Generative AI Leader Google Cloud's Generative AI Offerings Practice Test 6 — 15 Questions

Question 1

A financial services firm needs to generate synthetic data for training models while ensuring that no real customer data leaks. Which technique should they use?

Accepted Answer

Differential privacy during fine-tuning. Differential privacy during fine-tuning is the correct technique because it adds calibrated noise to the training process, ensuring that the synthetic data generated does not reveal information about any individual real customer record. This approach provides a formal mathematical guarantee of privacy, making it suitable for generating synthetic data that preserves statistical properties while preventing data leakage. In contrast, other methods like redaction, masking, or using a public model do not inherently prevent the model from memorizing and reproducing sensitive information.

Answer

Using the Vertex AI PII redaction service

Answer

Using a public foundation model without fine-tuning

Answer

Data masking before training

Question 2

A project manager wants to understand which Google Cloud generative AI services are subject to the 'Prohibited Use' policy. Where can they find the most up-to-date information?

Accepted Answer

The Google Cloud Acceptable Use Policy. The Google Cloud Acceptable Use Policy (AUP) is the authoritative document that defines prohibited uses of Google Cloud services, including generative AI offerings. It is regularly updated to reflect current legal, ethical, and security requirements, making it the most reliable source for the most up-to-date information on prohibited use cases. The AUP explicitly covers restrictions on generating harmful content, engaging in illegal activities, and violating intellectual property rights, which directly apply to generative AI services.

Answer

Google Cloud documentation

Answer

Google's AI Principles

Answer

The Gemini Terms of Service

Question 3

A company is evaluating Google Cloud's generative AI offerings for enterprise use. Which TWO considerations are most important when selecting the right model deployment option?

Accepted Answer

Data residency. Data residency (A) is a critical consideration because enterprise deployments must comply with regional and regulatory requirements, and Google Cloud's Vertex AI lets you pin model endpoints and data processing to specific locations such as us-central1 or europe-west4 to satisfy sovereignty and compliance mandates. Latency requirements (D) are equally important because the deployment option determines network proximity and serving characteristics — for example, a regional endpoint close to users or a provisioned throughput configuration reduces round-trip time, which directly affects real-time generative AI application performance. Together, residency and latency drive the choice between options like global vs. regional endpoints, on-demand vs. provisioned throughput, and Vertex AI vs. Gemini API. Developer preference (B) is subjective and does not determine the correct deployment architecture for enterprise compliance or performance needs. Model size (C) is a model characteristic rather than a deployment-selection criterion, since Vertex AI abstracts the underlying infrastructure. Training time (E) is irrelevant to deployment selection because it concerns the model-building phase, not how the model is served in production.

Answer

Developer preference

Answer

Model size

Answer

Training time

Question 4

An organization is building a generative AI application on Vertex AI. Which THREE actions should they take to ensure responsible AI practices?

Accepted Answer

Implement human review for sensitive outputs. Option B is correct because implementing human review for sensitive outputs ensures that high-risk or ambiguous AI-generated content is validated by a person before it reaches users, which is a core responsible AI safeguard for generative applications on Vertex AI. Option C is correct because conducting fairness evaluation (for example, using Vertex AI's model evaluation tools to assess bias across demographic groups) helps detect and mitigate discriminatory or skewed model behavior, directly supporting responsible AI. Option D is correct because creating a safety policy and enforcing it via content filtering operationalizes responsible AI by defining prohibited content and using Vertex AI safety filters (such as configurable hate speech, harassment, and dangerous content thresholds) to block violations. Option A is not correct because disabling content filtering removes a key safety control and increases the risk of harmful outputs, which is contrary to responsible AI. Option E is not correct because using only Google's foundation models does not by itself guarantee responsible AI; responsibility depends on evaluation, policy enforcement, monitoring, and human oversight regardless of which models are used.

Answer

Disable content filtering

Answer

Use only Google's foundation models

Question 5

A developer wants to use the Gemini API to generate creative text. Which TWO parameters can they adjust to influence the output?

