Question # 1
| You need to resolve the local machine learning pipeline performance issue. What should you do? |
| A. Increase Graphic Processing Units (GPUs). | | B. Increase the learning rate. | | C. Increase the training iterations, | | D. Increase Central Processing Units (CPUs). |
A. Increase Graphic Processing Units (GPUs).
Question # 2
| You use the Azure Machine learning SDK v2 tor Python and notebooks to tram a model. You use Python code to create a compute target, an environment, and a taring script. You need to prepare information to submit a training job.
Which class should you use? |
| A. MLClient | | B. command | | C. BuildContext | | D. EndpointConnection |
B. command
Question # 3
| You train and register a model in your Azure Machine Learning workspace.
You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.
You need to create the inferencing script for the ParallelRunStep pipeline step.
Which two functions should you include? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point. |
| A. run(mini_batch) | | B. main() | | C. batch() | | D. init() | | E. score(mini_batch) |
A. run(mini_batch)
D. init()
Explanation:
[Reference:, https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learningpipelines/parallel-run, , ]
Question # 4
| You use Azure Machine Learning to tram a model.
You must use Baylean sampling to Tune hyperparaters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE Each correct selection is worth one point. |
| A. Normal | | B. Uniform | | C. Choice | | D. LogUniform | | E. QNormal |
B. Uniform
C. Choice
Question # 5
| You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.
You must add a statement that retrieves the names of the logs and outputs generated by the script.
You need to reference a Python class object from the SDK for the statement.
Which class object should you use? |
| A. Run | | B. ScripcRunConfig | | C. Workspace | | D. Experiment |
A. Run
Explanation:
A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
The run Class get_all_logs method downloads all logs for the run to a directory.
[Reference:, https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class), , ]
Question # 6
| You train and publish a machine teaming model.
You need to run a pipeline that retrains the model based on a trigger from an external system.
What should you configure? |
| A. Azure Data Catalog | | B. Azure Batch | | C. Azure logic App |
C. Azure logic App
Question # 7
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.
|
| A. Penalize the classification | | B. Resample the data set using under sampling or oversampling | | C. Generate synthetic samples in the minority class. | | D. Use accuracy as the evaluation metric of the model. | | E. Normalize the training feature set. |
A. Penalize the classification
B. Resample the data set using under sampling or oversampling
D. Use accuracy as the evaluation metric of the model.
Explanation:
References:
https://machinelearningmastery.com/tactics -to-combat-imbalanced-classes-in-your-machine-learning-dataset/
Question # 8
You create and register a model in an Azure Machine Learning workspace.
You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.
You need to create the compute target.
Which class should you use?
|
| A. BatchCompute | | B. AdlaCompute | | C. AmlCompute | | D. Aks Compute |
C. AmlCompute
Explanation:
Compute target to use for ParallelRunStep. This parameter may be specified as a compute target object or the string name of a compute target in the workspace.
The compute_target target is of AmlCompute or string.
Note: An Azure Machine Learning Compute (AmlCompute) is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users
[Reference:, https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig, , https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class), , , ]
Question # 9
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slack_factor set to 02 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?
|
| A. Set the value of slack. factor of earty. termination policy to 0.1. | | B. Set the value of max_concurrent_trials to 4. | | C. Set the value of slack_factor of eartyjermination policy to 0.9. | | D. Set the value of max. concurrentjrials to 20. |
B. Set the value of max_concurrent_trials to 4.
Question # 10
| You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.
You need to select a data cleaning method.
Which method should you use? |
| A. Synthetic Minority Oversampling Technique (SMOTE) | | B. Replace using MICE | | C. Replace using; Probabilistic PCA | | D. Normalization |
C. Replace using; Probabilistic PCA
Explanation:
Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
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