Category: DP-100 Exam Dumps

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QUESTION 264
You need to consider the underlined segment to establish whether it is accurate.
To improve the amount of low incidence cases in a dataset, you should make use of the SMOTE module.
Select “No adjustment required” if the underlined segment is accurate. If the underlined segment is inaccurate, select the accurate option.

A. No adjustment required.
B. Remove Duplicate Rows
C. Join Data
D. Edit Metadata

Answer: A
Explanation:
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underrepresented 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.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

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QUESTION 190
You create a batch inference pipeline by using the Azure ML SDK.
You configure the pipeline parameters by executing the following code:

You need to obtain the output from the pipeline execution.
Where will you find the output?

A. the digit_identification.py script
B. the debug log
C. the Activity Log in the Azure portal for the Machine Learning workspace
D. the Inference Clusters tab in Machine Learning studio
E. a file named parallel_run_step.txt located in the output folder

Answer: E
Explanation:
output_action (str): How the output is to be organized. Currently supported values are ‘append_row’ and ‘summary_only’.
‘append_row’ ?All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
‘summary_only’
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig

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2020/November Latest Braindump2go DP-100 Exam Dumps with PDF and VCE Free Updated Today! Following are some new DP-100 Real Exam Questions!

QUESTION 190
You create a batch inference pipeline by using the Azure ML SDK.
You configure the pipeline parameters by executing the following code:

You need to obtain the output from the pipeline execution.
Where will you find the output?

A. the digit_identification.py script
B. the debug log
C. the Activity Log in the Azure portal for the Machine Learning workspace
D. the Inference Clusters tab in Machine Learning studio
E. a file named parallel_run_step.txt located in the output folder

Answer: E
Explanation:
output_action (str): How the output is to be organized. Currently supported values are ‘append_row’ and ‘summary_only’.
‘append_row’ ?All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
‘summary_only’
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig

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QUESTION 71
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 are analyzing a numerical dataset which contain missing values in several columns. You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Use the last Observation Carried Forward (IOCF) method to impute the missing data points.
Does the solution meet the goal?

A. Yes
B. No

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