DP-100 Exam Dumps

[November-2020]Valid DP-100 VCE and PDF Free Download in Braindump2go[Q190-Q212]

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 190You 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 scriptB. the debug logC. the Activity Log in the Azure portal for the Machine Learning workspaceD. the Inference Clusters tab in Machine Learning studioE. a file named parallel_run_step.txt located in the output folder Answer: EExplanation: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

DP-100 Exam Dumps

[May-2020-New]High Quality Braindump2go DP-100 Exam Dumps PDF and VCE 129Q Free Share(71-84)

May/2020 New Braindump2go DP-100 Exam Dumps with PDF and VCE Free Updated Today! Following are some new DP-100 Exam Questions! QUESTION 71Note: 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…