Generative AI Leader Practice Test 39 — 15 Questions

Question 1

A healthcare startup needs to process medical claims and extract structured data (e.g., patient name, procedure codes, amounts) from scanned PDF forms. They require HIPAA compliance and prefer a pre-built Google Cloud service. Which service should they use?

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

Accepted Answer

Document AI Healthcare API with the Claims processor. The Document AI Healthcare API with the Claims processor is a pre-built, HIPAA-compliant Google Cloud service specifically designed to extract structured data (e.g., patient name, procedure codes, amounts) from scanned medical claim forms. It leverages specialized machine learning models trained on healthcare documents, ensuring accurate parsing of fields like CPT codes and ICD-10 codes while meeting regulatory requirements.

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Cloud Vision API with OCR

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Vertex AI AutoML Tables

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Natural Language AI with entity extraction

Question 2

A financial services company uses a generative AI model to summarize customer complaints. They notice that summaries for certain demographics consistently omit negative sentiment. Which responsible AI practice should they apply FIRST to address this bias?

Accepted Answer

Evaluate the model's outputs for bias using a diverse test set that represents all customer demographics. Evaluating the model's outputs for bias using diverse test sets is essential to identify and mitigate unfair bias, as outlined in Google's AI Principles.

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Store all prompts and responses in Cloud Logging for auditing

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Implement SynthID watermarking on all generated summaries

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Reduce the temperature parameter of the LLM to 0.1 to make outputs more deterministic

Question 3

A company is considering whether to use Vertex AI's Generative AI Studio. Which TWO are benefits?

Accepted Answer

It integrates seamlessly with Vertex AI Pipelines for MLOps. Vertex AI Generative AI Studio is designed to work natively with Vertex AI Pipelines, enabling users to incorporate generative models into end-to-end MLOps workflows for automation, monitoring, and retraining. This integration allows seamless orchestration of prompt tuning, model evaluation, and deployment within the same managed environment, reducing operational overhead.

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It is always cheaper than using third-party APIs

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It generates outputs that are always more accurate than custom models

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It requires no coding or machine learning expertise to use

Question 4

A company is deploying a generative AI model for medical diagnosis support. Which THREE considerations are critical for responsible AI?

Accepted Answer

Ensure the training data is diverse and representative.. Diverse and representative training data is critical for responsible AI in medical diagnosis. If the data lacks diversity, the model may exhibit bias, leading to inaccurate or harmful diagnoses for underrepresented groups. This directly impacts fairness, safety, and regulatory compliance in healthcare AI.

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Maximize model throughput to handle high volumes.

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Use the cheapest model to reduce costs.

Question 5

A small business wants to use Vertex AI to analyze customer reviews and extract sentiment, product mentions, and overall themes. They have a small dataset of 500 reviews in a CSV file. The team is not experienced with machine learning and wants a pre-built solution that requires minimal coding. They want to start quickly and scale later. Which Google Cloud offering should they use?

Accepted Answer

Cloud Natural Language API for pre-trained sentiment and entity extraction.. Cloud Natural Language API provides pre-trained models for sentiment analysis and entity extraction, requiring minimal coding (just API calls) and no ML expertise. This aligns with the business's need for a quick, scalable, pre-built solution for their small dataset of 500 reviews, avoiding the overhead of custom training or complex prompting.

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Vertex AI Workbench to build a custom sentiment analysis model.

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AutoML Natural Language to train a custom model on their data.

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Vertex AI Gemini API with zero-shot prompting.

Question 6

A company's generative AI model is producing biased outputs. What is the most effective mitigation strategy?

Accepted Answer

Fine-tune the model using a balanced, representative dataset and implement output filtering. Fine-tuning on a balanced, representative dataset directly addresses the root cause of biased outputs by correcting the model's learned associations, while output filtering provides a safety net to catch residual bias. This combination is more effective than superficial fixes because it modifies the model's internal weights rather than just masking outputs.

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Use a larger model with more parameters to improve overall accuracy

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Use prompt engineering to instruct the model to avoid biased language

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Increase the diversity of input samples by random sampling

Question 7

Which TWO factors are most important when choosing a base foundation model for fine-tuning on a domain-specific task?

Accepted Answer

Model size and architecture. Model size and architecture directly determine the capacity for learning domain-specific patterns during fine-tuning. Larger models with more parameters can capture nuanced relationships, while architecture choices like transformer depth or attention mechanisms affect how well the model adapts to specialized tasks. For domain-specific fine-tuning, the base model must have sufficient representational power to avoid catastrophic forgetting and to generalize effectively within the target domain.

