaling. You need to reduce latency without incurring excessive cost. What should you do?

Accepted Answer

Change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5. Option C is correct because upgrading to a larger machine (n1-standard-8) provides more CPU cores to handle the increased inference workload, while enabling autoscaling (min=1, max=5) allows the deployment to dynamically add replicas during peak hours to distribute the load and reduce latency. This combination addresses the high CPU utilization without over-provisioning during off-peak times, thus controlling cost.

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Optimize the model using quantization and reduce the number of replicas

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Switch to batch prediction instead of online prediction

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Add a GPU accelerator to the existing machine

Question 7

You are a data scientist at a financial institution. You are using Vertex AI to fine-tune a large language model (LLM) for generating financial reports. You have prepared a dataset of 10,000 examples. During fine-tuning, you notice that the training loss is decreasing steadily, but the validation loss is increasing after 5 epochs. The model's generated reports on the validation set contain many factual errors and nonsensical statements. You suspect overfitting. You have limited compute budget and need to improve generalization. What should you do?

Accepted Answer

Implement early stopping with a patience of 2 epochs. Early stopping with a patience of 2 epochs is the correct approach because it directly addresses overfitting by halting training when the validation loss fails to improve for a specified number of epochs. This preserves the model's generalization ability without requiring additional compute or data, which aligns with the limited budget constraint. In Vertex AI, early stopping is a built-in hyperparameter tuning strategy that monitors validation metrics and stops the job to prevent further degradation.

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Increase the learning rate

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Increase the number of training epochs to 20

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Add more training examples from a public dataset

Question 8

A retail company is building a generative AI chatbot to assist customers with product recommendations and order tracking. The chatbot uses Vertex AI with Gemini 1.5 Pro, and the development team has implemented a Retrieval-Augmented Generation (RAG) pipeline using Vertex AI Search for grounding. The pipeline uses a vector store containing product descriptions and order history. During testing, the team observes that the chatbot sometimes provides incorrect order statuses—for example, claiming an order is 'shipped' when it is actually 'pending'. The team suspects the issue is related to how context is retrieved and used. The RAG pipeline currently retrieves the top 5 chunks based on cosine similarity from the vector store, and passes them as context to the model. The team is considering several changes to improve factual accuracy. Which single action would most effectively reduce hallucinations in this scenario?

Accepted Answer

Increase the similarity score threshold for retrieval to 0.85 to filter out less relevant chunks.. Option C is correct because increasing the similarity score threshold to 0.85 ensures that only highly relevant chunks are passed to the Gemini 1.5 Pro model, directly reducing the risk of the model generating responses based on irrelevant or low-confidence context. In a RAG pipeline using Vertex AI Search, low-similarity chunks can contain order statuses from different customers or products, leading to hallucinations like incorrect order statuses. Filtering out these less relevant chunks improves the factual grounding of the model's output.

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Switch from Vertex AI Search to a different vector database like Pinecone.

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Reduce the model temperature to 0.0 to make outputs more deterministic.

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Increase the top-K retrieval value to 10 to provide more context to the model.

Question 9

A marketing team wants to generate product descriptions using generative AI. They need to ensure factual accuracy and avoid hallucinations. Which approach should they use?

Accepted Answer

Implement a retrieval augmented generation (RAG) system that retrieves product facts from a database.. Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's output in verifiable, external data sources. By retrieving product facts from a database in real-time, the system ensures that the generated descriptions are based on accurate information, directly mitigating the risk of hallucination. This method combines the generative power of an LLM with a retrieval step that provides factual context, making it ideal for applications where precision is critical.

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Use a code generation model to generate structured descriptions.

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Fine-tune the model on all product descriptions using supervised learning.

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Use a large language model with detailed prompt instructions to be accurate.

Question 10

A company fine-tunes a text model on internal HR policies. After deployment, the model sometimes outputs sensitive employee information. What is the most likely cause?

