Generative AI Leader Practice Test 36 — 15 Questions

Question 1

A data scientist notices that a text generation model deployed on Vertex AI returns repetitive outputs after a few turns in a chat application. What is the most likely cause and the best parameter adjustment?

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

Accepted Answer

The top_p value is too high; reduce top_p to limit token sampling.. Repetitive outputs in a chat application after a few turns are typically caused by the model getting stuck in a loop due to high cumulative probability from top-p sampling. Reducing top_p limits the set of tokens considered at each step, forcing the model to explore less likely tokens and breaking the repetition cycle. This directly addresses the issue without sacrificing coherence, unlike temperature adjustments which affect randomness globally.

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The max_output_tokens is too low; increase it to allow more diverse output.

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The model is overfitted; switch to a smaller model.

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The temperature is too low; increase temperature to add randomness.

Question 2

Refer to the exhibit. A data scientist is fine-tuning a model. The training loss and accuracy are improving each epoch. However, after training, the model performs poorly on a held-out validation set. What is the most likely issue?

Accepted Answer

Overfitting. The model's training loss and accuracy improve each epoch, but performance on the validation set is poor. This classic symptom indicates overfitting, where the model memorizes the training data (including noise) rather than learning generalizable patterns. In fine-tuning, this often occurs when the model is trained for too many epochs or the dataset is too small relative to model capacity.

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Underfitting

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Inappropriate learning rate

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Data leakage

Question 3

A marketing agency uses gen AI for content generation. They need to brand consistently. What is a key business consideration?

Accepted Answer

Implement content moderation and brand guidelines. Consistent branding requires enforcing predefined guidelines on tone, style, and terminology across all generated content. Without content moderation and brand guidelines, a generative AI model may produce off-brand, inconsistent, or even harmful outputs, undermining brand identity. This is a core business strategy for deploying gen AI at scale, ensuring alignment with marketing objectives.

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Use only generated content

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Use the most creative model

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Optimize for speed

Question 4

A law firm wants to use generative AI to analyze contracts and extract key clauses. They need high accuracy and the ability to handle diverse contract formats. Which three steps should they take in their proof-of-concept (PoC) phase? (Choose THREE)

Accepted Answer

Use few-shot prompting with examples of desired clause extraction. Few-shot prompting provides the model with specific examples of desired clause extraction, guiding it to produce accurate outputs without requiring fine-tuning. This technique is efficient for a proof-of-concept because it leverages the model's existing capabilities while adapting to the task through in-context learning, which is critical for handling diverse contract formats with high accuracy.

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Deploy a pre-built contract analysis API without customization

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Train a custom model from scratch using all historical contracts

Question 5

An organization is developing a GenAI strategy for multiple business units. Which THREE steps should they take to ensure alignment? (Select three.)

Accepted Answer

Establish common data governance policies. Establishing common data governance policies (C) ensures that all business units adhere to consistent standards for data quality, privacy, and security, which is critical for training and deploying reliable GenAI models. Without unified governance, disparate data practices can lead to model bias, compliance violations, and integration failures across the organization.

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Implement a chargeback model for usage costs

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Allow each business unit to independently choose models

Question 6

A data science team wants to collaborate on a machine learning project using a shared notebook environment that supports Python and R, with free access to GPUs for prototyping. Which Google tool is most suitable?

Accepted Answer

Colab. Colab (D) is the most suitable Google tool because it provides a shared notebook environment that supports both Python and R via the `%load_ext rpy2.ipython` magic, offers free access to GPUs (e.g., NVIDIA T4 or V100) for prototyping, and enables real-time collaboration through Google Drive integration. This directly matches the team's requirements for a collaborative, multi-language notebook with free GPU resources.

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Kaggle

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Cloud Shell

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Vertex AI Workbench

Question 7

Refer to the exhibit. A user receives this error when trying to get predictions from a Vertex AI endpoint. What is the most likely cause?

Accepted Answer

The user lacks necessary IAM permissions. The error message indicates an authorization failure when calling the Vertex AI endpoint. The most likely cause is that the user's IAM principal lacks the necessary permissions, such as `aiplatform.endpoints.predict`, to invoke the endpoint. Even if the endpoint and model are correctly deployed, missing IAM roles (e.g., Vertex AI User or custom role) will block prediction requests.

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The endpoint does not exist

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The endpoint is in a different region

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The model is not deployed

Question 8

A company is deciding between building a custom fine-tuned model vs. using a pre-built API for a document summarization task. The documents contain domain-specific jargon. Which factor STRONGLY favors using a pre-built API with prompt engineering?

Accepted Answer

The requirement for low initial development cost and fast time-to-market. Using a pre-built API with prompt engineering eliminates the need for expensive model training infrastructure and specialized ML expertise, enabling rapid deployment at low initial cost. For a document summarization task, prompt engineering can leverage the API's existing capabilities without custom fine-tuning, making it ideal when speed and budget are primary constraints.

