Generative AI Leader Practice Test 24 — 15 Questions

Question 1

A financial services firm is deploying a generative AI model to assist in loan approval decisions. To comply with regulatory requirements for fairness and explainability, which THREE actions should they take? (Choose 3)

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

Accepted Answer

Evaluate the model for bias using diverse test sets. Option C is correct because evaluating the model for bias using diverse test sets is essential to detect disparate impact across protected groups (e.g., race, gender, age) and satisfy fairness regulations such as ECOA and fair-lending requirements. Option D is correct because implementing chain-of-thought reasoning produces intermediate reasoning steps that make each loan decision traceable and explainable to regulators, auditors, and applicants, directly supporting explainability obligations. Option E is correct because a human-in-the-loop process with override capability ensures meaningful human review of consequential credit decisions, allowing qualified staff to correct erroneous or unfair AI recommendations and meet accountability requirements. Option A does not belong because SynthID watermarks only mark AI-generated content for provenance and do not address fairness or explainability of loan decisions. Option B does not belong because increasing model size may improve accuracy but does nothing to guarantee fairness or provide the required explanations, and larger models can even amplify opaque behavior.

Answer

Add SynthID watermarks to all model outputs

Answer

Increase the model size to improve accuracy

Question 2

A healthcare organization wants to deploy a GenAI chatbot for patient intake. They must ensure that the model does not generate harmful advice and that patient data remains confidential. Which set of controls should they implement on Vertex AI?

Accepted Answer

Enable safety filters, use VPC Service Controls, and enforce Private Google Access. Safety filters block harmful content; VPC Service Controls keep data within the customer's VPC; Private Google Access prevents data exfiltration. The other options lack these combined controls.

Answer

Use model tuning with only positive examples

Answer

Disable logging and restrict API keys

Answer

Use a public model endpoint and rely on prompt engineering

Question 3

A government agency is procuring a generative AI solution for public service information. They require that the system can provide explanations for its answers, protect citizen privacy, and comply with data residency laws. Which three capabilities should they mandate? (Choose three.)

Accepted Answer

Support for grounding with citations to source documents. Option C (Support for grounding with citations to source documents) is correct because grounding ties generated answers to verifiable source documents and returns citations, which directly satisfies the requirement that the system can explain the basis of its answers and lets citizens and auditors trace claims back to authoritative public-service content. Option D (Differential privacy during model training or fine-tuning) is correct because differential privacy adds calibrated noise so that the model learns statistical patterns without memorizing or leaking any individual citizen's data, which is the standard technical mechanism for protecting citizen privacy in AI systems. Option E (Configurable data residency controls to keep data within specific regions) is correct because data residency laws require that data be stored and processed within designated jurisdictions, and configurable regional controls (for example, pinning storage, training, and inference to in-country regions) are how that legal constraint is enforced. Option A (Integration with social media APIs) is not required for explainability, privacy, or residency and would in fact broaden data collection and privacy risk, so it does not belong. Option B (Ability to generate images from text descriptions) is an unrelated multimodal feature that addresses none of the stated requirements for explanations, privacy, or data residency.

Answer

Integration with social media APIs

Answer

Ability to generate images from text descriptions

Question 4

A company wants to build a GenAI application that summarizes customer reviews into actionable insights. The solution must be cost-effective and handle variable traffic. Which TWO strategies should they implement? (Choose 2)

Accepted Answer

Use a small, fine-tuned model for summarization. Option C is correct because a small, fine-tuned foundation model delivers adequate summarization quality for a narrow task like review summarization while consuming far fewer compute resources and costing less per inference than a large general-purpose model. Option D is correct because batching multiple review-summarization requests into a single inference call amortizes model loading and per-request overhead, improving throughput and cost-efficiency, which is essential for handling variable traffic. Option A is not appropriate because a dedicated GPU cluster is expensive and provides fixed capacity that does not scale economically with variable traffic. Option B is not appropriate because using the largest available foundation model increases cost and latency without guaranteeing better results for a simple summarization task. Option E is not appropriate because stuffing all raw reviews into the prompt context inflates token usage, raises cost, and can exceed context limits without improving summarization quality.

Answer

Deploy on a dedicated GPU cluster to minimize latency

Answer

Use the largest available foundation model for best accuracy

Answer

Store all raw reviews in the prompt context to avoid retrieval

Question 5

A financial institution is deploying a generative AI model to recommend investment strategies. They must ensure the model's outputs are explainable to clients and regulators. Which approach BEST meets this requirement?

Accepted Answer

Implement chain-of-thought prompting and cite sources for each recommendation. Explainability in GenAI can be achieved by grounding outputs in verifiable sources and showing the reasoning process. Chain-of-thought reasoning provides step-by-step logic, making recommendations auditable.

Answer

Apply differential privacy to the training data

Answer

Aggregate model outputs across multiple runs to reduce variance

Answer

Use a larger model with more parameters to improve accuracy

Question 6

An AI company wants to detect whether text was generated by their own model. Which technology developed by Google is specifically designed for this purpose?

