Generative AI Leader Practice Test 5 — 10 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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