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

Question 1

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. Options A, B, and C are core features. Option D is wrong because automated hyperparameter tuning is not part of the studio. Option E is wrong because A/B testing requires deployment, not experimentation.

Answer

Automated hyperparameter tuning

Answer

A/B testing of multiple prompt versions

Question 2

A large enterprise is deploying a multi-modal generative AI application that processes customer support emails (text) and attached screenshots (images). They need to run inference on over 10,000 requests per minute with strict latency requirements (p99 < 500ms). They have already selected Gemini 1.5 Pro as the model and deployed it on Vertex AI using a GPU-based endpoint with autoscaling. During testing, they observe that the p99 latency spikes to over 2 seconds during peak traffic. The application is stateless and requests are independent. The team has access to Cloud Observability and can modify the deployment configuration. Which course of action should the team take to meet the latency requirements while minimizing cost?

Accepted Answer

Enable Vertex AI Model Caching and deploy the endpoint on a managed instance group with larger GPU nodes (e.g., A100 40GB). Option C is correct because enabling model caching reduces redundant computation for repeated prompts, and using dedicated VMs (MIGs) with higher GPU count per replica reduces per-request latency. Option A is wrong because adding more replicas may help throughput but not per-request latency. Option B is wrong because CPU-based serving would be much slower for Gemini. Option D is wrong because preemptible VMs are not reliable for production latency.

Answer

Increase the maximum number of replicas in the autoscaling configuration to handle spikes

Answer

Use preemptible VMs for the endpoint to get priority scheduling

Answer

Switch to a CPU-based ml.c5 instance to reduce GPU contention

Question 3

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. Option D is correct because a custom system instruction can explicitly constrain the agent's behavior. Option A is wrong because adding more data may not control behavior. Option B is wrong because fine-tuning may not cover all edge cases. Option C is wrong because a safety filter may not catch subtle recommendations.

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

Question 4

A retail company is building a chatbot for customer service. They need the model to generate product descriptions based on a catalog but also answer questions about store policies. The team wants to minimize latency and cost while maintaining high accuracy. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Gemini API. Option B is correct because Gemini is a multimodal model that can handle both product descriptions (text) and policy questions, and Vertex AI offers optimized inference for latency and cost. Option A (Imagen) is for image generation, not text. Option C (Codey) is for code generation. Option D (PaLM 2 via Model Garden) is possible but Gemini is more modern and efficient.

Answer

Vertex AI Model Garden with PaLM 2

Answer

Vertex AI Imagen

Answer

Vertex AI Codey APIs

Question 5

A media company is using Vertex AI Imagen to generate marketing images. The output frequently contains unrealistic artifacts, especially in human faces. The team has fine-tuned the model using their brand assets. What is the most likely cause and recommended fix?

Accepted Answer

The fine-tuning dataset is too small or too homogeneous; augment and diversify the training data.. Option D is correct. Overfitting to a small dataset can cause artifacts. Common symptoms include unrealistic details. Option A is less likely because safety filters usually block rather than degrade quality. Option B (low inference steps) could cause quality loss but typically not specific artifacts. Option C (negative prompt) might help but is not the root cause.

Answer

Safety filters are too aggressive; reduce them.

Answer

Negative prompts are missing; always include 'unrealistic'.

Answer

Inference steps are too low; increase to 100.

Question 6

A startup wants to deploy a custom-tuned large language model for real-time inference on Vertex AI. They need the lowest possible latency for end users. What deployment strategy should they choose?

Accepted Answer

Deploy the tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling.. Option A is correct: a dedicated endpoint with GPU ensures low latency. Option B (batch prediction) is for asynchronous tasks. Option C (Cloud Functions) adds overhead. Option D (Model Garden with PaLM 2) does not allow custom model deployment.

Answer

Use Vertex AI Model Garden to deploy the base PaLM 2 model.

Answer

Wrap the model in a Cloud Function and invoke via HTTP.

