Generative AI Leader Practice Exam 51 — 20 Questions

Question 1

An enterprise needs to generate natural-sounding speech from text for a voice assistant. They require low latency and support for custom voice models. Which service should they use?

Accepted Answer

Text-to-Speech API. The Text-to-Speech API (A) is correct because it is specifically designed to convert text into natural-sounding speech with low latency, and it supports custom voice models through features like Custom Voice and WaveNet voices. This directly meets the enterprise's requirements for a voice assistant that needs real-time, high-quality speech synthesis.

Answer

Cloud Translation API

Answer

Vertex AI Text Generation

Answer

Speech-to-Text API

Question 2

A data scientist notices that a Gemini model generates inconsistent responses to similar prompts. What is the likely cause?

Accepted Answer

The top_p or temperature parameters are set too high causing randomness. High temperature (e.g., >1.0) or high top_p (e.g., >0.9) increases the randomness of token sampling, causing the model to select less probable tokens. This directly leads to inconsistent responses for similar prompts, as the model's output distribution becomes more uniform and less deterministic.

Answer

Model is not fine-tuned enough

Answer

The prompt is too short

Answer

The temperature setting is too low

Question 3

A company wants to generate images from text descriptions using Google Cloud. Which service should they use?

Accepted Answer

Vertex AI Imagen. Vertex AI Imagen is Google Cloud's purpose-built service for generating high-fidelity images from text descriptions using diffusion models. It directly addresses the requirement of text-to-image generation, offering capabilities like image editing, upscaling, and style transfer, which are not available in other Vertex AI or Vision services.

Answer

Vertex AI Gemini

Answer

Cloud Vision API

Answer

AutoML Vision

Question 4

A project manager wants to measure the business impact of a GenAI code review tool. Which THREE metrics should they track to evaluate ROI? (Choose 3)

Accepted Answer

Developer satisfaction score. Developer satisfaction score (B) is a critical metric for GenAI code review ROI because it directly measures user adoption and perceived value. If developers find the tool frustrating or inaccurate, they will bypass it, negating any potential time savings or defect reduction. High satisfaction correlates with sustained usage, which is necessary for long-term return on investment.

Answer

Total tokens consumed by the GenAI model

Answer

Number of lines of code generated

Question 5

A team is using a generative AI model to create marketing copy. They want the responses to be more focused and less random. Which parameter should they adjust?

Accepted Answer

Decrease temperature. Decreasing the temperature parameter reduces the randomness of the model's token selection by lowering the probability of sampling less likely tokens. This makes the output more focused and deterministic, which is ideal for marketing copy that needs to stay on-brand and consistent.

Answer

Increase top-k

Answer

Decrease context window

Answer

Increase temperature

Question 6

Which feature in Vertex AI allows users to browse over 300 foundation models and deploy them with minimal code?

Accepted Answer

Vertex AI Model Garden. Vertex AI Model Garden, is correct because it provides a centralized repository where users can browse, discover, and deploy over 300 foundation models—including first-party, open-source, and third-party models—with minimal code. It abstracts away infrastructure complexity by offering pre-built deployment templates and one-click integration with Vertex AI endpoints, enabling rapid experimentation and production deployment without deep ML engineering overhead.

Answer

Vertex AI Studio

Answer

Vertex AI Agent Builder

Answer

Vertex AI Pipelines

Question 7

A company is using Vertex AI generative models for a high-volume text summarization service. Which two strategies can reduce operational costs?

Accepted Answer

Use batch prediction instead of online prediction.. Batch prediction reduces costs by processing multiple requests in a single batch job, which avoids the per-request overhead and idle compute time associated with online prediction. This is especially cost-effective for high-volume, non-real-time workloads like text summarization, as you pay only for the compute time used during the batch job rather than for each individual inference.

Answer

Increase the model's max output tokens to 2048.

Answer

Implement retry logic with exponential backoff.

Answer

Lower the temperature parameter to 0.

Question 8

A marketing team needs to generate personalized email campaigns for thousands of customers. They want to maintain brand tone consistency and avoid manual writing. Which GenAI approach is BEST suited?

Accepted Answer

Use Vertex AI Studio with prompt design and few-shot examples in the prompt. Vertex AI Studio enables prompt engineering with few-shot examples, allowing the team to generate personalized emails while maintaining brand tone consistency without fine-tuning or custom training. This approach leverages a pre-trained large language model (LLM) with carefully designed prompts that include brand guidelines and a few examples, ensuring output adheres to the desired style and context. It avoids the overhead of fine-tuning or building custom models, making it ideal for rapid deployment and iterative refinement.

Answer

Fine-tune a small model on brand guidelines only

Answer

Embed a rules-based template engine with no AI

Answer

Train a custom model from scratch on past campaigns

Question 9

A data scientist fine-tunes a large language model on Vertex AI but gets poor results on validation data. What is the most likely cause?

