Generative AI Leader Practice Test 2 — 30 Questions

Question 1

A security team wants to prevent prompt injection attacks on their generative AI application hosted on Vertex AI. Which best practice should they implement?

Accepted Answer

Implement input validation and output filtering. Prompt injection attacks exploit the model's inability to distinguish between user instructions and untrusted input. Implementing input validation (e.g., sanitizing special characters or known injection patterns) and output filtering (e.g., using a classifier to detect and block malicious responses) directly mitigates this risk by controlling what the model processes and returns. On Vertex AI, this can be enforced via custom safety attributes or integration with services like Cloud DLP for data loss prevention.

Answer

Use a custom model instead of a foundation model

Answer

Disable all logging

Answer

Use a private endpoint

Question 2

A data scientist is using the Vertex AI PaLM API for text generation. They notice that the model occasionally generates toxic content. Which parameter should they adjust to reduce the likelihood of toxic outputs?

Accepted Answer

safety_settings. Safety settings in the Vertex AI PaLM API allow you to configure thresholds for filtering harmful content categories (e.g., toxicity, harassment, hate speech). By adjusting these settings, you can block or reduce the likelihood of toxic outputs before they are returned, directly addressing the problem without altering the model's creativity or randomness.

Answer

max_output_tokens

Answer

temperature

Answer

top_k

Question 3

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 4

What is the key advantage of using vector search for retrieval in a RAG system compared to keyword search?

Accepted Answer

Vector search can find conceptually similar documents even without exact keyword matches. Vector search in a RAG system encodes documents and queries into dense vector embeddings using a foundation model, then retrieves documents based on semantic similarity in the embedding space. This allows it to find conceptually related documents even when they share no exact keywords with the query, overcoming the lexical gap that limits keyword search.

Answer

Vector search eliminates the need for a foundation model

Answer

Vector search is faster than keyword search

Answer

Vector search requires no preprocessing of documents

Question 5

A cloud architect is designing a generative AI pipeline that must comply with the EU AI Act for high-risk AI systems. Which of the following is a mandatory requirement under the Act?

Accepted Answer

The system must undergo a conformity assessment before deployment. Under the EU AI Act, high-risk AI systems must undergo a conformity assessment before deployment to ensure compliance with requirements such as risk management, data governance, and transparency. This is a mandatory procedural step, not a performance metric or specific reasoning technique. The assessment may involve self-evaluation or third-party review depending on the system's risk category.

Answer

The system must be explainable using chain-of-thought reasoning

Answer

The system must achieve a minimum accuracy of 90% on validation data

Answer

The system must be trained on data that is representative of the target population

Question 6

A company wants to generate marketing images for a new product launch using GenAI. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Imagen. Imagen on Vertex AI is Google Cloud's image generation model, available through Vertex AI APIs or Model Garden.

Answer

Gemini for Google Workspace

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 7

A company runs a pilot for a GenAI-powered internal knowledge base assistant. They want to measure adoption. Which metric is BEST for this purpose?

Accepted Answer

Number of unique users per week divided by total employees. The best metric for measuring adoption because it directly captures the breadth of usage across the organization. Adoption is defined as the proportion of the target user base that actively uses the system, and dividing unique weekly users by total employees provides a clear percentage of uptake, which is the standard measure for adoption in enterprise GenAI deployments.

Answer

Number of follow-up questions asked per session

Answer

User satisfaction score from surveys

Answer

Average response time of the assistant

Question 8

A financial services company uses a generative AI model to produce customer-facing investment advice. They need to ensure the model's outputs can be traced back to specific sources. Which explainability technique is BEST suited for this requirement?

Accepted Answer

Grounding (citing sources). Grounding (citing sources) is the best technique because it directly links each generated output to specific, verifiable source documents or data points. This ensures traceability, which is critical for regulated financial advice where every claim must be attributable to a known reference, such as a regulatory filing or market data feed.

