Generative AI Leader Practice Test 33 — 15 Questions

Question 1

An ML engineer sees the above deployment output. The business wants to reduce inference cost. Which action should they take?

Accepted Answer

Change to a lower-cost machine type. Option B is correct because switching to a lower-cost machine type directly reduces the per-request compute cost without altering the model architecture or inference logic. This is a common cost-optimization strategy in cloud-based ML deployments, where instance types (e.g., from GPU to CPU or from a larger to a smaller GPU) can be selected based on latency and throughput requirements, provided the model fits within the machine's memory and compute constraints.

Answer

Use a larger model

Answer

Deploy to multiple regions

Answer

Increase traffic split

Question 2

A company wants to ensure only authorized users can deploy gen AI models. The current policy allows all users in the domain. What is the best practice to restrict deployment?

Accepted Answer

Add condition to restrict deployment. Option C is correct because adding a condition to restrict deployment (e.g., using IAM conditions in AWS, conditional access policies in Azure, or attribute-based access control in GCP) allows you to limit model deployment to only authorized users based on attributes like user role, project, or resource tags. This is the best practice because it enforces fine-grained access control without removing existing permissions or adding unnecessary roles, directly addressing the requirement to restrict deployment while maintaining existing user access.

Answer

Remove the binding

Answer

Add more roles

Answer

Use organizational policies

Question 3

A healthcare provider wants to use generative AI to automatically draft clinical notes from doctor-patient conversations. They must comply with HIPAA and ensure patient data privacy. Which strategy best meets their requirements?

Accepted Answer

Use Google Cloud Healthcare API integrated with Vertex AI. Option B is correct because Google Cloud Healthcare API with Vertex AI provides a HIPAA-compliant, managed environment that integrates generative AI capabilities directly with healthcare data. The Healthcare API enforces data residency, access controls, and audit logging, while Vertex AI allows fine-tuning or using foundation models without exposing PHI to public endpoints. This combination ensures patient data privacy and regulatory compliance without requiring on-premises infrastructure.

Answer

Outsource note generation to a third-party HIPAA-compliant vendor

Answer

Deploy a custom model on-premises with strict access controls

Answer

Use a public LLM with a data anonymization pipeline

Question 4

A company is using a generative AI model for internal report generation. They notice costs are high because each request processes large amounts of text. Which business strategy would most effectively reduce costs while maintaining quality?

Accepted Answer

Fine-tune a smaller model on a specialized dataset.. Fine-tuning a smaller model on a specialized dataset reduces computational cost per inference because smaller models have fewer parameters and require less memory and processing power. By tailoring the model to the company's specific domain (e.g., internal reports), it can maintain output quality comparable to a larger general-purpose model, directly addressing the cost-per-request issue without sacrificing accuracy.

Answer

Use a more powerful model to reduce retries.

Answer

Implement caching for repeated requests.

Answer

Increase the batch size for online predictions.

Question 5

A company wants to scale their generative AI application globally with low latency. Which infrastructure configuration is most suitable?

Accepted Answer

Multiple regional endpoints with traffic routing to the nearest region.. Option B is correct because deploying multiple regional endpoints with traffic routing to the nearest region minimizes latency by directing user requests to the geographically closest inference endpoint. This architecture leverages global load balancing (e.g., using Anycast DNS or HTTP(S) load balancers with backend services in multiple regions) to reduce round-trip time (RTT) and meet latency SLAs for real-time generative AI applications.

Answer

Use a CDN to cache responses.

Answer

On-premises deployment for all regions.

Answer

Single endpoint in us-central1 with high max replicas.

Question 6

A company deployed a Gemini model on Vertex AI for real-time inference. After a week, they notice that some requests return 500 Internal Server Error, and the endpoint is occasionally unreachable. The endpoint is configured with minReplicaCount=1 and maxReplicaCount=2. What is the most likely cause?

Accepted Answer

The endpoint is under-provisioned: minReplicaCount=1 is too low for peak load, causing the single replica to become saturated.. Option C is correct because with minReplicaCount=1 and maxReplicaCount=2, the endpoint starts with a single replica. Under peak load, that single replica can become saturated (CPU/memory exhaustion), causing 500 errors and unreachability. Autoscaling can add a second replica, but if the traffic spike is sudden or the scaling metric takes time to trigger, the single replica is overwhelmed before the second instance is provisioned.

Answer

Autoscaling is disabled, so the endpoint cannot handle traffic spikes.

Answer

The model was updated while the endpoint was serving requests.

Answer

The project has reached its Vertex AI endpoint quota.

Question 7

A team is selecting a foundation model for a text summarization use case. They need to consider factors that affect both model performance and production deployment. Which THREE factors are most critical? (Choose three.)

Accepted Answer

Inference latency and throughput capabilities.. Inference latency and throughput are critical for production deployment because they directly determine the user experience and operational cost. A model with high latency may be unsuitable for real-time summarization, while low throughput limits the number of concurrent requests the system can handle, affecting scalability and cost-efficiency.

Answer

Model parameter count (billions of parameters).

Answer

Training data provenance and licensing.