Accepted Answer

Insufficient training data. Fine-tuning a large language model on Vertex AI with poor validation results is most likely due to insufficient training data. Large language models have billions of parameters and require a substantial amount of high-quality, task-specific data to effectively adapt to a new domain or task; without enough examples, the model cannot learn the desired patterns and will perform poorly on unseen data.

Answer

Incorrect learning rate

Answer

Using wrong model family

Answer

Overfitting due to too many epochs

Question 10

A developer uses Vertex AI to generate code but the output is not syntactically correct. Which parameter should be adjusted?

Accepted Answer

temperature. Temperature controls the randomness of token selection during generation. A high temperature increases the likelihood of less probable tokens, which can lead to syntactically incorrect code. Lowering temperature makes the model more deterministic and conservative, favoring higher-probability tokens that are more likely to form valid syntax.

Answer

candidate_count

Answer

max_output_tokens

Answer

top_k

Question 11

A health-tech startup is fine-tuning a generative AI model on electronic health records (EHR) to assist in clinical decision support. They need to ensure responsible AI practices. Which THREE measures should they implement? (Select three.)

Accepted Answer

Evaluate model outputs for bias across different demographic groups. Option B is correct because systematically evaluating model outputs for bias across demographic groups (e.g., using disaggregated performance metrics like sensitivity, specificity, and equal opportunity difference by age, sex, race, and ethnicity) is essential in clinical AI to detect and mitigate health disparities before deployment. Option C is correct because requiring a human clinician to review all AI-generated recommendations preserves meaningful human oversight, keeping the clinician accountable for the final decision and preventing automation bias or unsafe autonomous actions in clinical decision support. Option D is correct because a Model Card documents the model's intended use, limitations, performance metrics, and ethical considerations, providing the transparency and traceability needed for responsible AI governance and regulatory review. Option A is not appropriate because fully automating all decisions removes human oversight and amplifies automation bias and accountability gaps, which is contrary to responsible AI in healthcare. Option E is not appropriate because training exclusively on data from a single hospital produces narrow, non-generalizable models and can encode site-specific biases, undermining fairness and robustness across diverse patient populations.

Answer

Automate all decisions to reduce human error

Answer

Train the model exclusively on data from a single hospital to ensure consistency

Question 12

A large e-commerce company is experiencing high costs for their generative AI product recommendation system. The system generates personalized product descriptions for millions of users daily. The team wants to reduce cost while maintaining quality. They are using a fine-tuned version of a large foundation model hosted on Vertex AI. The current cost is driven by the number of tokens processed. Which approach should they take?

Accepted Answer

Optimize prompts to generate shorter, more concise descriptions. Prompt engineering to reduce output length decreases token usage per request, directly lowering cost without model changes. Option B (switching to a larger model) increases cost. Option C (increasing batch size) may not reduce per-request cost. Option D (retraining with more data) does not affect inference cost.

Answer

Switch to a larger, more capable foundation model

Answer

Retrain the model with more product data to improve efficiency

Answer

Increase the batch size of inference requests

Question 13

A gaming company is using Vertex AI Imagen to create concept art. They have a stable pipeline that generates images based on text prompts. Recently, they introduced a new feature: using a reference image to guide the style (image-to-image generation). However, when using a reference image, the generated images often have unnatural color shifts and artifacts. The team suspects that the reference image is being resized to a resolution that the model wasn't trained on. They are using the default Imagen settings. What is the most likely cause and the best solution?

Accepted Answer

The reference image is being resized to a non-standard aspect ratio; preprocess the image to the recommended resolution and aspect ratio.. The default Imagen settings expect input images at specific resolutions (e.g., 256x256, 512x512, or 1024x1024) and a 1:1 aspect ratio. When a reference image is resized to a non-standard resolution or aspect ratio, the model's internal processing can introduce artifacts and unnatural color shifts due to misalignment with its training distribution. Preprocessing the image to the recommended resolution and aspect ratio ensures the model operates within its optimal input space, eliminating these issues.

Answer

Increase the number of inference steps to improve detail.

Answer

Reduce the style weight in the image-to-image prompt.

Answer

Switch to a different image generation model.

Question 14

What is the key advantage of using adapter-based fine-tuning methods like LoRA compared to full fine-tuning of a large language model?

Accepted Answer

LoRA significantly reduces the number of trainable parameters, making fine-tuning more memory-efficient. LoRA (Low-Rank Adaptation) injects trainable low-rank matrices into the transformer layers while keeping the original model weights frozen. This drastically reduces the number of trainable parameters (often by 10,000x), which lowers GPU memory requirements for storing optimizer states and gradients during training, making fine-tuning feasible on consumer hardware.

