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NEW QUESTION: 1
A. Option B
B. Option C
C. Option D
D. Option A
Answer: D
Explanation:
Explanation
https://www.cisco.com/c/dam/en/us/products/collateral/software/one-software/one-overview-deck.pdf
NEW QUESTION: 2
You have an Azure subscription that contains several virtual machines and an Azure Log Analytics workspace named Workspace1. You create a log search query as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: 14 days
Two weeks will be covered.
Note: Startofweek returns the start of the week containing the date, shifted by an offset, if provided.
Start of the week is considered to be a Sunday.
Endofweek returns the end of the week containing the date, shifted by an offset, if provided.
Last day of the week is considered to be a Saturday.
Box 2:
The render operator renders results in as graphical output. Timechart is a Line graph, where the first column is x-axis, and should be datetime. Other columns are y-axes. In this case the Y axis has avg(CounterValue) Values.
References:
https://docs.microsoft.com/en-us/azure/azure-monitor/log-query/log-query-overview
https://docs-analytics-eus.azurewebsites.net/queryLanguage/query_language_renderoperator.html
NEW QUESTION: 3
You have two issue types in your HR project: Hire and Fire.
Which two requirements can be met through changes only to field configurations? (Choose two.)
A. For both issue types, the Employee Name custom field must be required upon creation and must never be blank.
B. For both issue types, the Assignee field should be hidden when creating but not when viewing an issue.
C. For the Fire issue type, the Description field should have a default value of "Termination".
D. For the Fire issue type, the default value for the Priority field should be Blocker.
E. For the Hire issue type, the field description for the Due Date field should read "First day of work".
F. For both issue types, the Resolution should be set once the issues are being resolved.
Answer: B,F
NEW QUESTION: 4
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 ML experiment that contains an intermediate dataset.
You need to explore data from the intermediate dataset by using Jupyter.
Solution: You add a Convert to CSV module to the Azure ML experiment and then open the module output in a new notebook.
Does this meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Access intermediate datasets from Machine Learning experiments
The following steps show an example that creates an experiment, runs it and accesses the intermediate dataset.
1. Create a new experiment.
2. Insert an Adult Census Income Binary Classification dataset module.
3. Insert a Split module, and connect its input to the dataset module output.
4. Insert a Convert to CSV module and connect its input to one of the Split module outputs.
5. Save the experiment, run it, and wait for it to finish running.
6. Click the output node on the Convert to CSV module.
7. When the context menu appears, select Generate Data Access Code
8. Select the code snippet and copy it to your clipboard from the window that appears..
9. Paste the code in your notebook.
Note: After an experiment is run in the Machine Learning Studio, it is possible to access the intermediate datasets from the output nodes of modules. Intermediate datasets are data that has been created and used for intermediate steps when a model tool has been run.
Intermediate datasets can be accessed as long as the data format is compatible with the Python client library.
The following formats are supported (constants for these are in the azureml.DataTypeIds class):
PlainText
GenericCSV
GenericTSV
GenericCSVNoHeader
GenericTSVNoHeader
References: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/python- data-access