Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 4 — 15 Questions

Question 1

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?

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

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.

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Use prompt engineering

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Use a larger model

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Increase temperature parameter

Question 2

A financial institution wants to use generative AI to generate personalized investment advice. They face strict regulatory requirements on explainability and bias. Which approach should they take?

Accepted Answer

Use a RAG system with curated proprietary data. A Retrieval-Augmented Generation (RAG) system allows the financial institution to ground generative AI outputs in curated, proprietary data sources (e.g., regulatory guidelines, client risk profiles, historical performance). This approach enhances explainability by enabling traceable citations back to specific documents, and reduces bias by controlling the data fed to the model, which is critical for meeting strict regulatory requirements like GDPR or SEC rules on algorithmic fairness.

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Use a foundation model with prompt engineering

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Use a custom model trained from scratch

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Use a closed-source model with vendor lock-in

Question 3

An e-commerce company uses a generative AI model to generate marketing copy. They notice that the model occasionally produces off-brand or inappropriate content. What is the best way to mitigate this?

Accepted Answer

Fine-tune the model on brand guidelines. Fine-tuning the model on brand guidelines directly addresses the root cause of off-brand or inappropriate content by adjusting the model's weights to align with specific stylistic and content constraints. This supervised learning approach teaches the model the desired output patterns, making it inherently less likely to generate violations compared to post-hoc filtering or sampling adjustments.

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Reduce the model's temperature

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Increase the model's top-k sampling

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Use a safety filter

Question 4

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.

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Cloud TPU

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TensorFlow

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Apigee

Question 5

A healthcare company wants to use generative AI to summarize patient records but must comply with HIPAA. Which deployment option should they choose?

Accepted Answer

Use an on-premises deployment of open-source model. An on-premises deployment of an open-source model ensures that all patient data remains within the organization's controlled infrastructure, never leaving the local network. This eliminates any risk of data transmission to external cloud services, which is critical for HIPAA compliance where protected health information (PHI) must be safeguarded against unauthorized access or breaches. On-premises solutions allow the organization to implement its own security controls, encryption, and audit trails without relying on a third-party's compliance posture.

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Use Vertex AI on Google Cloud with data residency

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Use Google Workspace AI

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Use a third-party API

Question 6

A marketing agency wants to generate images using Imagen on Vertex AI. They need to ensure the images are unique and avoid copyright issues. Which parameter adjustment is most relevant?

Accepted Answer

Use negative prompts. Negative prompts allow the model to exclude specific concepts, styles, or elements from generated images, directly reducing the risk of replicating copyrighted or trademarked content. By explicitly telling Imagen what not to include, the agency can steer outputs away from protected works without needing to modify training data or safety filters.

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Increase training steps

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Increase seed variability

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Set safety threshold

Question 7

Which TWO strategies are effective for reducing latency in a generative AI chat application deployed on Vertex AI? (Select 2)

Accepted Answer

Use streaming responses. Streaming responses reduce perceived latency by sending tokens to the client as they are generated, rather than waiting for the full response. This leverages server-sent events (SSE) or chunked transfer encoding to deliver partial results immediately, improving user experience in chat applications.

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Deploy on TPU instead of GPU

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Increase the max output tokens

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Use larger batch sizes

Question 8

Which THREE factors should be considered when choosing between a fine-tuned model and a prompted foundation model for a generative AI solution? (Select 3)

Accepted Answer

Need for domain-specific vocabulary. Fine-tuning allows the model to learn domain-specific vocabulary and terminology that may not be well-represented in the foundation model's pre-training data. This is critical for specialized fields like legal, medical, or technical domains where precise language is required for accurate outputs.

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Inference latency requirements

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Whether the model is open-source

Question 9

Which TWO Google Cloud services can be used together to implement a RAG (retrieval-augmented generation) pipeline? (Select 2)

Accepted Answer

Vertex AI Vector Search. Vertex AI Vector Search (option B) is correct because it provides a managed vector database for storing and querying embeddings, which is essential for the retrieval step in a RAG pipeline. It enables semantic similarity search over large datasets, allowing the system to fetch relevant context documents based on a user query.

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Cloud SQL

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Bigtable

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Cloud Functions

Question 10

Refer to the exhibit. A data scientist runs this command to upload a custom model to Vertex AI. What is the primary purpose of the --container-image-uri flag?

Accepted Answer

To specify the base image for model serving. The --container-image-uri flag in the `gcloud ai models upload` command specifies the custom container image that Vertex AI will use to serve predictions. This is the base image for model serving, not for training, because Vertex AI uses this image to create the serving environment that hosts the model and handles prediction requests.

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To indicate the model artifact location

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To set the training container

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To define the prediction container

Question 11

Refer to the exhibit. This JSON describes a Vertex AI endpoint with a deployed model. Which statement about scaling is true?

Accepted Answer

The endpoint uses only dedicated resources, no automatic scaling. The JSON shows that the endpoint is configured with `dedicatedResources` and no `autoscalingMetricSpecs` or `minReplicaCount`/`maxReplicaCount` fields. In Vertex AI, when you specify only `machineSpec` and a fixed `minReplicaCount` (here implicitly 1) without a `maxReplicaCount` or autoscaling metrics, the endpoint uses dedicated resources with no automatic scaling — the model will always run on exactly the number of replicas you define, regardless of load.

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The endpoint will automatically scale based on GPU utilization

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The endpoint will scale from 1 to 3 replicas based on load using automatic scaling

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The endpoint can scale to zero when not in use

Question 12

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 13

A healthcare provider plans to implement gen AI for clinical note summarization. They have limited AI expertise. Which Google Cloud approach best aligns with their business strategy?

Accepted Answer

Use Vertex AI Agent Builder with pre-built templates. Vertex AI Agent Builder provides pre-built templates and a low-code interface specifically designed for organizations with limited AI expertise. It enables rapid deployment of generative AI solutions like clinical note summarization without requiring deep data science skills, directly aligning with the healthcare provider's business strategy of minimizing technical overhead while leveraging AI.

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Hire a team of data scientists

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Deploy an open-source model on Compute Engine

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Build a custom model from scratch

Question 14

A financial institution wants to deploy a gen AI model for fraud detection but must comply with strict regulations regarding explainability. What is the best strategy?

Accepted Answer

Implement a smaller interpretable model with acceptable accuracy. Regulatory compliance for fraud detection demands explainability, which complex black-box models cannot provide. A smaller interpretable model (e.g., logistic regression or decision tree) offers transparency into decision factors, satisfying regulations like GDPR's right to explanation while maintaining acceptable accuracy for the use case.

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Use Vertex AI Explainable AI with a complex model

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Deploy multiple models and ensemble

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Use a large black-box model and rely on external auditing

Question 15

A startup wants to quickly prototype a gen AI application. Which Google Cloud service should they use first?

Accepted Answer

Gen AI Studio. Gen AI Studio (now part of Vertex AI) provides a low-code/no-code interface for quickly prototyping generative AI applications using pre-trained models like PaLM 2 and Gemini. It allows startups to experiment with prompts, tune models, and deploy without managing infrastructure, making it the fastest path from idea to prototype.

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Vertex AI Workbench

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Cloud TPUs

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Dataflow