Answer

Confidence indicators

Answer

Model Cards

Answer

Chain-of-thought reasoning

Question 9

A data science team wants to run a large-scale transformer training job with custom model architectures. They need the highest compute density for a multi-node job and want to minimize inter-node communication latency. Which Google Cloud infrastructure is BEST suited for this workload?

Accepted Answer

TPU v4 Pod slices with high-speed inter-chip interconnect. TPU v4 Pod slices provide the highest compute density for multi-node transformer training by using a custom 3D torus interconnect with 800 Gbps per chip bandwidth, which minimizes inter-node communication latency far below what standard networking can achieve. This architecture is specifically designed for large-scale model parallelism, making it ideal for custom transformer architectures that require frequent all-reduce and collective communication operations.

Answer

A single TPU v5e VM with multiple accelerators

Answer

A cluster of A100 GPU VMs connected via standard networking

Answer

Cloud Run jobs with GPU acceleration

Question 10

A startup wants to generate product descriptions from a few keywords using a large language model. They have no prior ML experience and need the fastest time-to-market. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Studio. Vertex AI Studio provides a no-code/low-code environment with pre-trained foundation models and prompt templates, enabling rapid generation of product descriptions from keywords without any ML expertise. It offers the fastest time-to-market because it eliminates the need for custom model training, infrastructure setup, or coding, directly leveraging Google's generative AI capabilities through a simple interface.

Answer

Vertex AI Workbench with custom training

Answer

Vertex AI Agent Builder

Answer

Vertex AI Model Garden

Question 11

A company is deploying a generative AI system for resume screening. They want to ensure fairness and avoid bias. Which TWO actions should they take? (Choose 2)

Accepted Answer

Evaluate the model's decisions across gender and ethnicity groups using a diverse test set. Option A is correct because fairness auditing requires measuring model outcomes across demographic subgroups (e.g., gender, ethnicity) using a diverse, representative test set, so disparate impact or bias can be detected and quantified before deployment. Option B is correct because if the training data is not representative of the candidate population, the model will learn skewed patterns and produce biased predictions, so ensuring representative training data is a foundational bias-mitigation step. Option C is incorrect because SynthID is a watermarking technique for identifying AI-generated content, not a fairness or bias-mitigation control for screening decisions. Option D is incorrect because simply removing protected attributes does not eliminate bias, since proxy variables and correlated features can still encode discriminatory patterns. Option E is incorrect because a larger model improves capacity or accuracy but does not inherently ensure fairness or reduce bias.

Answer

Apply SynthID watermarking to all generated decisions

Answer

Remove protected attributes like gender and race from training data

Answer

Use a larger model to improve accuracy

Question 12

A financial services firm is developing a GenAI application for investment advice. They need to ensure regulatory compliance. Which business strategy should they prioritize?

Accepted Answer

Implement strict human-in-the-loop review for all investment recommendations. In regulated industries like financial services, GenAI applications must prioritize compliance over speed. Option B is correct because a human-in-the-loop (HITL) review ensures that every investment recommendation is auditable and meets regulatory standards (e.g., SEC or FINRA rules), mitigating risks of hallucinated or non-compliant outputs. This strategy directly addresses the need for accountability and transparency in high-stakes decision-making.

Answer

Rapidly deploy an MVP and iterate based on user feedback

Answer

Open-source the model to gain community trust

Answer

Partner with a cloud provider that offers indemnification for model outputs

Question 13

Which TWO features are available in Vertex AI Studio for prompt engineering? (Choose two.)

Accepted Answer

Side-by-side comparison of model outputs. Vertex AI Studio provides a side-by-side comparison feature that allows prompt engineers to evaluate outputs from multiple model configurations or parameter settings simultaneously. This enables direct visual comparison of responses, helping to identify the most effective prompt phrasing or parameter combination without manual switching.

Answer

One-click deployment to a Vertex AI endpoint

Answer

Fine-tuning models directly in the interface

Answer

Building conversational agents with drag-and-drop

Question 14

Which TWO features are available in Vertex AI Agent Builder to enhance the conversational abilities of an agent? (Choose TWO.)

