Generative AI Leader Practice Test 38 — 15 Questions

Question 1

A company uses a generative AI model to generate product descriptions. They notice variations in style and length across products. How can they enforce consistent formatting?

Accepted Answer

Set a system instruction specifying style and structure.. Setting a system instruction explicitly defines the desired style, tone, and structure for the model's output. This is a fundamental technique in prompt engineering, particularly with instruction-tuned models like GPT-4 or Claude, where the system message acts as a persistent directive that overrides the model's default behavior, ensuring consistent formatting across all generated product descriptions.

Answer

Adjust top-k sampling to include more token candidates.

Answer

Randomly select few-shot examples from a pool of descriptions.

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Use a high temperature and vary the prompt slightly.

Question 2

A financial institution needs to deploy a document processing pipeline to extract data from invoices. They must ensure data isolation, audit logging, and compliance with SOC 2. Which THREE Google Cloud services should they combine?

Accepted Answer

Document AI. Document AI (Option A) is correct because it is purpose-built for extracting structured data from documents like invoices using pre-trained models (e.g., the Invoice Parser), enabling accurate key-value pair extraction. Cloud Audit Logs (Option C) is correct because it provides immutable, tamper-evident logs of all API calls and administrative actions, which is essential for SOC 2 compliance and audit trails. Vertex AI (Option D) is correct because it can host custom machine learning models for post-processing or validation of extracted data, and its integration with Cloud Audit Logs ensures data isolation and governance within a single tenant project.

Answer

Cloud Vision API

Answer

BigQuery

Question 3

A company has a Gemini-based application that sometimes produces factually incorrect answers. They want to improve accuracy without retraining the model. Which technique should they implement?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a curated knowledge base. Retrieval-Augmented Generation (RAG) is the correct technique because it grounds the model's responses in a curated, external knowledge base, providing factual context that reduces hallucinations without modifying the model's weights. This directly addresses the need for improved accuracy in a Gemini-based application while avoiding costly retraining.

Answer

Reduce the top-k value to 10

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Use prompt engineering to instruct the model to 'be more accurate'

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Increase the temperature to 1.0 for more diverse outputs

Question 4

A company has a large dataset of customer support tickets stored in BigQuery. They want to predict ticket severity (high, medium, low) using SQL queries without moving data out of BigQuery. Which service should they use?

Accepted Answer

BigQuery ML. BigQuery ML (D) is correct because it allows users to create and execute machine learning models directly within BigQuery using SQL, without moving data out of the warehouse. For a classification task like predicting ticket severity, BigQuery ML supports models such as logistic regression, boosted trees, and deep neural networks, all trained and deployed using standard SQL queries on data already in BigQuery.

Answer

Vertex AI AutoML Natural Language

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Cloud Natural Language API

Answer

Gemini API

Question 5

A data scientist wants to compare the performance of three different foundation models for a text summarization task. They have a labeled dataset of summaries. Which Vertex AI tool should they use to perform this evaluation?

Accepted Answer

Vertex AI Studio - Evaluation. Vertex AI Studio includes a built-in Evaluation feature that lets you compare foundation models side-by-side using your own labeled dataset, computing metrics like ROUGE, BLEU, and human-preference scores for tasks such as summarization. It is purpose-built for evaluating generative model outputs against ground-truth references, which matches the data scientist's need exactly.

Answer

Vertex AI RAG Engine - Retrieval evaluation

Answer

Vertex AI Agent Builder - Agent evaluation

Answer

Model Garden - Model comparison view

Question 6

A team is using a language model for customer feedback analysis. They want to improve the accuracy of sentiment extraction. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Provide few-shot examples of correctly labeled sentiment in the prompt.. Two complementary techniques improve sentiment extraction accuracy. First, providing few-shot examples of correctly labeled sentiment in the prompt guides the model through in-context learning, clarifying the desired output format and classification boundaries without retraining. Second, fine-tuning the model on a labeled dataset of customer feedback adapts the model's weights to the specific domain and label distribution, yielding more accurate and consistent sentiment classification than prompting alone. Increasing temperature, using a generic built-in sentiment API, or enforcing JSON output format do not directly improve classification accuracy.