Accepted Answer

Top-k. Top-k (C) is correct because it limits sampling to the k most probable next tokens, directly shaping the randomness and creativity of the generated text. Temperature (E) is correct because it scales the logits before the softmax, controlling how deterministic or diverse the model's token choices are. Color space (A) is irrelevant since it pertains to image pixel encoding, not text generation parameters. Audio sample rate (B) applies to audio processing, not Gemini text output. Image size (D) affects image dimensions, which has no bearing on text generation creativity.

Answer

Color space

Answer

Audio sample rate

Answer

Image size

Question 6

A company is building a customer support chatbot using Vertex AI Agent Builder. They want the agent to answer questions based on internal knowledge base documents stored in Cloud Storage. Which feature should they configure to ensure the agent can retrieve relevant information from these documents?

Accepted Answer

Enable grounding with a data store. Vertex AI Agent Builder uses grounding to connect the agent to external data sources, such as documents stored in Cloud Storage. By enabling grounding with a data store, the agent can retrieve and reference relevant information from the knowledge base documents in real time, ensuring accurate and context-aware responses without requiring model retraining.

Answer

Deploy the agent to a Vertex AI endpoint

Answer

Fine-tune a Gemini model on the knowledge base

Answer

Configure a safety filter to block irrelevant queries

Question 7

A startup wants to generate images from text descriptions for their marketing materials. They prefer a managed service that requires minimal coding. Which Google Cloud generative AI offering should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's managed generative AI service specifically designed for text-to-image generation. It requires minimal coding, as users can interact with it via the Cloud Console, API calls with simple prompts, or through Vertex AI's built-in tools, making it ideal for a startup needing to generate marketing images from text descriptions without extensive development effort.

Answer

Natural Language API

Answer

Document AI

Answer

Cloud Speech-to-Text

Question 8

An organization is using Vertex AI Gemini API for a multimodal chatbot. They notice that the model sometimes provides incorrect information with high confidence. They want to reduce hallucinations without retraining the model. What is the most effective approach?

Accepted Answer

Provide ground-truth context from a knowledge base using grounding. Grounding with a knowledge base is the most effective approach because it forces the model to base its responses on verified, external data rather than relying solely on its parametric knowledge. By providing ground-truth context via Vertex AI's grounding feature, the model can cross-reference its outputs with authoritative sources, significantly reducing the likelihood of hallucinations. This method directly addresses the root cause of incorrect high-confidence responses without requiring retraining.

Answer

Increase the temperature parameter to make the model more creative

Answer

Reduce the maximum output tokens to force concise answers

Answer

Adjust safety settings to filter uncertain responses

Question 9

A retailer wants to use generative AI to write product descriptions automatically. They have a large dataset of existing product descriptions and need to customize a foundation model for their brand voice. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI custom model tuning. Vertex AI custom model tuning (Option D) is correct because it allows the retailer to fine-tune a foundation model on their proprietary dataset of existing product descriptions, adapting the model's output to match their specific brand voice and style. This process adjusts the model's weights using supervised learning on the retailer's data, enabling personalized and consistent content generation that generic prompt engineering cannot achieve.

Answer

Vertex AI Search with grounding

Answer

Prompt design with the Gemini API directly

Answer

Vertex AI Model Evaluation

Question 10

A company deploys a fine-tuned text generation model on Vertex AI Endpoints. They want to monitor for data drift and performance degradation over time. Which GCP service should they integrate?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct choice because it is specifically designed to detect data drift (changes in input data distribution) and feature attribution drift in deployed models, including fine-tuned text generation models on Vertex AI Endpoints. It provides automated alerts when model performance degrades due to shifts in production data, enabling proactive retraining or intervention.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

Vertex AI Experiments

Question 11

A financial services firm is using Vertex AI to generate investment reports. They need to ensure that the model outputs are explainable and comply with regulatory requirements. Which Vertex AI feature should they use?