Answer

LoRA is faster at inference time compared to the fully fine-tuned model

Answer

LoRA eliminates the need for a base model

Answer

LoRA enables training on a larger dataset than full fine-tuning

Question 15

A large enterprise wants to deploy multiple generative AI models across different business units while ensuring cost governance and usage tracking. Which Google Cloud solution is best suited?

Accepted Answer

Deploy each model in a separate project with IAM policies. Deploying each model in a separate Google Cloud project with IAM policies provides the strongest isolation for cost governance and usage tracking. This approach ensures that each business unit's model usage is billed to its own project, enabling granular cost allocation and independent monitoring without cross-project interference. It also allows per-project budget alerts and usage quotas, directly addressing the enterprise's need for decentralized cost control.

Answer

Use Vertex AI Endpoint with monitoring

Answer

Implement a custom cost allocation using labels

Answer

Use Cloud Billing budgets and alerts per model

Question 16

A retail company plans to use Vertex AI's generative AI to create product descriptions. They need to ensure descriptions are factually accurate and do not misrepresent products. Which strategy should they prioritize?

Accepted Answer

Implement human-in-the-loop review. Human-in-the-loop (HITL) review is the correct strategy because it directly addresses the need for factual accuracy and prevention of misrepresentation. While generative AI can produce fluent text, it lacks a reliable grounding mechanism for product-specific facts, making human oversight essential to catch hallucinations, verify claims, and ensure compliance with advertising standards. This approach aligns with responsible AI practices and is a core recommendation for high-stakes content generation.

Answer

Use prompt engineering

Answer

Use a larger model

Answer

Increase temperature parameter

Question 17

A company is deploying a GenAI system that generates product descriptions. During A/B testing, the new system shows a 20% increase in click-through rate (CTR) but a 15% increase in average cost per query due to the model size. The team wants to optimize cost without sacrificing the CTR gain. Which THREE actions should they take? (Choose three.)

Accepted Answer

Batch similar requests to reduce per-request overhead. Batching similar requests reduces the per-request overhead by combining multiple inference calls into a single batch, which amortizes the fixed costs (e.g., model loading, token processing) across more outputs. This directly lowers the average cost per query while preserving the model architecture and CTR gains, as the model's output quality remains unchanged.

Answer

Use a larger model with higher accuracy to further increase CTR

Answer

Increase the number of few-shot examples in the prompt

Question 18

You are a generative AI lead at a healthcare startup developing a system to summarize patient medical records for quick review by doctors. The system uses a fine-tuned LLM. After deployment, doctors report that the summaries often miss critical details like medication dosages and allergy information. The current pipeline preprocesses patient records by extracting text from EHR, feeding it to the LLM, and outputting a summary. The team has limited time and budget. They cannot retrain the model because it is hosted as a managed API. Which action should you take to most effectively improve the summarization quality without changing the model?

Accepted Answer

Revise the prompt to explicitly ask for medication dosages and allergies, and format the input text by adding headings (e.g., '### Medications') to emphasize important sections.. Prompt engineering is the most effective and cost-efficient way to improve LLM output without retraining or changing the model. By explicitly instructing the model to include medication dosages and allergies, and by structuring the input with clear headings, you guide the model's attention to critical sections, directly addressing the missing details. This approach leverages the LLM's existing capabilities and requires no changes to the hosted API or additional infrastructure.

Answer

Increase the maximum output token limit to force the model to include more details.

Answer

Replace the LLM with a simpler extractive summarization model that selects sentences from the original document.

Answer

Implement a retrieval-augmented generation (RAG) system that pulls supplementary data from external drug databases.

Question 19

A retail company wants to integrate generative AI into its customer service chatbot to handle routine inquiries. They have a limited budget and want to launch quickly. Which strategy is most appropriate?

Accepted Answer

Use pre-trained models via Google Cloud's Generative AI Studio API. Using pre-trained models via Google Cloud's Generative AI Studio API allows the company to leverage existing, powerful models without the high cost and time investment of custom development or fine-tuning. This approach enables rapid deployment on a limited budget by simply integrating the API into their chatbot, handling routine inquiries effectively without requiring extensive machine learning expertise or infrastructure.

Answer

Partner with a generative AI vendor for a custom solution

Answer

Fine-tune an open-source model on their customer service logs

Answer

Build a custom LLM from scratch using the company's own data

Question 20

What is the primary purpose of Google's Datasheets for Datasets?

Accepted Answer

To document the dataset's creation, composition, and intended use. Datasheets for Datasets are designed to document the motivation, composition, collection process, and other details of a dataset to promote transparency and reproducibility.

Answer

To serve as a legal contract for data sharing

Answer

To list all models trained on the dataset

Answer

To provide a template for labeling data