Question # 1
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 # 2
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 - AzureML kernel.
Does the solution meet the goal?
| A. Yes | B. No |
B. No
Question # 3
You need to select a feature extraction method.
Which method should you use? | A. Spearman correlation | B. Mutual information | C. Mann-Whitney test | D. Pearson’s correlation |
A. Spearman correlation
Explanation:
Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.
Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.
Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
References:
https://docs.microsoft.com/en-us/azure/machine-lear ning/studio-module-reference/feature-selection-modules
Question # 4
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do? | A. Apply an analysis of variance (ANOVA). | B. Apply a Pearson correlation coefficient. | C. Apply a Spearman correlation coefficient. | D. Apply a linear discriminant analysis. |
D. Apply a linear discriminant analysis.
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Question # 5
You manage an Azure Machine learning workspace. The workspace includes an Azure Machine Learning kubernetes compute target configured as an Azure Kubemetes Service (AKS) cluster named AKS1 AKS1 is configured to enable the targeting of different nodes to train workloads.
You must run a command job on AK51 by using the Azure ML Python SDK v2? The command job must select different types of compute nodes. The compare node types must be specified by using a command parameter.
You need to configure the command parameter.
Which parameter should you use?
| A. compute | B. environment | C. instance_type | D. limits |
Explanation:
from azure.ai.ml import command
# define the command
command_job = command(
command="python -c "print('Hello world!')"",
environment="AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu@latest",
compute="",
instance_type=""
Question # 6
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
Question # 7
You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.
You need to identify the tasks you will be able to perform in the workspace.
Which three tasks will you be able to perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point. | A. Create a Compute Instance and use it to run code in Jupyter notebooks. | B. Create an Azure Kubernetes Service (AKS) inference cluster. | C. Use the designer to train a model by dragging and dropping pre-defined modules. | D. Create a tabular dataset that supports versioning. | E. Use the Automated Machine Learning user interface to train a model. |
A. Create a Compute Instance and use it to run code in Jupyter notebooks. B. Create an Azure Kubernetes Service (AKS) inference cluster. D. Create a tabular dataset that supports versioning.
Explanation:
[Reference:, https://azure.microsoft.com/en-us/pricing/details/machine-learning/, , , ]
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