Accepted Answer

SynthID. SynthID is Google DeepMind's watermarking technology that embeds imperceptible digital watermarks into AI-generated content (text, images, audio, video) so it can later be detected as AI-generated. It is specifically designed for identifying content produced by Google's generative models, matching the question's requirement.

Answer

Google's confidential computing

Answer

Differential privacy

Answer

Federated learning

Question 7

What is the primary purpose of SynthID, developed by Google DeepMind?

Accepted Answer

To embed an invisible watermark in AI-generated content for identification. SynthID is Google DeepMind's technology that embeds an imperceptible digital watermark directly into AI-generated content (images, audio, text, and video) so the content can later be identified as AI-generated. The watermark is designed to survive common transformations like compression, cropping, or editing. Its primary purpose is provenance and identification, not accuracy, safety filtering, or bias reduction.

Answer

To improve the factual accuracy of AI-generated text

Answer

To automatically filter harmful content in AI outputs

Answer

To reduce bias in training datasets

Question 8

A law firm wants to use generative AI to analyze contracts and extract key clauses. They need high accuracy and the ability to handle diverse contract formats. Which three steps should they take in their proof-of-concept (PoC) phase? (Choose THREE)

Accepted Answer

Use few-shot prompting with examples of desired clause extraction. Few-shot prompting provides the model with specific examples of desired clause extraction, guiding it to produce accurate outputs without requiring fine-tuning. This technique is efficient for a proof-of-concept because it leverages the model's existing capabilities while adapting to the task through in-context learning, which is critical for handling diverse contract formats with high accuracy.

Answer

Deploy a pre-built contract analysis API without customization

Answer

Train a custom model from scratch using all historical contracts

Question 9

Which Google service provides free access to Jupyter notebooks with GPU support for prototyping ML models?

Accepted Answer

Colab. Google Colab is a free, hosted Jupyter notebook environment that provides access to GPUs (and TPUs) for prototyping machine learning models without any setup. It integrates with Google Drive and allows users to write and run Python code in the browser. This directly matches the requirement for free Jupyter notebooks with GPU support.

Answer

Kaggle Notebooks

Answer

Vertex AI Workbench

Answer

BigQuery Studio

Question 10

A company is evaluating Google Cloud vs AWS Bedrock for building a multimodal application that needs to understand images, video, and text in a single model. Which unique Google Cloud capability supports this requirement?

Accepted Answer

Gemini API's native multimodal support across text, images, video, and audio. The Gemini API on Google Cloud offers native multimodal support, meaning a single model can process and reason across text, images, video, and audio without stitching together separate models. This is a distinguishing capability for building applications that must understand multiple modalities in one model. It directly satisfies the requirement for a multimodal application handling images, video, and text.

Answer

Use of Hugging Face models on Vertex AI

Answer

Integration with Amazon Bedrock's Titan model

Answer

BigQuery ML's image analysis functions

Question 11

Which THREE factors should be considered when choosing between a fine-tuned model and a prompted foundation model for a generative AI solution? (Select 3)

Accepted Answer

Need for domain-specific vocabulary. Fine-tuning allows the model to learn domain-specific vocabulary and terminology that may not be well-represented in the foundation model's pre-training data. This is critical for specialized fields like legal, medical, or technical domains where precise language is required for accurate outputs.

Answer

Inference latency requirements

Answer

Whether the model is open-source

Question 12

A data analyst wants to use Gemini in Google Sheets to help with complex formulas. Which feature should they use?

Accepted Answer

Gemini for Workspace in Google Sheets. Gemini for Workspace in Google Sheets provides an AI-powered side panel that can generate, explain, and debug complex formulas directly within the spreadsheet environment. This feature is specifically designed to assist with formula creation and data analysis tasks, making it the correct choice for a data analyst using Gemini in Google Sheets.

Answer

Model Garden in Vertex AI

Answer

Smart Compose in Gmail

Answer

Help me write in Google Docs

Question 13

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 14

Which Google DeepMind achievement is recognized for predicting protein structures and advancing drug discovery?

Accepted Answer

AlphaFold. AlphaFold is the correct answer because it is a groundbreaking AI system developed by Google DeepMind that predicts the 3D structure of proteins from their amino acid sequences with atomic-level accuracy. This achievement has revolutionized computational biology by solving a 50-year-old grand challenge in molecular biology, enabling significant advances in drug discovery, enzyme design, and understanding disease mechanisms.

Answer

AlphaGo

Answer

Gemini

Answer

AlphaCode

Question 15

A developer is using the Gemini API to build a chatbot. They want the model to always respond in a friendly, professional tone. Which prompt engineering technique should they use?

Accepted Answer

Set system instructions to 'You are a friendly and professional assistant.'. Setting system instructions is the most direct and reliable way to define the model's persona and behavioral constraints. In the Gemini API, system instructions act as a persistent, top-level directive that influences every response, ensuring the chatbot consistently adopts a friendly and professional tone without requiring repeated examples or parameter tuning.

Answer

Include a few-shot example in every user message.

Answer

Set the temperature to 0.2.

Answer

Set max output tokens to 100.