Answer

Use Vertex AI Batch Prediction to process requests in batches.

Question 7

Which TWO of the following are capabilities of Vertex AI Model Garden? (Choose 2)

Accepted Answer

Access to a curated set of foundation models like PaLM and Gemini.. Option A and D are correct. Model Garden provides a curated set of foundation models and allows fine-tuning. Option B (generating images) is Imagen's capability. Option C (generating code) is Codey's capability. Option E (deploying custom containers) is a general Vertex AI feature not specific to Model Garden.

Answer

Generate code snippets for common programming tasks.

Answer

Ability to generate images from text descriptions.

Answer

Deploy custom container images for model serving.

Question 8

Which THREE of the following are features of Vertex AI Studio (Gen AI Studio)? (Choose 3)

Accepted Answer

Compare responses from different models side-by-side.. Option A, B, and E are correct. Gen AI Studio provides design, test, and prompt engineering capabilities. Option C (deployment of custom containers) is not a feature of Studio. Option D (pre-built safety filters) is a feature of the overall Vertex AI platform, but Studio focuses on prototyping.

Answer

Configure pre-built safety filters for generated content.

Answer

Deploy custom container images to Vertex AI endpoints.

Question 9

Which TWO of the following are best practices for configuring safety settings in Vertex AI generative models? (Choose 2)

Accepted Answer

Adjust safety thresholds based on the specific use case and audience.. Option B and D are correct. Adjusting safety thresholds per use case and using the safety API are best practices. Option A (disabling safety filters) is risky. Option C (max thresholds) may block legitimate content. Option E (single setting for all) is not recommended.

Answer

Disable safety filters for maximum creativity.

Answer

Apply the same safety settings to all models in the organization.

Answer

Always use the maximum safety threshold to block all potentially harmful content.

Question 10

A financial services company is building a customer-facing chatbot using Vertex AI Gemini API to answer questions about account balances, transactions, and branch locations. The chatbot must adhere to strict data privacy regulations (e.g., GDPR) that prohibit sending personally identifiable information (PII) to the model provider. The architecture uses a retrieval-augmented generation (RAG) approach where customer queries are passed to a Cloud Run service, which queries a BigQuery database for relevant data and then sends the context along with the query to the Gemini API. The team is concerned that the context may contain PII. They want to minimize modifications to the existing architecture. What step should the team take to ensure compliance?

Accepted Answer

Configure the Gemini API to disable data logging and use the Enterprise tier that ensures data stays within Google Cloud's controls.. Option B is correct. Vertex AI Gemini API can be configured to not log prompts and responses, and data is processed within Google Cloud's infrastructure (data residency). Option A (third-party proxy) adds complexity and latency. Option C (always reject PII queries) would break functionality. Option D (anonymizing pipeline) is costly and not minimal.

Answer

Build a separate anonymization pipeline using Cloud Data Loss Prevention to remove PII before sending context.

Answer

Modify the chatbot to reject any query that might contain PII based on regex patterns.

Answer

Route all requests through a third-party proxy that strips PII before sending to Gemini.

Question 11

A healthcare startup is using Vertex AI Imagen to generate synthetic medical images for training a diagnostic model. The images must comply with HIPAA regulations and cannot contain any real patient data. The team fine-tuned Imagen on a dataset of de-identified medical scans. However, during testing, they notice that some generated images closely resemble specific patients from the original dataset, even though the dataset was de-identified. They suspect that the model memorized some training examples. The team needs to address this issue without losing image quality. They have access to the original training data and Vertex AI tools. What action should they take?

Accepted Answer

Re-tune the model using differential privacy (DP-SGD) to prevent memorization of individual examples.. Option D is correct. Differential privacy during fine-tuning adds noise to prevent memorization. Option A (more data) might not help if the model is overfitting. Option B (stronger safety filters) won't prevent uniqueness recall. Option C (post-processing) only alters output after generation, doesn't fix memorization.