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The need to handle highly specialized industry terminology

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The need for the model to learn a unique writing style from past summaries

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The need to keep all data on-premises for security compliance

Question 9

A news organization is using Vertex AI Gemini to summarize articles. They observe that the summaries sometimes contain hallucinated facts—specifically, dates and statistics that are not in the original article. The team is using the default temperature and top_p settings. They want to reduce hallucinations without making summaries too repetitive or overly conservative. They also need to keep latency low. Which action should they take?

Accepted Answer

Enable grounding with Google Search to provide factual source context.. Enabling grounding with Google Search is the correct action because it directly addresses the root cause of hallucinations—lack of factual source context—by allowing the model to cross-reference generated content with real-time, authoritative web data. This approach reduces fabricated dates and statistics without requiring changes to temperature or top_p, which could introduce repetition or conservatism, and it maintains low latency by leveraging Google's infrastructure for retrieval rather than model retraining.

Answer

Increase the temperature to 1.0 and lower top_p to 0.1.

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Fine-tune the model on a large dataset of articles and human-written summaries.

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Lower the temperature to 0.0 and increase top_p to 1.0.

Question 10

A social media platform wants to use generative AI to moderate harmful content. They need to detect hate speech, violence, and sexual content. Which Google Cloud service provides pre-built filters for these categories?

Accepted Answer

Natural Language API with content moderation. Google Cloud's Natural Language API includes content moderation models that can detect categories like hate speech, violence, and sexual content.

Answer

Dialogflow CX

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Vertex AI Matching Engine

Answer

Cloud Vision API

Question 11

A company is comparing Google Cloud Vertex AI with AWS Bedrock and Azure OpenAI. They need a model that can natively process text, images, audio, and video. Which differentiator does Google Cloud offer?

Accepted Answer

Gemini's native multimodal capabilities. Gemini is Google's multimodal model natively trained on text, images, audio, and video from the ground up, enabling it to process and reason across these modalities without separate components. This native capability is a key differentiator for Google Cloud Vertex AI, as competing platforms like AWS Bedrock and Azure OpenAI primarily offer models that are text-centric or require separate models for different modalities.

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Access to GPT-4o

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Support for Anthropic Claude

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Integration with Microsoft Copilot

Question 12

Refer to the exhibit. A developer receives this error when trying to call a model for prediction. What is the most likely cause?

Accepted Answer

The developer's service account lacks the required IAM role.. The error when calling a model for prediction most likely stems from the developer's service account lacking the required IAM role. In Google Cloud AI Platform, the 'aiplatform.user' or 'aiplatform.predictor' role is necessary to invoke prediction endpoints; without it, the API returns a permission-denied error. This is a common misconfiguration when service accounts are created without explicit roles attached.

Answer

The project has exceeded its prediction quota.

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The model version has been deprecated.

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The model is not deployed on an endpoint.

Question 13

A company is running a GenAI proof-of-concept (PoC) for internal document Q&A. The PoC shows high latency and cost. The team suspects they are using an unnecessarily large model for the task. What is the BEST action to optimize?

Accepted Answer

Switch to a smaller model from Vertex AI Model Garden. Switching to a smaller model from Vertex AI Model Garden directly addresses the root cause of high latency and cost: an unnecessarily large model. Smaller models have fewer parameters, requiring less compute per inference, which reduces both response time and operational expense while often being sufficient for domain-specific tasks like internal document Q&A.

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Disable grounding and rely solely on the model's internal knowledge

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Increase the batch size for requests

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Fine-tune the existing model on the company's documents

Question 14

A large enterprise is evaluating gen AI for internal knowledge management. They need to ensure accuracy and reduce hallucinations. Which strategy is most effective?

Accepted Answer

Use Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation (RAG) is the most effective strategy because it grounds the model's responses in an external, authoritative knowledge base, retrieving relevant documents at inference time to provide factual context. This directly reduces hallucinations by ensuring the generated output is based on retrieved evidence rather than relying solely on the model's parametric memory, which is critical for enterprise knowledge management where accuracy is paramount.

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Fine-tune a model on domain-specific data

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Increase model temperature

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Use a larger model without customization

Question 15

A startup wants to leverage Google Cloud's generative AI but has limited ML expertise. Which Google Cloud service allows them to build generative AI applications without deep ML knowledge?

Accepted Answer

Vertex AI Generative AI Studio. Vertex AI Generative AI Studio is a managed service that provides a low-code/no-code interface for building, testing, and deploying generative AI applications using pre-trained foundation models. It abstracts away the complexities of model training, infrastructure management, and ML pipeline orchestration, enabling teams with limited ML expertise to leverage generative AI capabilities through simple prompts and visual workflows.

Answer

Cloud TPU

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TensorFlow

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Apigee