Generative AI Leader Business Strategies for Generative AI Solutions Practice Test 3 — 15 Questions

Question 1

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. Option B is correct because 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

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Implement a custom cost allocation using labels

Answer

Use Cloud Billing budgets and alerts per model

Question 2

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. Option C is correct because 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.

Answer

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 3

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.

Answer

Increase training steps

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Increase seed variability

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Set safety threshold

Question 4

A company is using generative AI for code generation and wants to evaluate the quality of generated code for security vulnerabilities. Which metric is most appropriate?

Accepted Answer

Automatic static analysis. Option C is correct because automatic static analysis can scan code for security issues efficiently. Option A (BLEU score) measures text similarity, not security. Option B (human evaluation) is subjective and expensive. Option D (perplexity) measures language model confidence, not code security.

Answer

BLEU score

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Human evaluation

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Perplexity

Question 5

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. Option B is correct because 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.

Answer

Deploy on TPU instead of GPU

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Increase the max output tokens

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Use larger batch sizes

Question 6

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. Option A is correct because 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.

Answer

Inference latency requirements

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Whether the model is open-source

Question 7

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.

Answer

Cloud SQL

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Bigtable

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Cloud Functions

Question 8

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.

Answer

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 9

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. Option A is correct because 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.

Answer

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 10

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. Option B is correct because the error message explicitly says PERMISSION_DENIED, indicating lack of IAM permissions. Option A (endpoint does not exist) would give NOT_FOUND error. Option C (model not deployed) would give a different error. Option D (different region) would also give a different error.

Answer

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 11

A retail company wants to use gen AI for customer service chatbots. They have a large volume of customer interactions. What is the primary business consideration for deploying a gen AI solution?

Accepted Answer

Ensuring data privacy and compliance. Option D is correct because ensuring data privacy and compliance is critical when handling customer data. Option A is wrong because complexity doesn't guarantee success. Option B is wrong because minimizing latency at any cost can be too expensive. Option C is wrong because open-source models may not meet all requirements.

Answer

Minimizing latency at any cost

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Using open-source models only

Answer

Choosing the most complex model

Question 12

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.

Answer

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 13

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. Option D is correct because 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.

Answer

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 14

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.

Answer

Vertex AI Workbench

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Cloud TPUs

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Dataflow

Question 15

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.

Answer

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