Answer

Increase the temperature to 0.8 to allow more creative interpretations.

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Use the model's built-in sentiment analysis API instead of prompting.

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Add a system instruction that asks the model to strictly follow JSON output format.

Question 7

A media company wants to build a multi-modal generative app that accepts text, image, and video inputs and produces summaries. The app must handle variable-length videos up to 10 minutes. Which architecture is most scalable and cost-effective?

Accepted Answer

Use a pipeline to split videos into short clips, extract key frames, and process with Gemini 1.5 Pro (with context caching) to generate summaries.. Option A is the most scalable and cost-effective because it preprocesses variable-length videos by splitting them into short clips and extracting key frames, reducing token usage and computational load. Gemini 1.5 Pro's context caching further improves efficiency by reusing processed context across requests. Option B loses visual context and adds an extra API, reducing accuracy and scalability. Option C discards multi-modal information entirely. Option D is less practical for variable-length videos because directly ingesting raw video into a single endpoint without preprocessing can lead to high token costs and latency, especially for videos up to 10 minutes.

Answer

Use Video Intelligence API to generate video captions, then feed captions to a text model.

Answer

Convert all inputs to text descriptions and use a text-only model.

Answer

Deploy a single Vertex AI endpoint with a model that can ingest multi-modal data directly.

Question 8

A company uses a text-to-image model to generate marketing visuals. The outputs often contain distorted human faces. Which technique is most likely to improve face generation?

Accepted Answer

Fine-tune the model on a curated dataset of human faces. Fine-tuning the model on a high-quality dataset of human faces directly addresses the distortion issue by specializing the model for face generation. Option B (increasing output resolution) may improve overall image sharpness but does not specifically correct face distortions. Option C (increasing inference steps) can enhance image coherence but is not targeted at face quality. Option D (reducing classifier-free guidance scale) decreases prompt adherence, which could actually worsen face generation rather than improve it.

Answer

Increase the output resolution

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Increase the number of inference steps

Answer

Reduce the classifier-free guidance scale

Question 9

A company wants to build a GenAI-powered customer support chatbot. They require the chatbot to provide accurate answers based on the latest product documentation, and they need to control costs by minimizing token usage. Which TWO strategies should they use?

Accepted Answer

Cache frequent queries and their responses. Option B is correct because caching frequent queries and their responses avoids re-invoking the model for identical requests, directly reducing token consumption and cost for repeated customer questions. Option C is correct because Retrieval-Augmented Generation (RAG) retrieves only the most relevant document chunks from the latest product documentation and injects just those into the prompt, ensuring accurate, up-to-date answers while keeping the token payload small. Option A is not appropriate because using the largest model increases cost per token without addressing retrieval of current documentation. Option D is not appropriate because stuffing entire documents into a long context window inflates token usage, contradicting the cost-minimization requirement. Option E is not appropriate because fine-tuning on documentation is expensive, must be repeated as docs change, and does not inherently minimize per-query token usage.

Answer

Use the largest available model for maximum accuracy

Answer

Use a longer context window to include entire documents in the prompt

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Fine-tune a large language model on the product documentation

Question 10

A company is using a fine-tuned LLM for generating financial reports. They need to ensure that the output complies with regulatory standards and does not include speculative content. Which combination of techniques should they implement?

Accepted Answer

Fine-tune the model on historical compliant reports, use RAG with a regulatory database, and implement a human-in-the-loop review.. Fine-tuning on historical compliant reports ensures the model learns from past regulatory requirements, RAG with a regulatory database provides up-to-date compliance information, and human-in-the-loop review adds a final verification layer to catch any non-compliant or speculative content.

