AI-300 ML Model Lifecycle And Operations Practice Test 3 — 15 Questions

Question 1

You are configuring a batch scoring job. You need to ensure that the job processes data in parallel to reduce completion time. What property should you adjust in the 'ParallelRunConfig'?

Accepted Answer

Adjust 'process_count_per_node' and 'node_count'.. The 'node_count' and 'process_count_per_node' are the key parameters in 'ParallelRunConfig' that dictate the degree of parallelism.

Answer

Change the 'output_action' to 'append_row'.

Answer

Enable 'distributed_training'.

Answer

Set 'min_nodes' to 1.

Question 2

You are using MLflow to track experiments in Azure Machine Learning. You need to log a custom metric that is calculated every 100 iterations. Which MLflow function should you use?

Accepted Answer

'mlflow.log_metric()'.. The 'mlflow.log_metric' function is used to log key-value pairs of metrics, which can be called within the training loop.

Answer

'mlflow.set_tag()'.

Answer

'mlflow.log_artifact()'.

Answer

'mlflow.log_param()'.

Question 3

You are defining an Azure Machine Learning environment for a training job. The environment requires a specific set of Python libraries. What is the best practice for defining these dependencies?

Accepted Answer

Define dependencies in a 'conda.yaml' file.. Using a 'conda.yaml' file is the best practice for managing reproducible Python environments in Azure ML.

Answer

Hardcode pip install commands in the training script.

Answer

Install libraries via a startup script in the compute cluster.

Answer

Pre-install them on the virtual machine image.

Question 4

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 5

You need to ensure that training data is encrypted at rest in the Blob Storage linked to your Azure Machine Learning workspace. How do you ensure this?

Accepted Answer

Enable encryption at the storage account level.. Azure Storage accounts support Storage Service Encryption (SSE) by default. You can also use Customer-Managed Keys (CMK) for additional control.

Answer

Configure the training script to encrypt files.

Answer

Use SSL/TLS for all communication.

Answer

Use an encrypted VM for training.

Question 6

What is the primary purpose of a 'Labeling Project' in Azure Machine Learning?

Accepted Answer

To facilitate manual data annotation for supervised learning.. A Labeling Project in Azure Machine Learning is designed to facilitate manual data annotation for supervised learning. It provides a workspace where human labelers (or ML-assisted labeling) tag images, text, or other data to create labeled datasets for training. It does not version datasets, monitor drift, or define evaluation metrics.

Answer

To automatically version datasets.

Answer

To monitor model deployment drift.

Answer

To define model evaluation metrics.

Question 7

You are deploying a model that requires a high-memory compute for inference. You are using a 'Managed Online Endpoint'. Where do you specify the instance type for this deployment?

Accepted Answer

In the 'Deployment' YAML file under 'instance_type'.. The instance type is defined in the 'Deployment' configuration object, typically in the 'instance_type' field.

Answer

In the 'Endpoint' YAML file.

Answer

In the 'Environment' definition.

Answer

In the 'Workspace' settings.

Question 8

What is the purpose of a 'Datastore' in Azure Machine Learning?

Accepted Answer

To manage connectivity to storage services.. In Azure Machine Learning, a Datastore is an abstraction that stores connection information to Azure storage services (Blob, File, Data Lake, SQL, etc.). It lets you reference storage by name in AML without hardcoding credentials or paths. Datastores manage connectivity, not model weights, versioning, or in-memory processing.

Answer

To store model weights.

Answer

To track model versions.

Answer

To process data in memory.

Question 9

Which Azure Machine Learning resource provides a pre-configured environment for development?

Accepted Answer

Compute Instance.. A 'Compute Instance' is a managed, cloud-based development environment that comes pre-installed with the Azure ML SDK, Jupyter, and other tools.

Answer

Inference Cluster.

Answer

Compute Cluster.

Answer

Managed Endpoint.

Question 10

You are automating model registration using the Azure ML CLI. You need to ensure the registration only happens if the model accuracy is above 0.9. How do you implement this condition?

Accepted Answer

Implement logic in the pipeline to gate the registration step.. You must include logic in your pipeline or script to evaluate the metric (e.g., via a 'PythonScriptStep') and only call the 'az ml model create' command if the condition is met.

Answer

Use the 'condition' parameter in the 'model create' command.

Answer

Use an 'Azure Function' to trigger registration.

Answer

Configure a 'ValidationThreshold' in the registry.

Question 11

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 12

What is the 'Workspace' in Azure Machine Learning?

Accepted Answer

The top-level resource for managing all ML assets.. The Workspace is the top-level resource for Azure Machine Learning, providing a centralized place to manage all artifacts, computes, and jobs.

Answer

A specific model deployment.

Answer

A virtual machine for training.

Answer

A storage container for datasets.

Question 13

What is the benefit of using 'Azure Machine Learning Datasets' (or Data Assets) over raw storage paths?

Accepted Answer

They provide versioning and lineage tracking.. Data Assets allow for versioning, lineage tracking, and simplified usage across different compute targets.

Answer

They convert data to Parquet format.

Answer

They automatically remove duplicate records.

Answer

They are faster to read.

Question 14

You have an automated model training pipeline that is failing intermittently due to compute availability. What should you configure to ensure the pipeline is more resilient?

Accepted Answer

Configure 'retry' settings for the step.. You should configure 'retry' settings in the 'PipelineStep' definition to handle transient compute errors.

Answer

Increase the 'max_nodes' of the cluster.

Answer

Set the 'allow_reuse' parameter to True.

Answer

Use 'LowPriority' compute.

Question 15

You are implementing a custom container for model training. You need to push the image to the 'Azure Container Registry' (ACR) linked to your workspace. What is the correct command?

Accepted Answer

'docker build' and 'docker push' to the ACR URL.. The standard approach is 'docker build' followed by 'docker push' to the ACR URL, ensuring authentication via 'az acr login'.

Answer

'az ml environment create --custom'.

Answer

'az container create'.

Answer

'az ml model deploy --image'.