AI-300 Practice Test 7 — 10 Questions

Question 1

Which TWO of the following are required to secure an Azure ML workspace using a Private Link?

Accepted Answer

A Virtual Network (VNet). To secure an Azure Machine Learning workspace with Private Link, you must have a Virtual Network (VNet) (option B) because the private endpoint is deployed into a subnet within a VNet, which provides the network isolation boundary for the workspace. You also need a Private Endpoint (option C), which creates a private IP address in that VNet and maps it to the Azure ML workspace, allowing access over the Microsoft backbone network instead of the public internet. A public IP address (option A) is not required and actually contradicts the goal of private connectivity. A Data Factory instance (option D) is unrelated to Private Link for Azure ML, and Azure Active Directory B2C (option E) is an identity service for customer-facing apps, not a networking component for Private Link.

Answer

Public IP address

Answer

Data Factory instance

Answer

Azure Active Directory B2C

Question 2

Which service allows you to track ML models and their associated artifacts?

Accepted Answer

Azure Model Registry.. The 'Azure Machine Learning Model Registry' is specifically designed to store, version, and manage models.

Answer

Azure Container Registry.

Answer

Azure Key Vault.

Answer

Azure Data Lake.

Question 3

In an LLM pipeline, what is the primary risk of relying solely on automated 'Groundedness' evaluators?

Accepted Answer

They may have false negatives and miss subtle hallucinations. Automated groundedness evaluators (such as the groundedness metric in Azure AI Foundry's evaluation SDK) compare model output against supplied context using a judge model, and that judge is itself probabilistic. Because the evaluator relies on an LLM or NLP model to decide whether a claim is supported, it can return a passing score for a subtly fabricated or misattributed statement. This means false negatives — undetected hallucinations — are an inherent limitation, so human review and complementary metrics (relevance, fluency, retrieval score) remain necessary.

Answer

They prevent the use of custom prompts

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They always increase latency significantly

Answer

They require a permanent database connection

Question 4

You are preparing a model for deployment. Which THREE items should you include in the model package?

Accepted Answer

Model weights (pickle/onnx).. The model package must contain the serialized model artifact itself, so option C (Model weights in pickle/onnx format) is correct because this is the trained artifact that will be loaded to produce predictions. Option D (Inference script, score.py) is correct because the deployment service needs an entry point that defines init() and run() to load the model and handle scoring requests. Option E (Environment file, conda.yaml) is correct because it declares the exact Python and library dependencies required to recreate the runtime environment so the model executes consistently. Option A (Training dataset) is not included because the package is for inference, not retraining, and shipping raw training data is unnecessary and often a privacy/compliance risk. Option B (Pipeline definition) is not included because a pipeline describes the training/retraining workflow, not the deployable scoring artifact.

Answer

Training dataset.

Answer

Pipeline definition.

Question 5

Your team wants to use prompt versioning. Where should this be managed in the AI Foundry workflow?

Accepted Answer

Prompt Flow in Azure AI Foundry. Prompt versioning in the AI Foundry workflow is managed through Prompt Flow, which treats prompts as versioned flow assets. Prompt Flow lets you author, version, and compare prompt variants, track them alongside flow runs, and promote a specific version to a deployment, giving reproducibility and rollback. This is the native mechanism for prompt lifecycle management in Azure AI Foundry.

Answer

Azure Database for PostgreSQL

Answer

GitHub repository only

Answer

Azure Blob Storage

Question 6

Which TWO of the following are benefits of using Azure ML Environments?

Accepted Answer

Consistent software dependencies. Environments ensure reproducibility and simplify dependency management across environments.

Answer

Network traffic filtering

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Database optimization

Answer

Automatic scaling

Question 7

You are moving a model from a local environment to Azure Machine Learning. Which file is required to define the entry script for the model inference?

Accepted Answer

'score.py'.. The entry script (often named 'score.py') is required to define 'init()' and 'run()' functions for the deployment.

Answer

'inference_config.json'.

Answer

'model.pkl'.

Answer

'environment.yml'.

Question 8

To optimize the cost of your AI infrastructure, which THREE actions should you consider?

Accepted Answer

Deleting unused model deployments.. Option A is correct because deleting unused model deployments eliminates the ongoing compute charges (e.g., per-hour inference endpoint or provisioned throughput costs) that accrue even when a deployment receives no traffic, directly reducing waste. Option B is correct because purchasing reserved capacity (such as Azure OpenAI Provisioned Throughput Units or reserved VM instances) commits to a 1- or 3-year term in exchange for a significant discount versus pay-as-you-go pricing, which lowers cost for predictable, long-term workloads. Option D is correct because right-sizing instances based on actual usage metrics (CPU, GPU, memory, and token throughput) ensures you pay only for the capacity you truly need, avoiding over-provisioned and underutilized resources. Option C is not correct because deploying every model in every region multiplies redundant compute and storage costs without guaranteeing usage, which increases rather than optimizes spend. Option E is not correct because always choosing the 'Premium' SKU incurs higher charges for features that may not be required, whereas selecting the appropriate SKU tier for the workload is the cost-efficient approach.

Answer

Deploying all models in every available region.

Answer

Always selecting the 'Premium' SKU.

Question 9

You need to ensure that your model inference code has access to the latest secret keys without hardcoding them. What is the recommended integration?

Accepted Answer

Use the workspace Key Vault to store and retrieve secrets.. You should integrate your scoring script with Azure Key Vault using the workspace 'get_default_keyvault()' function to retrieve secrets at runtime.

Answer

Store keys as environment variables in the Dockerfile.

Answer

Use a configuration file on the VM.

Answer

Pass them as arguments to the 'init()' function.

Question 10

Which THREE of the following are types of Azure ML endpoints?

Accepted Answer

Batch endpoint. C. Batch endpoint is correct because Azure Machine Learning supports batch endpoints as a distinct endpoint type used to run scoring jobs over large datasets asynchronously, producing file-based output. D. Web service (legacy) is correct because Azure ML historically exposed deployed models as web services (the legacy AKS/ACI web service endpoint type) before the newer endpoint concepts were introduced. E. Managed online endpoint is correct because managed online endpoints are the current Azure ML endpoint type for real-time, low-latency inference with managed infrastructure and traffic routing. A. Network endpoint is not an Azure ML endpoint type; it refers to networking constructs such as private endpoints or service endpoints, not model deployment endpoints. B. Database endpoint is not an Azure ML endpoint type; it describes a database connectivity target, not an Azure ML deployment endpoint.

Answer

Network endpoint

Answer

Database endpoint