Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 8 — 15 Questions

Question 1

A data scientist uses Vertex AI Model Evaluation to assess a fine-tuned model for sentiment analysis. The evaluation report shows high precision but low recall on the 'negative' class. What is the best course of action to improve recall without sacrificing too much precision?

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

Accepted Answer

Collect more labeled examples of negative sentiment and retrain. Collecting more labeled examples of negative sentiment and retraining addresses the root cause of low recall: insufficient or imbalanced training data for the negative class. This improves the model's ability to recognize negative sentiment without sacrificing precision, as the decision boundary is refined with more representative data. Option A (adjusting prediction threshold) can increase recall but typically at the cost of precision, contradicting the goal. Option B (switching model architecture) is excessive and may not fix data imbalance. Option D (using a larger pretrained model) does not specifically target recall on the negative class.

Answer

Adjust the prediction threshold for the negative class

Answer

Switch to a different model architecture (e.g., from BERT to RoBERTa)

Answer

Use a larger pretrained model from Model Garden

Question 2

Which THREE steps are required to secure a generative AI pipeline that uses Vertex AI and involves sensitive customer data?

Accepted Answer

Use VPC Service Controls to create a perimeter around Vertex AI resources. Option A is correct because VPC Service Controls lets you define a service perimeter around Vertex AI resources, preventing data exfiltration of sensitive customer data even if credentials are compromised. Option B is correct because applying IAM roles with least privilege and using dedicated service accounts for the pipeline enforces fine-grained access control and limits the blast radius of any compromised identity. Option D is correct because enabling data encryption at rest with Cloud KMS (customer-managed encryption keys) protects sensitive customer data stored in Vertex AI datasets, models, and related storage from unauthorized access at the storage layer. Option C is incorrect because exposing the prediction endpoint publicly with only an API key removes network-level protections and is not a recommended security control for sensitive data. Option E is incorrect because disabling audit logging reduces visibility and accountability, which weakens security and compliance rather than strengthening them.

Answer

Expose the prediction endpoint publicly with an API key

Answer

Disable audit logging to reduce data exposure

Question 3

Which THREE benefits does Vertex AI Agent Builder provide over building a custom conversational agent from scratch?

Accepted Answer

Automatic scaling and load balancing. Option A is correct because Vertex AI Agent Builder is a managed service that automatically handles scaling and load balancing of the agent infrastructure, removing the need to provision or tune servers yourself. Option B is correct because it provides pre-built connectors and integrations for grounding responses on enterprise data sources such as Vertex AI Search, BigQuery, and other Google Cloud data stores, which would otherwise require custom retrieval pipelines. Option D is correct because it includes built-in safety filters and guardrails (e.g., responsible AI controls, content moderation, and policy enforcement) that a from-scratch agent would need to implement manually. Option C is not correct because Agent Builder abstracts the underlying model and does not give full control over the ML model architecture, which is actually a limitation rather than a benefit. Option E is not correct because Agent Builder does not guarantee lower inference latency; latency depends on the selected model, region, and workload, and no such guarantee is offered.

Answer

Full control over the underlying ML model architecture

Answer

Guaranteed lower inference latency

Question 4

A company is building a customer service chatbot using Vertex AI Agent Builder. The chatbot needs to answer questions based on a large internal knowledge base stored in a Cloud Storage bucket. The team wants to ensure the model can reference the latest documents without fine-tuning. Which configuration should they use?

Accepted Answer

Ground the model with a Vertex AI Search data store connected to the Cloud Storage bucket. Grounding with a Vertex AI Search data store is the correct pattern for retrieval-augmented generation (RAG) in Vertex AI Agent Builder. The data store indexes the Cloud Storage documents and the agent retrieves relevant chunks at query time, so the model can cite the latest content without any fine-tuning. This keeps the knowledge base fresh — updating a document in the bucket automatically flows into subsequent answers.

Answer

Fine-tune a model on the knowledge base documents

Answer

Use a pre-built model with no additional configuration

Answer

Store the documents in BigQuery and use a BigQuery connector

Question 5

A data scientist wants to generate realistic product images for an online catalog using Google Cloud's generative AI. Which service should they use?