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Model popularity in the developer community

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Model license (open-source vs. proprietary)

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Inference latency of the base model

Question 8

You are a generative AI architect for a large e-commerce company. Your team has built a product description generator using Vertex AI's text-bison model. The model is accessed via the Vertex AI API from a web application. You have set the temperature to 0.5 and top_k to 40. The team reports that the generated descriptions are often too generic and lack creativity. They want the descriptions to be more diverse and engaging. You are also concerned about cost, as each API call is billed. Which change should you recommend to increase creativity while managing cost?

Accepted Answer

Increase the temperature to 0.8 and keep top_k at 40.. Increasing the temperature to 0.8 makes the model's output probability distribution flatter, which increases randomness and allows less likely tokens to be selected. This directly addresses the need for more diverse and creative descriptions. Keeping top_k at 40 ensures the model still considers a broad set of candidate tokens, balancing creativity with coherence, and does not increase API call costs since temperature and top_k are inference parameters that do not affect billing.

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Keep temperature at 0.5 but reduce top_k to 20.

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Switch to a larger model like text-bison@002 and keep same parameters.

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Decrease the temperature to 0.2 and increase top_k to 60.

Question 9

A marketing team wants to generate consistent brand-aligned social media posts using Vertex AI Studio. Which prompt engineering technique should they use to ensure the output tone matches their brand voice?

Accepted Answer

Provide a few-shot prompt with examples of previous brand-aligned posts. Few-shot examples provide the model with clear examples of desired tone, ensuring consistency. Zero-shot or chain-of-thought are less effective for tone adherence.

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Set the temperature to 0 and use a long system instruction

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Use a zero-shot prompt describing the brand voice

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Use chain-of-thought prompting to explain the reasoning behind each post

Question 10

A data scientist needs to fine-tune a foundation model for a sentiment analysis task without managing infrastructure. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Model Garden. Vertex AI Model Garden is the correct service because it provides a curated hub of foundation models that can be fine-tuned with managed infrastructure, eliminating the need for the data scientist to provision or manage servers. It supports one-click deployment and fine-tuning workflows for sentiment analysis, directly addressing the requirement to avoid infrastructure management.

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Compute Engine

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BigQuery ML

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Cloud Run

Question 11

An e-commerce company uses a generative AI model to generate marketing copy. They notice that the model occasionally produces off-brand or inappropriate content. What is the best way to mitigate this?

Accepted Answer

Fine-tune the model on brand guidelines. Fine-tuning the model on brand guidelines directly addresses the root cause of off-brand or inappropriate content by adjusting the model's weights to align with specific stylistic and content constraints. This supervised learning approach teaches the model the desired output patterns, making it inherently less likely to generate violations compared to post-hoc filtering or sampling adjustments.

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Reduce the model's temperature

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Increase the model's top-k sampling

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Use a safety filter

Question 12

A data scientist wants to quickly prototype a text generation application using Google's foundation models. Which Google Cloud service should they use?

Accepted Answer

Generative AI Studio. Generative AI Studio is the correct service because it provides a purpose-built environment for quickly prototyping and experimenting with Google's foundation models, including text generation models like PaLM 2 and Gemini. It offers a no-code interface and SDK access for rapid iteration, directly aligning with the data scientist's goal of fast prototyping without needing to manage infrastructure or training pipelines.

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Cloud Natural Language API

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Vertex AI Prediction

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AI Platform Training

Question 13

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.. Vertex AI Model Garden provides a curated repository of Google's foundation models, including PaLM 2, Gemini, and other first-party and third-party models, allowing users to discover, test, and deploy them directly from the Google Cloud console. This access is a core capability of Model Garden, enabling rapid experimentation with pre-trained models without requiring custom model building.

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Generate code snippets for common programming tasks.

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Ability to generate images from text descriptions.

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Deploy custom container images for model serving.

Question 14

A team deployed a fine-tuned model for code generation. After training, the model produces syntactically correct but functionally wrong code. What is the most likely cause?

Accepted Answer

Overfitting to training data. Overfitting to training data causes the model to memorize specific code patterns and syntax from the training set without learning the underlying logic or functional requirements. This results in syntactically correct outputs that fail to generalize to new, unseen coding tasks, producing functionally wrong code despite proper syntax.

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Incorrect prompt format

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Low temperature setting

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Insufficient training epochs

Question 15

A company is using a large language model for a customer-facing chat application. They notice that the model sometimes generates plausible-sounding but incorrect information. Which strategy is most effective to reduce this issue?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) to retrieve relevant documents before generating a response.. RAG grounds responses by retrieving relevant, verified information from a trusted source, reducing hallucinations. Fine-tuning on incorrect data would not help; increasing temperature increases randomness; reducing context window may lose relevant info.

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Fine-tune the model on a dataset of corrected conversations.

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Reduce the context window to limit the information the model considers.

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Increase the temperature parameter to make the model more deterministic.