AI-300 ML Model Lifecycle And Operations Practice Test 2 — 15 Questions

Question 1

You want to perform hyperparameter tuning using the 'HyperDrive' service. You have a requirement to stop poor-performing runs early to save compute costs. Which policy should you use?

Accepted Answer

'MedianStoppingPolicy'.. The 'BanditPolicy' is the standard early-termination policy that stops runs based on a slack factor/amount compared to the best-performing run.

Answer

'TruncationSelectionPolicy'.

Answer

'RandomParameterSampling'.

Question 2

You are configuring a 'Managed Online Endpoint' for a very large model (10GB+). The deployment is failing during the 'pulling image' phase. What is the most likely cause?

Accepted Answer

The 'readiness_probe' timeout is too short.. The 'pulling image' phase is when the managed online endpoint pulls the model's Docker image from Azure Container Registry to the compute instance. For a 10GB+ model, the pull can take many minutes. The default readiness_probe timeout is often too short, causing the deployment to fail before the image finishes pulling and the container becomes ready. Increasing the readiness_probe timeout (and possibly the startup timeout) resolves this.

Answer

The instance count is too high.

Answer

The ACR is private.

Answer

The compute is too small.

Question 3

You are monitoring model drift. Which TWO features are required to configure a Data Drift Monitor?

Accepted Answer

A target dataset to compare against.. In Azure Machine Learning, a Data Drift Monitor requires two datasets: a baseline dataset (option C), which represents the reference distribution the model was trained on, and a target dataset (option B), which is the newer production data compared against the baseline to compute drift metrics. The monitor calculates drift by comparing the feature distributions of the target dataset against the baseline dataset over time, so both are mandatory inputs. Option A is incorrect because an Azure SQL database is not required — datasets can reside in various sources such as Azure Blob Storage or Azure Data Lake, and the monitor does not depend on SQL. Option D is incorrect because, while a model may be associated with the monitor for organization, a model version is not a required configuration input for the drift monitor itself. Option E is incorrect because Data Drift Monitor uses built-in drift metrics (e.g., population stability index, Jensen-Shannon distance) and does not require a custom Python script.

Answer

An Azure SQL database.

Answer

A model version.

Answer

A custom Python script.

Question 4

You have an automated deployment pipeline. You want to run an integration test on the model after deployment. Which tool is best suited for this?

Accepted Answer

Azure DevOps pipelines.. 'Azure DevOps' (or GitHub Actions) is the industry-standard tool for orchestrating post-deployment testing as part of a CI/CD pipeline.

Answer

A manual 'Endpoint' health check.

Answer

The training script.

Answer

Azure Machine Learning Studio 'Tests' tab.

Question 5

Which THREE types of information are found in a Run Object in Azure ML?

Accepted Answer

Tags.. In Azure Machine Learning, a Run object is the record of a single execution of an experiment and it captures the metadata produced during that execution. Option A (Tags) is correct because runs support arbitrary key-value tags that you can add for labeling, filtering, and organizing runs in the studio and via the SDK. Option C (Metrics) is correct because logged metrics (for example, via run.log or mlflow.log_metric) are stored on the run and used for charting and comparing model performance. Option E (Parameters) is correct because run parameters (for example, via run.log or the ScriptRunConfig arguments) are recorded on the run to document the inputs and hyperparameters used. Option B (Billing history) is not part of a Run object; cost and billing data are surfaced through Azure Cost Management and the subscription/billing APIs, not the run record. Option D (Network topology map) is not stored in a Run object; network configuration belongs to the workspace, compute, and virtual network resources rather than to an individual run.

Answer

Billing history.

Answer

Network topology map.

Question 6

Which THREE settings are part of the 'InferenceConfig' object in Azure ML?

Accepted Answer

Source directory.. In Azure ML, the InferenceConfig object (created via InferenceConfig or inference_config in the Python SDK) defines how a deployed model runs, and its core parameters include the source directory (A), which points to the folder containing the model artifacts and scoring code; the environment definition (B), which specifies the Docker image and dependencies used by the inference container; and the entry script path (D), which identifies the scoring script (e.g., score.py) that contains the init() and run() functions. These three settings are exactly the fields accepted by InferenceConfig (entry_script, source_directory, environment). Option C (target storage account URL) is not part of InferenceConfig—storage targets are configured in deployment or datastore settings, not in the inference configuration. Option E (compute instance size) is also not part of InferenceConfig; compute sizing belongs to the deployment configuration (e.g., AciWebservice or AksWebservice) or the compute target definition, not to the inference configuration object.

Answer

Target storage account URL.

Answer

Compute instance size.

Question 7

You are using Azure Machine Learning Pipelines. Which THREE triggers can be used to start a pipeline?

Accepted Answer

Scheduled trigger.. Option B (Scheduled trigger) is correct because Azure Machine Learning pipelines support time-based schedules via Schedule objects (created with CLI, SDK, or studio), allowing pipelines to run on a recurring cadence such as cron or recurrence patterns. Option D (REST API endpoint) is correct because a published pipeline exposes a REST endpoint that can be invoked programmatically (for example, via the Azure ML REST API or the az ml pipeline invoke command) to start a run on demand. Option E (Event-based trigger) is correct because Azure Machine Learning integrates with Azure Event Grid and Logic Apps, enabling event-driven pipeline execution when events such as blob creation or model registration occur. Option A (Manual file drag-and-drop) is not a supported pipeline trigger mechanism in Azure Machine Learning, and Option C (Direct code modification in the UI) is not a trigger at all—editing code does not automatically start a pipeline run.

