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DP-100 Practice Questions

Question # 1
You need to implement a model development strategy to determine a user’s tendency to respond to an ad. Which technique should you use?
A. Use a Relative Expression Split module to partition the data based on centroid distance.
B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
C. Use a Split Rows module to partition the data based on distance travelled to the event.
D. Use a Split Rows module to partition the data based on centroid distance.


A. Use a Relative Expression Split module to partition the data based on centroid distance.

Explanation:

Split Data partitions the rows of a dataset into two distinct sets.

The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.

Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.

Scenario:

Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.

The distribution of features across training and production data are not consistent

References:

https://docs.microsoft.co m/en-us/azure/machine-learning/studio-module-reference/split-data


Question # 2
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 # 3
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events. Model performance has degraded due to model input data changes. You need to trigger a remediation ML pipeline based on an Azure Machine Learning event. Which event should you use?
A. RunStatusChanged
B. DatasetDriftDetected
C. ModelDeployed
D. RunCompleted


B. DatasetDriftDetected


Question # 4
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 # 5
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 # 6
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 # 7
You arc 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
B. Replace using Probabilistic PAC
C. Replace using MICE
D. Normalization


B. Replace using Probabilistic PAC


Question # 8
You are solving a classification task. The dataset is imbalanced. You need to select an Azure Machine Learning Studio module to improve the classification accuracy. Which module should you use?
A. Fisher Linear Discriminant Analysis.
B. Filter Based Feature Selection
C. Synthetic Minority Oversampling Technique (SMOTE)
D. Permutation Feature Importance


C. Synthetic Minority Oversampling Technique (SMOTE)

Explanation:

Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.

[Reference:, https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote, , ]


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Exam Name: Designing and Implementing a Data Science Solution on Azure Exam
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