Generative AI Leader Practice Exam 53 — 20 Questions

Question 1

A machine learning engineer is fine-tuning a large language model using LoRA (Low-Rank Adaptation) to reduce memory usage. During training, they notice that the model's performance on the downstream task is not improving. What is the most likely issue?

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

Accepted Answer

The rank r of the LoRA adapter is too low to capture the task complexity. When fine-tuning with LoRA, the rank r determines the expressiveness of the low-rank adaptation matrices. If r is too low, the adapter lacks the capacity to learn the necessary task-specific features, causing the model's performance to stagnate. This is the most likely issue because the engineer observes no improvement, indicating the adapter's representational power is insufficient for the downstream task complexity.

Answer

The learning rate is too high, causing the loss to oscillate

Answer

The LoRA adapter is applied to all layers, which slows convergence

Answer

The base model is too large for the dataset, causing overfitting

Question 2

An organization is deploying a summarization model on Vertex AI and needs to ensure that the model's responses are consistent and avoid hallucinations. They have a labeled dataset of source documents and human-written summaries. Which approach would best align the model with their quality requirements?

Accepted Answer

Perform supervised fine-tuning using their labeled dataset. Supervised fine-tuning (D) directly optimizes the model's weights using the labeled dataset of source documents and human-written summaries, which teaches the model to produce consistent, factual outputs and reduces hallucinations. This approach aligns the model's behavior with the specific quality requirements by learning from ground-truth examples, unlike prompt engineering or parameter adjustments that do not modify the underlying model.

Answer

Deploy the model with a larger max_output_tokens

Answer

Use prompt engineering with few-shot examples

Answer

Increase the temperature to 0.9

Question 3

An engineer is testing a generative AI application using the Gemini API. They receive a 400 error with message 'INVALID_ARGUMENT: text has been blocked.' What is the most likely cause?

Accepted Answer

The input text was flagged by a safety filter. The 400 error with 'INVALID_ARGUMENT: text has been blocked' indicates that the input text was rejected by Gemini's safety filters before any model processing occurred. These filters evaluate content against categories like hate speech, harassment, or sexually explicit material, and when triggered, they block the request at the API gateway level, returning this specific error message.

Answer

The specified Gemini model version does not exist

Answer

The API key is invalid

Answer

The API quota has been exceeded

Question 4

A machine learning engineer is defining a Vertex AI pipeline for model evaluation using the JSON representation shown. The pipeline fails with an error that the 'eval_dataset' parameter is missing. What is the issue?

Accepted Answer

The pipeline spec does not declare 'eval_dataset' as a pipeline input parameter. The pipeline fails because the JSON representation of the Vertex AI pipeline does not include 'eval_dataset' in the `pipelineSpec.root.inputDefinitions.parameters` section. Without declaring it as a pipeline input parameter, the pipeline runtime cannot resolve the reference to `inputs.eval_dataset` in the component's arguments, causing the missing parameter error.

Answer

The component 'comp-model-eval' does not accept 'eval_dataset' as input

Answer

The 'project' parameter should be a pipeline input, not a constant

Answer

The runtimeConfig parameter values must be strings, not references

Question 5

A healthcare company is building a chatbot to answer patient queries using Vertex AI Agent Builder. They want to ensure the chatbot only uses approved medical references and does not generate unverified advice. How should they configure the agent?

Accepted Answer

Set up grounding with a private data store containing verified medical documents. Vertex AI Agent Builder supports grounding with private data stores, which allows the agent to restrict its responses to only the information contained in the verified medical documents. This ensures the chatbot does not generate unverified advice by grounding its outputs in a trusted, curated dataset rather than relying on the model's general training data.

Answer

Enable strict safety filters to block any medical advice

Answer

Increase the temperature parameter to get more diverse responses

Answer

Use Vertex AI Model Monitoring to track answer accuracy

Question 6

A company uses Vertex AI PaLM for code generation. The code often contains security vulnerabilities. Which improvement should be applied?

Accepted Answer

Include a security-focused system instruction. Including a security-focused system instruction directly guides the model to prioritize secure coding practices, such as input validation and proper error handling, reducing vulnerabilities. This leverages prompt engineering to shape model behavior without altering parameters like temperature or top_k, which control randomness, not security awareness.

Answer

Set top_k to 1

Answer

Use Codey model instead

Answer

Increase temperature to 0.8

Question 7

A data scientist is evaluating the output quality of a text generation model. They observe that the model often repeats phrases and produces very generic responses. Which THREE parameter adjustments could help increase diversity and reduce repetition? (Choose three.)

Accepted Answer

Increase the top-k value from 40 to 100. Increasing the top-k value from 40 to 100 (option A) widens the candidate token pool at each generation step, so the model is no longer restricted to only the 40 most probable tokens and can select from a broader, more varied set, which reduces repetitive and generic phrasing. Increasing the temperature from 0.5 to 0.9 (option B) flattens the softmax probability distribution, giving lower-probability tokens a greater chance of being sampled and thereby producing more diverse, less deterministic text. Increasing the top-p value from 0.8 to 0.95 (option D) expands nucleus sampling to include tokens cumulatively covering 95% of the probability mass instead of 80%, again enlarging the sampling pool and lowering repetition. Option C (decreasing max output tokens from 1024 to 512) only shortens the response length and does not change the sampling distribution, so it does not increase diversity. Option E (decreasing the frequency penalty to 0.0) removes or weakens the penalty that discourages repeated tokens, which would tend to increase repetition rather than reduce it.