Accepted Answer

Slot filling. Slot filling is correct because it allows the agent to collect required parameters (slots) from the user during a conversation, enabling multi-turn interactions to fulfill complex requests. In Vertex AI Agent Builder, slot filling is a core feature for conversational agents, as it systematically prompts for missing information (e.g., date, location) until all necessary slots are filled, enhancing the agent's ability to handle dynamic user inputs.

Answer

Sentiment analysis

Answer

Code execution

Answer

Knowledge base integration

Question 15

A company uses a generative model to produce product descriptions. The descriptions are factually inconsistent with the product specs. Which technique would best ensure factual accuracy?

Accepted Answer

Implement retrieval-augmented generation (RAG) with product database. Retrieval-augmented generation (RAG) is the best technique because it dynamically retrieves relevant, up-to-date product specifications from a trusted database at inference time, grounding the model's output in verified facts. This directly addresses factual inconsistency by ensuring the generated description is based on authoritative source data rather than relying solely on the model's parametric memory.

Answer

Enhance the system prompt with product details

Answer

Lower the temperature to 0.0

Answer

Fine-tune the model on product descriptions

Question 16

Which TWO Google Cloud services can be used together to implement a RAG (retrieval-augmented generation) pipeline? (Select 2)

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search (option B) is correct because it provides a managed vector database for storing and querying embeddings, which is essential for the retrieval step in a RAG pipeline. It enables semantic similarity search over large datasets, allowing the system to fetch relevant context documents based on a user query.

Answer

Cloud SQL

Answer

Bigtable

Answer

Cloud Functions

Question 17

Refer to the exhibit. The team changed the generation parameters to reduce output variability. However, summaries now often repeat the same phrases. Which parameter change is most likely causing the repetition?

Accepted Answer

Reducing temperature from 0.7 to 0.2. Reducing temperature from 0.7 to 0.2 makes the model's token selection much more deterministic, favoring the highest-probability tokens. This low-entropy sampling often causes the model to repeat the same phrases across summaries because it consistently picks the most likely continuation.

Answer

Reducing top_p from 0.95 to 0.85

Answer

Using the same model text-bison@002

Answer

Reducing top_k from 40 to 10

Question 18

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.. Deploying the custom-tuned model to a Vertex AI endpoint with GPU acceleration and autoscaling is the best choice for lowest-latency real-time inference. A dedicated endpoint keeps the model loaded and ready to serve requests, avoiding the per-request startup overhead of serverless wrappers like Cloud Functions. GPU acceleration speeds up the model's forward-pass computation, and autoscaling adds or removes replicas to match traffic so that requests are not queued behind insufficient capacity. Batch prediction (D) is designed for high-throughput offline jobs and is not suitable for interactive, low-latency use cases.

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 19

A company uses a text-to-image model to generate marketing visuals. The results often misinterpret the prompt, e.g., 'a red car' generates a blue car. Which technique should they try first to align the output with the prompt?

Accepted Answer

Refine the prompt with more adjectives and context, e.g., 'bright red sports car'. Refining the prompt with more adjectives and context directly addresses the root cause of misalignment: insufficient specificity in the text description. Text-to-image models rely on the semantic richness of the prompt to guide the latent diffusion process; adding 'bright red sports car' provides stronger conditioning signals that steer the model's cross-attention layers toward the intended color and object attributes. This is the most efficient first step before adjusting hyperparameters like guidance scale.

Answer

Use a negative prompt to exclude blue

Answer

Upscale the image resolution to 1024x1024

Answer

Increase the guidance scale to 20

Question 20

Which Google Cloud service provides a managed environment for prompt engineering and model evaluation?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is the correct answer because it is a managed service within Vertex AI specifically designed for prompt engineering, model tuning, and evaluation of generative AI models. It provides a no-code interface for testing prompts, comparing model outputs, and iterating on prompt design, directly supporting the workflow described in the question.

Answer

AI Platform Notebooks

Answer

Dialogflow CX

Answer

Cloud Composer