Accepted Answer

The fine-tuning dataset contained personally identifiable information that was not removed.. The most likely cause is that the fine-tuning dataset contained personally identifiable information (PII) that was not properly scrubbed. During fine-tuning, the model learns patterns and memorizes specific sequences from the training data. If the dataset includes sensitive employee records, the model can reproduce that information verbatim when prompted, leading to data leakage. This is a well-known risk in fine-tuning, as models can overfit to rare or unique examples in the training set.

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The model was not trained with reinforcement learning from human feedback (RLHF).

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The model has insufficient parameters to generalize properly.

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The prompt engineering was too verbose and included misleading instructions.

Question 11

A developer uses the Vertex AI Python SDK to call a Gemini model for structured JSON output. However, the model often returns malformed JSON. Which parameter should the developer set in the generation configuration to enforce valid JSON output?

Accepted Answer

Set the 'response_mime_type' parameter to 'application/json'.. Option B is correct because setting `response_mime_type` to `'application/json'` in the generation configuration instructs the Gemini API to constrain the model's output to valid JSON format. This parameter leverages the model's native structured output capability, ensuring the response adheres to JSON syntax without relying on post-processing or prompt engineering.

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Set the temperature to a lower value (0.1) to reduce variation.

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Include few-shot examples of the desired JSON format in the system prompt.

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Switch to a smaller model to reduce complexity.

Question 12

A graphic design company wants to generate high-quality synthetic images for product mockups. Which Google Cloud generative AI service is most suitable?

Accepted Answer

Imagen on Vertex AI. Imagen on Vertex AI is the correct choice because it is Google Cloud's state-of-the-art text-to-image diffusion model specifically designed to generate high-quality, photorealistic synthetic images from natural language prompts. This directly meets the requirement for creating product mockups, as Imagen can produce custom visuals with fine-grained control over style and composition, and it integrates seamlessly with Vertex AI for deployment and management.

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AutoML Vision

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Codey APIs for code generation

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Natural Language API

Question 13

A data scientist fine-tunes a foundation model on customer support transcripts. After evaluation, the model's responses are too formal. Which adjustment during fine-tuning is most likely to make responses more conversational?

Accepted Answer

Include examples of informal customer interactions in the fine-tuning data.. The training data directly influences the tone and style of model outputs. Including examples of informal conversations in the fine-tuning dataset teaches the model the desired conversational tone. Other options affect training dynamics but not the style.

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Increase the batch size to stabilize training.

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Decrease the number of fine-tuning steps to prevent overfitting.

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Use a higher learning rate for faster adaptation.

Question 14

An organization uses a fine-tuned model for medical diagnosis and must comply with HIPAA. Which measure is essential when deploying the model on Vertex AI?

Accepted Answer

Use a private Google Cloud Access and disable external internet access for the endpoint.. Option D is correct because HIPAA requires that patient data be protected from unauthorized access during transmission and deployment. Using a private Google Cloud Access endpoint with external internet access disabled ensures that the model endpoint is only reachable within the organization's VPC network, preventing data exposure over the public internet and meeting HIPAA's security rule for safeguarding electronic protected health information (ePHI).

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Store all patient data in Cloud Storage with object versioning.

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Enable encryption at rest for all resources.

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Use a publicly accessible endpoint for faster response times.

Question 15

A prompt engineer wants to improve the model's adherence to a specific output format (e.g., always start with a greeting). Which technique should they try first?

Accepted Answer

Include a system instruction at the beginning of the prompt that specifies the desired format.. Option C is correct because system instructions are the most direct and efficient method to enforce output formatting in large language models. By placing a clear directive at the beginning of the prompt (e.g., 'Always start your response with a greeting'), the model's attention mechanism is guided to prioritize this rule during generation, without requiring retraining or hyperparameter changes.

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Use a lower temperature to make the output more deterministic.

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Fine-tune the model on many examples of the desired format.

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Modify the model's tokenizer to encode the format rules.