Answer

Decrease the max output tokens from 1024 to 512

Answer

Decrease the frequency penalty to 0.0

Question 8

A team is using a generative AI model to create summaries of customer feedback. The summaries are often too long and include unnecessary details. The team wants to make the summaries more concise without losing key information. Which technique should they use?

Accepted Answer

Use prompt engineering to specify a maximum word count for the summaries.. Prompt engineering with a specified word limit is a straightforward way to control the length of generated summaries. By instructing the model to be concise and setting a maximum word count, the team can achieve shorter outputs that still capture essential information. This approach is flexible and can be adjusted easily without model retraining.

Answer

Increase the temperature parameter to encourage brevity.

Answer

Reduce the model's top-k parameter to limit vocabulary diversity.

Answer

Fine-tune the model on a dataset of short summaries.

Question 9

A software team is using a generative AI model to write code snippets. They want to control the model's creativity and ensure it produces consistent, deterministic output for the same prompt. Which parameter should they adjust?

Accepted Answer

Temperature. Temperature controls the randomness of token selection. Setting it to zero makes the model choose the highest-probability token each time, yielding deterministic output for the same prompt. Top-p, top-k, and max tokens do not provide the same level of determinism.

Answer

Top-p (nucleus sampling)

Answer

Top-k sampling

Answer

Maximum output tokens

Question 10

A media company is using a generative AI model to create short video scripts. They notice that the model sometimes produces content that is factually incorrect or nonsensical. Which term best describes this phenomenon?

Accepted Answer

Hallucination. Hallucination is the term for generative AI producing incorrect or nonsensical information that appears confident. In this scenario, the model's factually incorrect script content is a direct example. Other options like overfitting, bias, or underfitting describe different issues and do not capture the specific problem of generating false but plausible text.

Answer

Bias

Answer

Underfitting

Answer

Overfitting

Question 11

A retail company wants to use a generative AI model to create unique product descriptions for thousands of items. They need the model to produce varied, human-like text without being explicitly programmed for each product. Which core capability of generative AI does this scenario primarily rely on?

Accepted Answer

Generating novel content based on patterns learned from training data. Generative AI is designed to create new content by learning patterns from existing data. In this scenario, the model generates unique product descriptions without explicit programming, which is the essence of generative AI. The other options describe discriminative or predictive tasks that do not produce novel text, so they fail to meet the company's need for varied, human-like descriptions.

Answer

Forecasting future sales trends

Answer

Clustering similar products based on features

Answer

Classifying products into predefined categories

Question 12

A mid-size retail company wants to launch a generative AI assistant that drafts promotional product descriptions for its marketing team. The team expects a rapid pilot, but the CIO insists that any generated text must never expose the company's unreleased product roadmap or pricing data that may exist in internal documents. The company has no dedicated AI engineering staff and prefers a managed approach on Google Cloud. Which strategy best balances rapid pilot delivery with this data-exposure requirement?

Accepted Answer

Use a managed generative AI API with grounding restricted to an approved, curated product catalog and clear system instructions limiting the assistant to that content.. The scenario couples a fast, low-skill pilot with a hard boundary around sensitive internal content. A fully managed generative AI API eliminates infrastructure and model-tuning overhead, while grounding limited to an approved catalog plus explicit system instructions confines generation to vetted material, preventing unreleased roadmap and pricing data from appearing in drafts. This combination delivers speed without weakening the data-exposure control the CIO demanded.

Answer

Build a custom large language model from scratch using the company's historical marketing copy as the sole training corpus.

Answer

Deploy an open-weight model on a Compute Engine VM and allow the assistant to search the entire shared drive so the marketing team gets the most complete answers.

Answer

Fine-tune a foundational model on all internal product documents so the assistant learns the company's writing style and terminology.

Question 13

A developer is building a customer support chatbot using Gemini models on Vertex AI. The chatbot must respond in a consistent, professional tone and avoid generating harmful or off-topic content. Which approach should the developer take to enforce these behavioral constraints?

Accepted Answer

Use a system instruction to define the chatbot's role and guidelines.. System instructions are the correct mechanism to set persistent behavioral guidelines for a generative AI model. They are processed before user input and apply to the entire conversation, ensuring the chatbot maintains a professional tone and avoids harmful or off-topic content. Other parameters like temperature or top-k affect creativity, not adherence to rules.

Answer

Increase the top-k parameter to 40.

Answer

Enable the model's safety filters to block all categories.

Answer

Set the temperature parameter to 0.

Question 14

A retail company wants to build an internal assistant that answers employee HR policy questions using their existing policy documents, without exposing sensitive data to external APIs. They need a managed Google Cloud service that supports retrieval-augmented generation (RAG) and integrates with their existing data in Cloud Storage. Which Google Cloud service should they use?