Option A (safety settings, low top-p, limit tokens) may reduce harmful content but does not guarantee regulatory compliance. Option C (larger model) alone does not enforce specific regulations. Option D (system instruction, temperature 0.0, keyword filter) is insufficient for complex regulatory standards.

Answer

Increase the model's safety settings to maximum, use a low top-p value, and limit output tokens.

Answer

Use a larger model with more parameters and rely on its inherent knowledge.

Answer

Use a system instruction to adhere to regulations, set temperature to 0.0, and apply a keyword filter.

Question 11

A chatbot built with Vertex AI PaLM API often provides outdated information about company policies because the training data is months old. Which approach should the team use?

Accepted Answer

Implement grounding by connecting to a knowledge base of current policies.. Grounding connects the PaLM API to a live, authoritative knowledge base (e.g., Cloud Storage, BigQuery, or Vertex AI Search) containing the latest company policies. This allows the model to retrieve and cite current information at inference time without retraining, directly solving the staleness issue. Grounding is the recommended approach in Vertex AI for ensuring factual, up-to-date responses from a foundation model.

Answer

Use prompt engineering to instruct the model to say 'I don't know' if unsure.

Answer

Increase the context window to include more history.

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Fine-tune the model on the latest policy documents.

Question 12

Which THREE techniques are commonly used to improve the overall quality and coherence of generative model outputs? (Choose three.)

Accepted Answer

Using self-consistency or iterative refinement to choose the best output.. Self-consistency (A) improves quality by generating multiple candidate outputs from the same prompt and selecting the most consistent or frequent answer, reducing variance and errors, and iterative refinement lets the model revise its own output via feedback loops. In-context learning (B) with relevant few-shot examples steers the model toward the desired format, style, and reasoning pattern, improving output quality without retraining. Prompt chaining (D) decomposes a complex task into simpler sub-tasks, so each step is handled with a focused prompt and yields more coherent final outputs. By contrast, output safety filters (C) address appropriateness rather than quality or coherence, and random sampling (E) increases diversity but can reduce coherence, so neither is a quality-improvement technique.

Answer

Applying output safety filters to remove inappropriate content.

Answer

Random sampling to increase output diversity.

Question 13

A data scientist fine-tunes a model on a small proprietary dataset. After fine-tuning, the model repeats training examples verbatim. What is the most effective mitigation?

Accepted Answer

Add regularization like dropout and use a smaller learning rate.. Overfitting on a small dataset causes the model to memorize training examples rather than generalize. Adding dropout introduces noise that forces the model to learn more robust features, while a smaller learning rate prevents the model from over-optimizing on the limited data. Together, these regularization techniques reduce the model's capacity to memorize verbatim outputs.

Answer

Reduce the temperature during inference to 0.

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Train for more epochs to improve generalization.

Answer

Use early stopping based on validation loss.

Question 14

What is the primary benefit of using foundation models (like Gemini) as opposed to training a model from scratch?

Accepted Answer

They require less data and compute to adapt to new tasks. Foundation models like Gemini are pre-trained on vast datasets, capturing general language understanding and patterns. Adapting them to new tasks via fine-tuning requires significantly less task-specific data and computational resources compared to training a model from scratch, which demands enormous datasets and compute for initial training. This transfer learning approach is the primary benefit, enabling efficient customization for specialized applications.

Answer

They are always faster at inference

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They guarantee 100% accuracy on all tasks

Answer

They are open-source and free to use

Question 15

A marketing team wants to generate social media posts from product descriptions using Generative AI. They need consistent brand tone and the ability to iterate quickly. Which tool is BEST suited for this task?

Accepted Answer

Vertex AI Studio. Vertex AI Studio is the correct choice because it provides a purpose-built environment for prompt engineering, model tuning, and rapid iteration with foundation models. It allows the marketing team to experiment with prompts, adjust parameters like temperature and top-p, and maintain consistent brand tone through saved prompt templates and versioning, directly supporting the need for quick iteration.

Answer

Duet AI in Google Docs

Answer

Vertex AI Model Garden

Answer

Vertex AI Agent Builder