Accepted Answer

Vertex Explainable AI. Vertex Explainable AI provides feature attributions and explanations for model predictions, which is essential for financial services firms that must comply with regulatory requirements like the EU's GDPR or the US SEC's model risk management guidelines. It helps auditors and stakeholders understand why a model generated a specific investment report output, ensuring transparency and accountability in AI-driven decisions.

Answer

Vertex AI Model Registry

Answer

Vertex AI Safety Settings

Answer

Vertex AI AutoML

Question 12

A developer wants to integrate Gemini multimodal capabilities (text + image) into a mobile app using Python. Which Google Cloud client library should they use?

Accepted Answer

Vertex AI client library (google-cloud-aiplatform). The Vertex AI client library (google-cloud-aiplatform) provides the Generative AI SDK that supports multimodal capabilities, including the ability to send both text and image inputs to Gemini models. This library directly exposes the `GenerativeModel` class with methods like `generate_content()` that accept `Part` objects containing image data (e.g., `Part.from_image()` or `Part.from_uri()`), making it the correct choice for integrating Gemini multimodal features into a Python mobile app backend.

Answer

Dialogflow CX

Answer

Cloud Vision API

Answer

Natural Language API

Question 13

A healthcare company is building a chatbot to answer patient queries using Vertex AI Agent Builder. They want to ensure the chatbot only uses approved medical references and does not generate unverified advice. How should they configure the agent?

Accepted Answer

Set up grounding with a private data store containing verified medical documents. Vertex AI Agent Builder supports grounding with private data stores, which allows the agent to restrict its responses to only the information contained in the verified medical documents. This ensures the chatbot does not generate unverified advice by grounding its outputs in a trusted, curated dataset rather than relying on the model's general training data.

Answer

Enable strict safety filters to block any medical advice

Answer

Increase the temperature parameter to get more diverse responses

Answer

Use Vertex AI Model Monitoring to track answer accuracy

Question 14

Which TWO safety features are available in Vertex AI Gemini API? (Select TWO.)

Accepted Answer

Safety filters for categories like hate speech and harassment. Option A is correct because the Vertex AI Gemini API applies configurable safety filters that screen both prompts and responses against harm categories such as hate speech, harassment, sexually explicit content, and dangerous content. Option B is correct because these safety filters operate using configurable thresholds (for example BLOCK_LOW_AND_ABOVE, BLOCK_MEDIUM_AND_ABOVE, BLOCK_ONLY_HIGH, BLOCK_NONE) that let developers tune how aggressively content is blocked per category. Together, A and B describe the built-in safety attributes exposed through the API's safetySettings. Option C is not a Gemini API safety feature but a general Google Cloud storage/platform control (encryption at rest is handled by the underlying infrastructure, not the API's safety settings). Option D is incorrect because the Gemini API does not automatically redact PII as a built-in safety feature. Option E is incorrect because Cloud DLP is a separate Google Cloud service that must be integrated manually and is not a native safety feature of the Gemini API.

Answer

Model-level encryption at rest

Answer

Automatic redaction of personally identifiable information (PII)

Answer

Integration with Cloud Data Loss Prevention (DLP)

Question 15

A data scientist runs the above command to upload a model to Vertex AI Model Registry. The model is a TensorFlow 2.6 model trained on tabular data. After deployment to an endpoint, the prediction latency is higher than expected. What is the most likely cause?

Accepted Answer

The artifact URI points to a single file instead of a directory. When uploading a model to Vertex AI Model Registry, the artifact URI must point to a directory containing the SavedModel or model artifacts, not a single file. If the URI points to a single file, the model may not load properly or may serve with higher latency. For a TensorFlow 2.6 tabular model, GPU acceleration is generally not the primary latency bottleneck; tabular models are typically small and run efficiently on CPU. The container image choice is less likely to be the root cause than an incorrect artifact URI.

Answer

The model should be uploaded with a different display name

Answer

The container image used is CPU-only, but a GPU-accelerated image would improve latency

Answer

The model is uploaded to the wrong region