Answer

Manual file drag-and-drop.

Answer

Direct code modification in the UI.

Question 8

Which TWO languages are natively supported for the Azure ML SDK?

Accepted Answer

R.. Option C (R) is correct because the Azure Machine Learning SDK provides a native R interface via the azuremlsdk package, allowing R users to manage workspaces, experiments, and model training directly. Option D (Python) is correct because the Azure ML SDK is primarily a Python SDK (azureml-core, azureml-sdk), and Python is the first-class, fully supported language for authoring and running Azure ML workloads. Options A (Pascal), B (Basic), and E (Fortran) are not natively supported by the Azure ML SDK and have no official SDK bindings, so they do not belong.

Answer

Pascal.

Answer

Basic.

Answer

Fortran.

Question 9

You need to implement a retraining trigger based on performance degradation. Which TWO metrics should you monitor to decide when to retrain?

Accepted Answer

Model prediction precision. Options D and E are correct because retraining triggers based on performance degradation must directly measure how well the model's predictions match ground truth: model prediction precision (D) captures the proportion of positive predictions that are actually correct, and model prediction accuracy (E) captures the overall fraction of correct predictions, both of which decline when the model drifts or degrades. Monitoring these model-quality metrics lets you set thresholds (e.g., accuracy drops below 90% or precision falls by 5%) that signal when retraining is warranted. Option A (CPU usage of the inference cluster) is an infrastructure resource metric that reflects load or scaling needs, not model correctness. Option B (network latency) measures communication delay and says nothing about prediction quality. Option C (number of active users) is a traffic/usage metric that may correlate with load but does not indicate whether the model's predictions have degraded.

Answer

CPU usage of the inference cluster

Answer

Network latency

Answer

Number of active users

Question 10

You are configuring a CI/CD pipeline for model deployment. Which THREE actions must you perform to ensure model traceability?

Accepted Answer

Register the model in the Azure ML Model Registry with versioning. Option B is correct because registering the model in the Azure ML Model Registry with versioning creates an immutable, uniquely identifiable record (name:version) that links each deployed artifact to its exact lineage, enabling rollback and audit. Option C is correct because adding experiment tags to the model artifact attaches metadata such as run ID, dataset version, and git commit, which are queryable and provide the contextual provenance needed to trace a model back to its training experiment. Option E is correct because storing the training script hash in the model properties creates a cryptographic fingerprint that proves which exact code produced the model, ensuring reproducibility and detecting any drift between the deployed model and its source code. Option A is not appropriate because a shared local folder is not versioned, not immutable, and not accessible across pipeline agents, so it cannot guarantee traceability. Option D is incorrect because deleting training logs destroys the audit trail and provenance evidence required for traceability.

Answer

Use a shared local folder for model artifacts

Answer

Delete all training logs after deployment

Question 11

You are building an Azure Machine Learning pipeline. You need to ensure that the pipeline components are reusable and versioned independently. What should you use?

Accepted Answer

Azure ML Components. Azure ML components allow for independent versioning and reusability across different pipelines.

Answer

Azure ML Datasets

Answer

Azure ML Pipelines Steps

Answer

Azure Container Registry images

Question 12

You need to monitor the data drift of a model deployed in Azure ML. What is the first step you must take?

Accepted Answer

Create a Data Drift Monitor. In Azure Machine Learning, data drift detection is a first-class feature that requires you to create a Data Drift Monitor on a deployed model endpoint or dataset. The monitor defines the baseline dataset, target dataset, features to track, and the drift metric (e.g., population stability index) and schedule. Only after the monitor exists can alerts and metrics be surfaced.

Answer

Configure Application Insights

Answer

Run a manual evaluation script

Answer

Enable Azure Monitor logs

Question 13

Your team needs to share a model across different workspaces. What is the most efficient way to achieve this in Azure Machine Learning?

Accepted Answer

Use a shared Azure Machine Learning Registry.. Using a shared Azure Machine Learning Registry is the most efficient way to share a model across different workspaces because it provides a centralized, versioned repository for models, components, and environments that can be accessed by multiple workspaces with proper access control. It eliminates duplication and ensures consistency.

Answer

Export the model as a pickle file and upload to each workspace.

Answer

Duplicate the workspace storage account.

Answer

Re-train the model in every workspace.

Question 14

You are implementing a retraining trigger for a demand forecasting model. You want to trigger a pipeline execution only when the drift metric for the 'Price' feature exceeds a predefined threshold. Which service should you integrate with Azure Machine Learning?

Accepted Answer

Azure Event Grid and Logic Apps.. Azure Machine Learning Data Drift Monitors can be configured to emit events to Azure Event Grid, which can then trigger an Azure Logic App or Azure Function to start the pipeline.

Answer

Azure Batch schedules.

Answer

Azure Data Factory triggers.

Answer

Azure Monitor alerts on logs.

Question 15

You are deploying a model via a Managed Online Endpoint. You want to implement a Canary deployment where 10% of traffic is sent to a new model version. How do you configure this?

Accepted Answer

Configure the 'traffic' parameter in the 'az ml online-endpoint update' command.. You use the 'update' command on the endpoint to set the 'traffic' parameter, distributing weights across deployment names.

Answer

Modify the 'inference_config.json' file to include weights.

Answer

Create two endpoints and use an Azure Load Balancer.

Answer

Set the 'replica_count' to 10% of total capacity.