AI-300 Practice Exam 4 — 20 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

Answer

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

Answer

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

Question 11

Which THREE of the following are components of an Azure AI Foundry project?

Accepted Answer

Prompt Flows.. In an Azure AI Foundry project, Prompt Flows (C) are a core component that lets you orchestrate prompts, tools, and logic into deployable AI workflows, so they are correctly listed. Connections (D) are also a project component because they define authenticated links from the project to external resources such as Azure OpenAI, Azure AI Search, or storage, enabling the project to consume those services. Models (E) are a component as well, since a Foundry project manages model deployments and references to models used by prompt flows and other assets. Azure Key Vault (A) is not a component of the project itself; it is a separate Azure resource that a project may connect to for secret management. Azure Subscriptions (B) are not a project component either, as a subscription is the billing and management boundary that contains the Foundry resource and project, not an internal part of the project.

Answer

Azure Key Vault (the resource itself).

Answer

Azure Subscriptions.

Question 12

You are optimizing a model for a specific domain language. Which fine-tuning approach minimizes cost while maximizing domain adaptation?

Accepted Answer

Using LoRA (Low-Rank Adaptation).. LoRA (Low-Rank Adaptation) freezes the pretrained model weights and injects small trainable low-rank matrices into attention layers, so only a tiny fraction of parameters are updated. This dramatically reduces GPU memory, compute, and storage costs compared to full fine-tuning while still adapting the model to domain-specific language. It is the standard cost-efficient fine-tuning technique for domain adaptation.

Answer

Training the model from scratch.

Answer

Prompt engineering alone.

Answer

Full parameter fine-tuning.

Question 13

You are monitoring the cost of your GenAI infrastructure. You want to track usage per specific project. Which feature in Azure AI Foundry should you use?

Accepted Answer

Use 'Resource Tags' on the Azure AI project resource.. Resource Tags on the Azure AI project resource allow you to categorize and track costs by project. By applying tags (e.g., Project: A), you can use Azure Cost Management to filter and analyze costs per tag, enabling per-project cost tracking. This is the recommended approach for cost allocation in Azure.

Answer

Configure 'Cost Analysis' alerts in the Azure Portal.

Answer

Implement 'Azure Policy' to restrict resource types.

Answer

Switch to 'Pay-As-You-Go' subscription model.

Question 14

Which THREE of the following are valid compute targets for Azure Machine Learning training?

Accepted Answer

Compute Cluster. A. Compute Cluster is correct because Azure Machine Learning compute clusters are managed, auto-scaling clusters of CPU or GPU nodes specifically designed to run training scripts and pipelines at scale. B. Attached Kubernetes Cluster is correct because Azure ML lets you attach an existing Azure Kubernetes Service (AKS) cluster as a compute target, which can then execute training workloads (and inference) via the Azure ML Kubernetes compute. C. Compute Instance is correct because a compute instance is a managed, single-node VM in your workspace that can be used as a training target for development, testing, and small-scale training jobs. D. Azure Function is not a valid Azure ML compute target; it is a serverless event-driven compute service for running small pieces of code, not for orchestrating ML training jobs. E. SQL Database is not a compute target at all; it is a data storage service and cannot execute training scripts.

Answer

Azure Function

Answer

SQL Database

Question 15

To optimize costs for an enterprise chatbot, you want to implement token usage monitoring. Which Azure service should you integrate to track token consumption per user?

Accepted Answer

Azure Monitor with Log Analytics.. Azure Monitor with Log Analytics is the correct service to track token consumption per user because Azure OpenAI emits metrics and diagnostic logs (including token usage) that can be routed to a Log Analytics workspace for querying and per-user aggregation. You enable diagnostic settings on the Azure OpenAI resource to send logs and metrics to Log Analytics, then use Kusto Query Language (KQL) to aggregate tokens by user or custom dimension. This provides centralized monitoring, alerting, and cost analysis.

Answer

Azure Data Factory.

Answer

Azure Cognitive Search.

Answer

Azure SQL Database.

Question 16

Which of the following is a key component of an observability strategy for Generative AI applications?

Accepted Answer

Monitoring both technical and quality metrics. Observability requires tracking both technical metrics (latency, errors) and quality metrics (groundedness, safety).

Answer

Hard-coding all model responses

Answer

Using only manual review processes

Answer

Disabling logging to save costs

Question 17

You are implementing an agentic workflow using Azure AI Foundry. The agent needs to access a secure external API. How should you store the API key securely within the project?

Accepted Answer

Store the key in an 'Azure Key Vault' and reference it as a 'Connection' in the AI Project.. Storing the key in Azure Key Vault and referencing it as a Connection in the AI Project is correct because Key Vault provides secure secret management with access policies, auditing, and encryption. The AI Project can securely retrieve the key at runtime without exposing it in code or configuration files. Other options expose the key insecurely.

Answer

Pass the key as an environment variable in the deployment YAML.

Answer

Hardcode the key in the prompt template.

Answer

Save the key in a local '.env' file in the code repository.

Question 18

You are tracking LLM performance. Which metric is most critical to monitor if your cost-per-request is increasing unexpectedly?

Accepted Answer

Token usage. Token usage is the primary driver of LLM cost because most providers bill per input and output token. If cost-per-request rises unexpectedly, monitoring token usage reveals whether prompts, completions, or context windows have grown. This metric directly correlates with the billing unit, making it the most critical to track for cost anomalies.

Answer

User satisfaction score

Answer

Latency

Answer

Model version ID

Question 19

Which THREE factors influence the cost of an Azure ML Compute Cluster?

Accepted Answer

Idle time before shutdown. Azure ML Compute Cluster billing is driven by the compute resources actually provisioned and how long they run, so option A (Idle time before shutdown) is correct because the idle scale-down timer determines how long nodes keep running—and accruing charges—after jobs finish before the cluster scales back down. Option B (Minimum number of nodes) is correct because the minimum node count sets the baseline number of always-on nodes, and those nodes are billed even when no jobs are running. Option E (VM SKU (size/type)) is correct because the VM size/family chosen determines the per-hour price of each node, directly scaling the total cost. Option C (Workspace name) is not a billing factor—the workspace is just a logical container and its name has no effect on compute pricing. Option D (Number of users) does not affect cost either, since compute charges are based on provisioned nodes and runtime, not on how many users submit jobs.

Answer

Workspace name

Answer

Number of users

Question 20

Which THREE components are required to define a 'Managed Online Deployment'?

Accepted Answer

An environment definition.. A Managed Online Deployment in Azure Machine Learning requires an environment definition (B), which specifies the Docker image and dependencies (e.g., conda.yaml) used to build the scoring container. It also requires an inference script (C), typically score.py, that defines the init() and run() functions to load the model and handle scoring requests. A registered model (D) is also required, since the deployment must reference a model asset (by name and version) from the workspace registry to serve predictions. A GPU compute instance (A) is not mandatory because managed online endpoints can run on CPU SKUs, and a training dataset (E) is irrelevant to inference-time deployment.

Answer

A GPU compute instance.

Answer

A training dataset.