Accepted Answer

Imagen on Vertex AI. Imagen on Vertex AI is Google Cloud's specialized service for generating high-quality, photorealistic images from text prompts. It is built on diffusion models and is directly designed for image generation tasks, making it the correct choice for creating product images for an online catalog.

Answer

Codey API for code generation

Answer

Gemini API with text-to-text prompts

Answer

Vertex AI Model Garden without a specific model

Question 6

A company deploys a Gemini model on Vertex AI for a healthcare application. They need to ensure that the model does not generate medical advice and that responses are grounded in trusted medical sources. Which combination of safety measures should they implement?

Accepted Answer

Enable safety filters and use Vertex AI Grounding with a labeled medical dataset. It combines two essential safety layers: safety filters block harmful content (including medical advice), and Vertex AI Grounding anchors responses to a labeled medical dataset, ensuring factual accuracy and compliance with healthcare regulations. This dual approach prevents the model from generating unverified or dangerous medical information while maintaining relevance to trusted sources.

Answer

Use Vertex AI Grounding with a public dataset and disable safety filters

Answer

Enable safety filters only, without grounding

Answer

Fine-tune the model on a curated medical dataset and disable safety filters for faster responses

Question 7

A developer is using Vertex AI's Generative AI Studio to prototype a text summarization model. The initial results are too verbose. What is the most efficient way to adjust the output length without retraining?

Accepted Answer

Modify the prompt with specific length instructions and adjust model parameters. The most efficient way to adjust output length without retraining is to modify the prompt with specific length instructions (e.g., 'Summarize in one sentence') and adjust model parameters like max output tokens, temperature, and top_p in Vertex AI's Generative AI Studio. Option A is wrong because switching to a smaller base model like BERT would require retraining or different model architecture, and BERT is not primarily a text generation model for summarization. Option B is wrong because adding a separate classifier increases complexity and does not directly control the model's output length. Option C is wrong because fine-tuning requires additional training data and computational resources, which is not the most efficient approach compared to prompt and parameter adjustments.

Answer

Switch to a smaller base model like BERT

Answer

Use a separate classifier to filter long responses

Answer

Fine-tune the model with a dataset of concise summaries

Question 8

A startup wants to embed generative AI features into their mobile app but has limited ML expertise. Which Google Cloud service is best suited for rapid integration with no ML training?

Accepted Answer

Gemini API. The Gemini API provides direct, no-code access to Google's most capable generative AI models via a simple REST API, requiring zero ML training or infrastructure setup. This makes it the fastest path for a startup with limited ML expertise to embed generative AI features like text generation, summarization, or chat into a mobile app.

Answer

Vertex AI Model Garden

Answer

Vertex AI Agent Builder

Answer

Cloud Run with a custom container

Question 9

A company is deploying a large language model on Vertex AI for real-time inference. They observe high latency and want to optimize. They have already enabled model caching. What next step should they take to reduce latency?

Accepted Answer

Apply model quantization to reduce precision. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and accelerates computation on the hardware, directly lowering inference latency. Since Vertex AI already has model caching enabled, quantization is the next logical optimization step to reduce latency without requiring additional infrastructure changes.

Answer

Add more GPUs to the prediction endpoint

Answer

Use a larger, more accurate model variant

Answer

Increase the batch size for inference requests

Question 10

A marketing team wants to use Vertex AI to generate ad copy. They need the model to follow a specific tone and style. What is the best approach?

Accepted Answer

Provide few-shot examples in the prompt and adjust temperature. Few-shot prompting with adjusted temperature is the best approach because it directly controls the model's output style and tone without requiring additional infrastructure or training. By providing a few examples of desired ad copy in the prompt, the model learns the specific tone and style through in-context learning, while temperature tuning (e.g., 0.2 for deterministic output) ensures consistency. This is the most efficient and cost-effective method for immediate, controllable generation.

Answer

Use Vertex AI Grounding to retrieve style guides

Answer

Fine-tune the model on a dataset of past ad copy

Answer

Enable safety filters to enforce brand guidelines

Question 11

A developer wants to generate Python code using Google Cloud's generative AI. Which model should they invoke?

Accepted Answer

Codey. Codey is Google Cloud's family of models specifically designed for code generation, completion, and chat, built on the PaLM 2 architecture and fine-tuned on code-heavy datasets. For a developer needing to generate Python code, Codey is the correct choice because it is purpose-built for code-related tasks, unlike other models that specialize in different modalities.

