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

Question 1

A model is exhibiting data drift. You have created a drift monitor. What is the next step to automate the retraining?

Accepted Answer

Configure an Event Grid subscription to trigger a pipeline.. After creating a monitor, you set up an 'Event Grid' subscription to notify an Azure Function or Logic App to trigger the pipeline.

Answer

Update the dataset version.

Answer

Manually restart the compute cluster.

Answer

Create a 'RetrainingTrigger' in the model registry.

Question 2

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 3

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()'.

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'mlflow.log_param()'.

Question 4

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 5

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 6

You are running a distributed training job using the 'PyTorch' framework on Azure Machine Learning. You need to configure the 'DistributionConfiguration'. Which setting is mandatory for multi-node training?

Accepted Answer

'process_count' or 'node_count' in the configuration.. When using 'PyTorch' distribution, you must specify the 'process_count' or 'node_count' to correctly distribute the workload across the compute cluster.

Answer

'framework' set to 'TensorFlow'.

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'shm_size' set to 1GB.

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'enable_gpu' set to False.

Question 7

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 8

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

Accepted Answer

To facilitate manual data annotation for supervised learning.. Labeling projects are used to manage the process of annotating data (images, text) to create datasets for supervised learning.

Answer

To automatically version datasets.

Answer

To monitor model deployment drift.

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To define model evaluation metrics.

Question 9

You are configuring an 'Azure Machine Learning Compute Cluster' for a heavy training job. You want to ensure it shuts down automatically when no jobs are running. What setting should you configure?

Accepted Answer

'min_nodes' set to 0.. The 'idle_seconds_before_scaledown' property in the compute configuration determines how long the cluster waits before removing idle nodes.

Answer

'max_nodes' set to 0.

Answer

'cluster_priority' to 'LowPriority'.

Question 10

You have an Azure Machine Learning pipeline that uses 'PipelineData' to pass information between steps. You want to share data between a training step and a scoring step. What is the recommended way to persist this data?

Accepted Answer

Use an 'OutputDataBinding' to a registered Datastore.. 'PipelineData' allows intermediate data passing, but 'OutputDataBindings' are preferred for persisting artifacts that need to be accessed later, such as model files.

Answer

Embed the data in the run metadata.

Answer

Write the file to the local temp directory.

Answer

Use an environment variable to pass the file path.

Question 11

You are reviewing the 'Run History' in Azure Machine Learning. You want to compare the training time of two different experiments. Which UI feature should you use?

Accepted Answer

The 'Experiments' tab, then 'Compare' button.. The 'Charts' or 'Metrics' comparison view in the Azure ML Studio allows you to visualize and compare metrics across multiple runs.

Answer

The 'Models' registry list.

Answer

The 'Endpoints' dashboard.

Answer

The 'Notebooks' file browser.

Question 12

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.

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In the 'Workspace' settings.

Question 13

You are debugging an Azure ML pipeline. You want to see the stdout of a specific step that failed. How do you access this?

Accepted Answer

Run 'az ml job get-logs' with the job ID.. You can access the logs for each step by clicking on the 'Outputs + logs' tab of that specific step run in the Azure ML Studio interface.

Answer

Check the 'Experiment' dashboard settings.

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Download the 'run.json' file.

Answer

Look in the 'Workspace' logs folder.

Question 14

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

Accepted Answer

To manage connectivity to storage services.. A Datastore is an abstraction layer over Azure storage services (Blob, Files, ADLS) used to manage connections and data access in ML tasks.

Answer

To store model weights.

Answer

To track model versions.

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To process data in memory.

Question 15

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.