Answer

Use a post-processing step to blur or distort generated images.

Answer

Increase the size of the training dataset by adding more synthetic images.

Answer

Apply stricter output safety filters to block images that look like any known patient.

Question 12

A marketing agency wants to use Vertex AI to automatically generate social media posts for clients. They plan to use the Gemini API with few-shot prompting. The agency's developers have limited experience with generative AI and want the fastest way to prototype and iterate on prompts. They are already using Google Cloud for other services. Which approach should they take to quickly develop and test prompts?

Accepted Answer

Use Vertex AI Studio (Gen AI Studio) to design and test prompts interactively.. Option A is correct. Vertex AI Studio provides a no-code interface for prompt design and testing. Option B (write code) is slower. Option C (use Cloud Shell) is possible but less user-friendly. Option D (third-party tool) adds complexity.

Answer

Use a third-party platform like OpenAI Playground and migrate later.

Answer

Use Google Cloud Shell to invoke the model via curl commands.

Answer

Write Python scripts using the Vertex AI SDK and run them in Airflow.

Question 13

An e-commerce company is using Vertex AI PaLM 2 for Text (via Model Garden) to generate product descriptions. They have an existing pipeline that calls the model with a prompt including product attributes. Recently, they migrated to the Gemini API. The team notices that the Gemini model sometimes outputs descriptions that are factually inconsistent with the input (e.g., wrong color or size). This was less frequent with PaLM 2. They have not changed the prompts. What is the most likely cause and solution?

Accepted Answer

Adjust the prompt to be more explicit about adhering to the input data, and reduce the temperature.. Option C is correct. Gemini may follow instructions differently; adjusting the prompt or temperature can help. Option A (increase model temperature) may increase randomness, worsening consistency. Option B (add negative prompts) might not address factual alignment. Option D (switch back to PaLM 2) does not solve the cause.

Answer

Revert to PaLM 2 since it was more reliable for this task.

Answer

Add negative prompts to discourage incorrect facts.

Answer

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

Question 14

A multinational corporation is using Vertex AI to generate multilingual customer support responses. They have fine-tuned the Gemini model on support tickets in English and now want to extend to 10 additional languages. The fine-tuning dataset for new languages is small (1000 tickets each). During evaluation, the model performs well for common languages (Spanish, French) but poorly for languages like Finnish and Thai. The team needs to improve performance for low-resource languages. They have budget constraints and cannot collect more data quickly. Which approach should they take?

Accepted Answer

Use a multilingual foundation model and fine-tune with cross-lingual transfer learning techniques.. Option B is correct. Cross-lingual transfer learning (like using a multilingual model or fine-tuning with a high-resource language pair) leverages data from similar languages. Option A (collect more data) is not feasible quickly. Option C (use Codey) is irrelevant. Option D (separate models) increases cost and complexity.

Answer

Switch to Vertex AI Codey API for generating responses in all languages.

Answer

Deploy separate fine-tuned models for each language.

Answer

Collect more training data for low-resource languages via crowdsourcing.

Question 15

A news organization is using Vertex AI Gemini to summarize articles. They observe that the summaries sometimes contain hallucinated facts—specifically, dates and statistics that are not in the original article. The team is using the default temperature and top_p settings. They want to reduce hallucinations without making summaries too repetitive or overly conservative. They also need to keep latency low. Which action should they take?

Accepted Answer

Enable grounding with Google Search to provide factual source context.. Option D is correct. Grounding with Google Search can provide factual references. Option A (lower temperature) may reduce creativity but not eliminate hallucination. Option B (increase top_p) might increase randomness. Option C (fine-tuning) is expensive and might not generalize.

Answer

Increase the temperature to 1.0 and lower top_p to 0.1.

Answer

Fine-tune the model on a large dataset of articles and human-written summaries.

Answer

Lower the temperature to 0.0 and increase top_p to 1.0.