Answer

Chirp

Answer

Imagen

Answer

Meena

Question 12

A company is building a generative AI application that must adhere to strict data residency regulations. Which TWO Google Cloud features can help ensure that data does not leave a specific geographic region?

Accepted Answer

Using the regional endpoint for Vertex AI. To ensure data residency, you can use the regional endpoint for Vertex AI (A) which ensures that API calls and data processing stay within the specified region. Additionally, deploying models on dedicated VMs in a specific region (E) ensures that compute resources and data do not leave that region. Option B (Vertex AI Model Caching) does not enforce residency as caching may use regional resources but does not guarantee data stays within a region. Option C (Global load balancer with Cloud Armor) is a network security and load balancing service that operates globally. Option D (Cloud CDN) caches content at edge locations globally, which would move data outside the region.

Answer

Vertex AI Model Caching

Answer

Global load balancer with Cloud Armor

Answer

Cloud CDN for content delivery

Question 13

A machine learning engineer is tuning a large language model on Vertex AI for question answering. They want to evaluate the model's performance before deployment. Which THREE metrics should they consider?

Accepted Answer

F1 score. For a question-answering evaluation on Vertex AI, F1 score (B) is correct because it measures token-level overlap between the predicted answer and the ground-truth answer, balancing precision and recall and handling partial matches. Exact match (C) is correct because it reports the percentage of predictions that exactly equal the reference answer, a standard metric in QA benchmarks such as SQuAD. ROUGE-L score (E) is correct because it measures the longest common subsequence between generated and reference text, capturing fluency and recall-oriented overlap useful for generative QA outputs. Cost per training epoch (A) and training time per epoch (D) are operational/training-efficiency measures, not model quality metrics, so they do not evaluate performance before deployment.

Answer

Cost per training epoch

Answer

Training time per epoch

Question 14

A data scientist is using Vertex AI's Generative AI Studio to experiment with prompt designs. Which THREE features are available in the studio?

Accepted Answer

Grounding configuration. In Vertex AI's Generative AI Studio, grounding configuration (A) is available so you can ground model responses in specific data sources such as Vertex AI Search or your own datasets, reducing hallucinations and improving factual accuracy. Model parameter adjustments (B) are also provided, letting you tune values like temperature, top_p, top_k, and max output tokens directly in the studio to control response creativity and length. Prompt templates (D) are included as a feature, offering pre-built and customizable prompt structures that help you quickly design and reuse effective prompts. Automated hyperparameter tuning (C) belongs to Vertex AI training services like Vertex AI Vizier or custom training jobs, not the prompt-design interface of Generative AI Studio. A/B testing of multiple prompt versions (E) is not a built-in feature of Generative AI Studio; comparing prompt variants typically requires external evaluation tooling or Vertex AI evaluation services rather than a native A/B testing option in the studio.

Answer

Automated hyperparameter tuning

Answer

A/B testing of multiple prompt versions

Question 15

A fintech startup is building a generative AI application that generates personalized investment advice based on user profiles and market data. They are using Vertex AI Agent Builder to create an agent that retrieves information from a BigQuery table containing user data and from a real-time market data API. The agent needs to ensure that responses comply with financial regulations, meaning the model must not give specific stock recommendations unless the user explicitly requests them after disclaimers. The team has implemented grounding with both sources. During testing, the agent sometimes spontaneously suggests buying a particular stock without being asked, which could lead to regulatory issues. The team wants to enforce strict control over the agent's behavior. What should the team do?

Accepted Answer

Implement a custom system instruction that explicitly prohibits unsolicited stock recommendations and requires a disclaimer before any advice. System instructions in Vertex AI Agent Builder allow you to define strict behavioral rules that the agent must follow, such as prohibiting unsolicited stock recommendations and requiring a disclaimer before any advice. This directly addresses the regulatory compliance issue by enforcing a policy at the agent's instruction layer, which overrides any learned or grounded behavior. Unlike other options, this approach provides explicit, enforceable control without altering data sources or model training.

Answer

Increase the safety filter sensitivity to block any financial recommendations

Answer

Add more historical data to the BigQuery table to improve grounding accuracy

Answer

Fine-tune the model on a dataset of compliant conversations