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NEW QUESTION: 1
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

NEW QUESTION: 2
A customer moves in to a premise and requests electricity service starting on June 7. According to the schedule records, the scheduled meter reading date is June 29, the scheduled billing date is July 1, and the allocation date for billing is June 30.
Given that batch billing is executed every night with all portions included, what is the first day the customer's contract could be billed?
A. June 29
B. June 7
C. July 1
D. June 30
Answer: C

NEW QUESTION: 3
Sie arbeiten als Senior Administrator bei Contoso.com. Das Contoso.com-Netzwerk besteht aus einer einzelnen Domäne mit dem Namen Contoso.com. Auf allen Servern im Contoso.com-Netzwerk ist Windows Server 2012 installiert.
Sie führen eine Schulungsübung für Junioradministratoren durch. Sie diskutieren derzeit über übergreifende Volumes.
Welche der folgenden Aussagen ist in Bezug auf übergreifende Volumes WAHR? (Wähle alle, die zutreffen.)
A. Übergreifende Volumes sind eine fehlertolerante Lösung.
B. Übergreifende Volumes bieten keine Fehlertoleranz.
C. Sie können ein übergreifendes Volume auf maximal 16 dynamische Datenträger erweitern.
D. Sie können kein übergreifendes Volume mit einem System- oder Startvolume erstellen.
Answer: B,D
Explanation:
Erläuterung
Ein übergreifendes Volume ist ein dynamisches Volume, das aus Speicherplatz auf mehr als einer physischen Festplatte besteht. Wenn es sich bei einem einfachen Volume nicht um ein System- oder Startvolume handelt, können Sie es auf weitere Festplatten (insgesamt bis zu 32) erweitern, um ein übergreifendes Volume zu erstellen, oder Sie können ein übergreifendes Volume in nicht zugeordnetem Speicherplatz auf einer dynamischen Festplatte erstellen.
Sie benötigen mindestens zwei dynamische Datenträger zusätzlich zum Startdatenträger, um ein übergreifendes Volume zu erstellen. Sie können ein übergreifendes Volume auf maximal 32 dynamische Datenträger erweitern.
Übergreifende Volumes sind nicht fehlertolerant.