Question 8

A company has a generative AI model that is too slow for real-time inference. What architectural change would help?

Accepted Answer

Apply model quantization and deploy on TPUs. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which significantly decreases memory footprint and computation time, enabling faster inference. Deploying on TPUs (Tensor Processing Units) further accelerates matrix operations through specialized hardware, making this combination ideal for real-time latency requirements.

Answer

Switch to a larger, more accurate model

Answer

Deploy the model on more powerful CPUs

Answer

Use distributed training across multiple GPUs

Question 9

A healthcare startup is exploring GenAI for clinical note summarization. They have concerns about patient data privacy. Which Google Cloud approach best addresses privacy while still using powerful models?

Accepted Answer

Use Vertex AI with data residency controls and no external data sharing. Vertex AI with data residency controls and no external data sharing ensures that patient data remains within specified geographic boundaries and is not used for model training or improvement, directly addressing healthcare privacy regulations like HIPAA. This approach leverages Google Cloud's powerful models while maintaining strict data governance, unlike options that risk data exposure or lack enterprise-grade controls.

Answer

Deploy open-source models on-premises

Answer

Use a third-party API with anonymization of patient data

Answer

Use Vertex AI with model customization (fine-tuning)

Question 10

A marketing team wants to generate product descriptions using generative AI. They need to ensure factual accuracy and avoid hallucinations. Which approach should they use?

Accepted Answer

Implement a retrieval augmented generation (RAG) system that retrieves product facts from a database.. Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's output in verifiable, external data sources. By retrieving product facts from a database in real-time, the system ensures that the generated descriptions are based on accurate information, directly mitigating the risk of hallucination. This method combines the generative power of an LLM with a retrieval step that provides factual context, making it ideal for applications where precision is critical.

Answer

Use a code generation model to generate structured descriptions.

Answer

Fine-tune the model on all product descriptions using supervised learning.

Answer

Use a large language model with detailed prompt instructions to be accurate.

Question 11

A team is training a custom foundation model using JAX on TPUs on Google Cloud. They encounter frequent Out of Memory (OOM) errors. Which action is most effective in resolving the OOM error?

Accepted Answer

Enable model parallelism using GSPMD to distribute the model across TPU cores.. Option D is correct because OOM errors when training large foundation models on TPUs often stem from the model exceeding the memory of a single TPU core. GSPMD (Generalized SPMD) enables automatic model parallelism, sharding the model's parameters, gradients, and optimizer states across multiple TPU cores, thereby reducing per-core memory pressure without altering the model architecture or precision.

Answer

Reduce the model size by decreasing the number of layers.

Answer

Increase the batch size to maximize TPU utilization.

Answer

Use mixed precision training (bfloat16) to reduce memory footprint.

Question 12

Refer to the exhibit. A developer runs this command. What is the primary purpose?

Accepted Answer

Upload a model artifact to Model Registry. The command shown in the exhibit is `az ml model create --name my-model --path ./model.pkl --registry-name myregistry`. This command uploads a local model artifact (model.pkl) to the Azure Machine Learning Model Registry, which is a centralized repository for versioning and managing trained models. It does not initiate training, deployment, or pipeline creation; its sole purpose is to register the model artifact for later use.

Answer

Create a training pipeline

Answer

Deploy a model to an endpoint

Answer

Train a model

Question 13

Which TWO are components of the Vertex AI Generative AI Studio?

Accepted Answer

Model Garden. Model Garden is a core component of Vertex AI Generative AI Studio that provides a curated repository of foundation models, including Google's PaLM and Gemini models, as well as third-party models. It allows users to discover, compare, and deploy these models directly within the studio environment, making it essential for generative AI workflows.

Answer

Dataflow

Answer

Pipeline templates

Answer

Cloud Functions

Question 14

After fine-tuning a foundation model on company emails, the model outputs confidential information. What is the most likely cause?

Accepted Answer

The fine-tuning dataset was not anonymized. Option C is correct because the most likely cause of a fine-tuned model outputting confidential information is that the fine-tuning dataset contained sensitive data that was not anonymized. During fine-tuning, the model learns patterns and can memorize specific sequences, including confidential details like names, addresses, or proprietary information, which it then reproduces in responses. This is a well-known data leakage risk in fine-tuning workflows.

Answer

The prompt is too vague

Answer

The model is too large

Answer

Overfitting to the training data leading to memorization

Question 15

A data scientist is selecting a base model for generating Python code. Which TWO factors are most important to consider?

Accepted Answer

Model's license (proprietary vs open-source).. Option A is correct because the model's license determines whether the generated code can be used in commercial products without violating copyright or requiring attribution. Proprietary models may impose restrictions on output usage, while open-source models (e.g., CodeLlama, StarCoder) offer more flexibility for enterprise deployment. This is critical for compliance and intellectual property management in production environments.

Answer

Model's support for multiple programming languages.

Answer

Model's training data recency.

Answer

Model's parameter count (size).

Generative AI Leader Practice Test 33 — 15 Questions

Exhibit

Generative AI Leader Practice Test 33 — 15 Questions

Exhibit