Generative AI Leader Timed Practice Test 2 — 10 Questions

Question 1

A manufacturing company wants to use GenAI to generate maintenance reports from sensor data. They need structured output (JSON) for downstream systems, and they want to reduce token costs. Which THREE strategies should they use?

10 minute time limit — choose an answer to begin.

Accepted Answer

Use batch API requests for multiple sensor readings. Structured output ensures JSON format; batch requests reduce cost; the smallest suitable model minimizes token usage. Few-shot adds tokens; caching may not help for diverse sensor data.

Answer

Use the largest available model to ensure accuracy

Answer

Include multiple few-shot examples of JSON in every prompt

Question 2

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.. Retrieval-Augmented Generation (RAG) grounds the model's responses in retrieved, authoritative documents at inference time, which directly reduces hallucination by supplying factual context the model can cite. It does not require retraining and can be updated as source documents change. This is the most effective and operationally practical mitigation for plausible-sounding but incorrect outputs.

Answer

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

Answer

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

Answer

Increase the temperature parameter to make the model more deterministic.

Question 3

A global corporation with 50,000 employees has seen rapid adoption of GenAI across marketing, product, and engineering teams. Each team selected its own models and cloud accounts, resulting in fragmented governance, unexpected costs, and varying output quality. The CFO demands a unified strategy to control costs and ensure consistency. The Chief AI Officer proposes several solutions. Which course of action best balances control with innovation?

Accepted Answer

Establish a GenAI Center of Excellence (CoE) that provides approved models, shared APIs, and best practices, while allowing team-specific customizations. A GenAI Center of Excellence (CoE) provides centralized governance through approved models and shared APIs, enabling cost control and quality consistency while preserving team-level flexibility for innovation. This balances the CFO's need for unified strategy with the CAIO's goal of avoiding rigid mandates that stifle experimentation.

Answer

Migrate all GenAI workloads to a single on-premises server to reduce cloud costs

Answer

Mandate all teams use a single model (e.g., Gemini) via a centralized Vertex AI endpoint with usage quotas

Answer

Allow teams to continue using their own models but require them to submit monthly cost reports

Question 4

A company has deployed a GenAI-powered report generation system using Vertex AI. They notice that the cost is higher than expected. Investigation shows that many requests include very long prompts with repetitive boilerplate text. Which cost optimization strategy is MOST effective?

Accepted Answer

Enable context caching for repeated prompt prefixes. Caching repeated prompt prefixes can significantly reduce token usage and cost. Batch requests help with throughput but not with per-request token savings. Reducing model size may hurt quality. Ignoring is not a strategy.

Answer

Increase the batch size for batch requests

Answer

Switch to a smaller model size

Answer

Ignore the cost increase as it will stabilize

Question 5

A developer needs to integrate a GenAI model into an existing customer relationship management (CRM) system. The CRM exposes REST APIs and runs on-premises. Which integration pattern is MOST suitable?

Accepted Answer

Implement an API-first integration by calling Vertex AI API from the CRM's backend. API-first integration using Vertex AI API allows any system with HTTP capabilities to call the GenAI model. Workspace add-ons and Apps Script are for Google Workspace only. Fine-tuning doesn't help with integration.

Answer

Use Apps Script to call the model from Google Sheets

Answer

Fine-tune a model on CRM data and deploy it on-premises

Answer

Build a custom Google Workspace add-on

Question 6

A legal firm wants to automate contract analysis to extract key clauses and risks. They have 10,000 contracts in PDF format. The solution must handle varying layouts and be cost-effective. Which approach is BEST?

Accepted Answer

Use Document AI to convert PDFs to structured text, then use Vertex AI with a prompt that specifies clauses to extract. Using Document AI to parse PDFs into text, then Vertex AI with a structured prompt for clause extraction combines robust document understanding with flexible GenAI. Fine-tuning on 100 contracts is insufficient for layout variation. Agent Builder is overkill. Direct Gemini API on raw PDFs loses document structure.

Answer

Build a retrieval-augmented generation (RAG) system in Vertex AI Agent Builder

Answer

Use the Gemini API directly with the raw PDF files as input

Answer

Fine-tune a Gemini model on 100 annotated contracts and run inference on all contracts

Question 7

Which THREE are valid methods to reduce bias in generative AI outputs?

Accepted Answer

Using a more diverse training dataset. Option C is correct because a more diverse training dataset reduces representational bias by exposing the model to a wider range of demographics, cultures, languages, and viewpoints, so the learned distribution is less skewed toward a dominant group. Option D is correct because safety filters (e.g., content moderation classifiers, toxicity detectors, or bias guardrails applied to prompts and outputs) can detect and block biased or harmful generations, mitigating bias at inference time. Option E is correct because prompt engineering that explicitly instructs the model to be fair, neutral, or inclusive (e.g., system prompts specifying balanced representation) steers generation toward less biased outputs without retraining. Option A is not correct because restricting prompts to English narrows the linguistic and cultural scope, which can amplify bias rather than reduce it. Option B is not correct because increasing model size alone does not remove bias and can even amplify biases present in the training data.

Answer

Using only English prompts

Answer

Increasing model size

Question 8

A company has been using an on-premises ML infrastructure for generative AI and wants to migrate to Google Cloud. They have a pipeline that fine-tunes a large language model weekly using a proprietary dataset. The migration must minimize downtime and data transfer costs. Which approach best addresses these requirements?

Accepted Answer

Use Vertex AI Pipelines to orchestrate the fine-tuning process, and use Vertex AI Managed Datasets to incrementally sync new data with BigQuery as the source.. Vertex AI Pipelines provides a managed, serverless orchestration service that can run the weekly fine-tuning workflow with minimal operational overhead, while Vertex AI Managed Datasets can incrementally sync new data from BigQuery, reducing data transfer costs by avoiding full dataset copies. This combination minimizes downtime because the pipeline can be triggered on a schedule without manual intervention, and incremental syncs avoid re-transferring the entire proprietary dataset each week.

Answer

Use AutoML to train a new model directly from the dataset without fine-tuning.

Answer

Deploy the existing pipeline on a Google Kubernetes Engine cluster and use Google Cloud Filestore for shared storage.

Answer

Use Cloud Storage Transfer Service to move all data to Cloud Storage, then set up a Vertex AI custom training job to run the fine-tuning.

Question 9

An organization wants to ensure that when their employees use the Gemini API via Vertex AI, the grounding searches are restricted to internal company knowledge bases rather than the public web. Which feature should they enable?

Accepted Answer

Vertex AI Search (with private data indexing). Vertex AI Search allows organizations to index private, internal data sources (e.g., documents, databases) and use them as the grounding source for Gemini API queries. By enabling this feature, grounding searches are restricted to the indexed private knowledge base, ensuring no public web results are used. This directly meets the requirement to keep grounding internal.

Answer

Enterprise Data Governance on Vertex AI

Answer

VPC Service Controls

Answer

Google Search Grounding

Question 10

A company wants to use a pre-trained language model for customer support summarization. They need to ensure responses are concise and accurate. Which prompt engineering technique is most effective?

Accepted Answer

Few-shot prompting with examples. Few-shot prompting (B) is most effective because it provides the model with a small set of example input-output pairs (e.g., a customer query and its concise summary), which guides the model to produce outputs that match the desired format, length, and accuracy. This technique is particularly useful for summarization tasks where consistency and adherence to a specific style are critical, as it reduces ambiguity without requiring fine-tuning.

Answer

Zero-shot prompting

Answer

Chain-of-thought prompting

Answer

Negative prompting