Generative AI Leader Fundamentals of Generative AI Practice Test 3 — 15 Questions

Question 1

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?

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

Accepted Answer

Change the machine type to n1-standard-8 and enable autoscaling with min replicas=1, max replicas=5. 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 2

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 3

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 4

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 5

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'.. 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 6

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 7

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 8

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.. 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 9

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.. 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.

Question 10

During a RAG pipeline implementation, the retrieval system frequently returns irrelevant documents, causing the generator to produce incorrect answers. Which change is most likely to improve the relevance of retrieved documents?

Accepted Answer

Add a re-ranking step using a cross-encoder model to refine the top results.. Re-ranking with a cross-encoder model evaluates query-document pairs more deeply, improving precision at the cost of latency. Increasing the number of documents may introduce more noise. Changing embedding dimension or chunk size may help but are less targeted.

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Increase the number of documents retrieved from the vector store.

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Use a different embedding model with higher vector dimension.

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Decrease the chunk size of documents to reduce noise.

Question 11

A team is training a custom foundation model using JAX on TPUs on Google Cloud. They encounter frequent Out of Memory (OOM) errors. Which action is most effective in resolving the OOM error?

Accepted Answer

Enable model parallelism using GSPMD to distribute the model across TPU cores.. OOM errors when training large foundation models on TPUs often stem from the model exceeding the memory of a single TPU core. GSPMD (Generalized SPMD) enables automatic model parallelism, sharding the model's parameters, gradients, and optimizer states across multiple TPU cores, thereby reducing per-core memory pressure without altering the model architecture or precision.

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Reduce the model size by decreasing the number of layers.

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Increase the batch size to maximize TPU utilization.

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Use mixed precision training (bfloat16) to reduce memory footprint.

Question 12

A data scientist is selecting a base model for generating Python code. Which TWO factors are most important to consider?

Accepted Answer

Model's license (proprietary vs open-source).. The model's license determines whether the generated code can be used in commercial products without violating copyright or requiring attribution. Proprietary models may impose restrictions on output usage, while open-source models (e.g., CodeLlama, StarCoder) offer more flexibility for enterprise deployment. This is critical for compliance and intellectual property management in production environments.

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Model's support for multiple programming languages.

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Model's training data recency.

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Model's parameter count (size).

Question 13

What are THREE benefits of using embedding models in a Retrieval Augmented Generation (RAG) system?

Accepted Answer

They compress text into dense vectors for efficient retrieval.. Embedding models convert text into dense vector representations that capture semantic meaning, enabling efficient similarity search in vector databases. This compression reduces the dimensionality of the data, allowing the RAG system to quickly retrieve the most relevant documents from a large corpus based on vector distance metrics like cosine similarity.

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They allow the model to generate new training data automatically.

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They provide deterministic outputs for the same query.

Question 14

A company deploys a Gemini model on Vertex AI for a customer-facing chatbot. They observe the chatbot occasionally produces toxic language. Which TWO measures should they implement immediately to reduce toxic outputs?

Accepted Answer

Enable the 'block offensive content' flag in the model's safety configuration.. Enabling the 'block offensive content' flag directly activates Gemini's built-in safety filters, which are designed to detect and suppress toxic language at the model's output layer. This is an immediate, configuration-level measure that requires no additional training or external services, making it the fastest way to reduce harmful responses in a production chatbot.

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Increase the model's temperature to make outputs more conservative.

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Use a separate language model to rephrase the outputs before sending to users.

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Fine-tune the model on a curated dataset of polite conversations.

Question 15

Refer to the exhibit. A data scientist runs the gcloud command and sees the model listed. However, when they try to deploy the model to an endpoint, they get an error: 'Model is not deployable'. What is the most likely reason?

Accepted Answer

The model was imported from a custom container but without a serving specification or artifact.. A model imported from a custom container must include a serving specification (e.g., a `predict` route) and an artifact (e.g., a saved model file) to be deployable. Without these, Vertex AI cannot determine how to serve predictions, resulting in the 'Model is not deployable' error. The `gcloud` command listing the model only confirms its registration, not its readiness for deployment.

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The model is still in training and not yet ready.

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The model does not have the correct IAM permissions assigned to the deployment service account.

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The region for the endpoint is different from the model's region.