Accepted Answer

Vertex AI Search and Conversation. Vertex AI Search and Conversation is the only Google Cloud service listed that provides a managed RAG solution for building conversational assistants grounded in enterprise documents. It securely integrates with Cloud Storage and other data sources, enabling the company to deploy an internal HR assistant without exposing sensitive data externally.

Answer

Document AI

Answer

Dialogflow CX

Answer

Cloud Natural Language API

Question 15

A healthcare organization wants to build a generative AI application that summarizes patient notes. They are concerned about the model generating harmful or inappropriate content. Which Google Cloud service should they use to filter out such content in real time?

Accepted Answer

Vertex AI Safety Filters. Vertex AI Safety Filters are specifically built to detect and filter harmful content in generative AI outputs. For a healthcare application summarizing patient notes, using these filters ensures that inappropriate or harmful content is blocked in real time. Other services like Model Garden, Search, or Pipelines serve different purposes and do not provide content moderation.

Answer

Vertex AI Pipelines

Answer

Vertex AI Search

Answer

Vertex AI Model Garden

Question 16

A marketing team wants to use a generative AI model to create product descriptions from a short list of features. They need the output to be creative but also follow a specific brand voice. Which approach should they take?

Accepted Answer

Use prompt engineering with a foundation model, providing examples of the desired tone and style.. Prompt engineering is the most suitable approach because it allows the team to specify the desired tone, style, and content directly in the prompt. By providing examples and clear instructions, they can steer the foundation model to produce creative yet on-brand descriptions. This method is fast, cost-effective, and requires no model training.

Answer

Fine-tune a foundation model on a large dataset of existing product descriptions.

Answer

Deploy the model without any customization and rely on its default output.

Answer

Train a custom model from scratch using the team's product catalog.

Question 17

A logistics company plans to embed a generative AI assistant into its dispatch workflow, where it will draft driver instructions and answer operations questions. The executive sponsor insists that the initiative include mechanisms to measure whether the assistant is delivering business value and to keep its behavior within approved limits as usage grows. (Choose two.)

Accepted Answer

Establish an operating model with human review thresholds, escalation paths, and periodic prompt and policy updates as usage expands.. Value measurement and behavioral control are distinct governance obligations. Business-aligned metrics with recurring reviews show whether the dispatch assistant is producing tangible benefit, while an operating model with human review thresholds, escalation paths, and periodic prompt and policy updates keeps outputs within approved limits as volume grows. Together they satisfy the sponsor's two requirements.

Answer

Increase the foundation model's temperature setting so the assistant produces more varied dispatch instructions over time.

Answer

Move all dispatch assistant traffic to a single region to simplify network routing and reduce egress charges.

Answer

Purchase the largest available context window model so the assistant can accept longer operations queries.

Question 18

A regional insurance company wants to launch a generative AI assistant that drafts policyholder responses for its claims team. Before any code is written, the CIO asks the team to produce a document that articulates the intended business outcome, the target user group, the success metrics, and the boundaries of what the assistant may and may not do. Which artifact best matches this request?

Accepted Answer

A generative AI use case definition that states the business objective, target users, measurable success criteria, and explicit scope boundaries.. A generative AI initiative should begin with a use case definition that ties the technology to a business outcome, names the users it serves, defines how success will be measured, and sets explicit boundaries. This keeps the claims-drafting assistant aligned to measurable value and gives later technical and governance work a stable reference point before any build activity begins.

Answer

A Vertex AI model card documenting the training data, evaluation results, and known limitations of the underlying foundation model.

Answer

A service-level objective document specifying latency percentiles and uptime targets for the assistant's API endpoint.

Answer

A cloud architecture diagram showing the assistant's front end, API layer, retrieval store, and model endpoint.

Question 19

A marketing team is using Vertex AI's text generation model to create product descriptions. They want to control the randomness of the output to ensure consistent, focused messaging for a new product line. Which parameter should they adjust to reduce randomness and make the output more deterministic?

Accepted Answer

Temperature. Temperature is the parameter that directly controls the randomness of the model's output. Lowering the temperature makes the model more likely to choose high-probability tokens, resulting in more deterministic and consistent text. This is exactly what the marketing team needs for focused product descriptions.

Answer

Max output tokens

Answer

Top-k

Answer

Top-p

Question 20

A marketing team is using a generative AI model to create ad copy. They want to ensure the outputs align with their brand voice and avoid offensive language. Which two practices should they adopt? (Choose two.)

Accepted Answer

Provide clear instructions and examples in the prompt to guide the model's tone and style.. To align outputs with brand voice and avoid offensive language, the team should use prompt engineering to guide tone and style, and implement Vertex AI Safety Filters to block harmful content. These two practices work together to ensure both stylistic and safety requirements are met. Other options either introduce risks or are inefficient.

Answer

Set the model's temperature to the maximum value to encourage creativity.

Answer

Deploy multiple model versions and manually select the best output for each ad.

Answer

Fine-tune the model on a large dataset of competitor ads.