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NEW QUESTION: 1
In which two circumstances will the overwrite protection period for a tape begin? (Select two.)
A. at the end time of the last append operation on a media
B. at the start time of the last append operation on a media
C. at the end time of the first append operation on a media
D. at the end time of an overwrite job (if there are no appends)
E. at the start time of an overwrite job (if there are no appends)
Answer: C,D
Explanation:
Explanation/Reference:
Reference: https://www.veritas.com/support/en_US/article.000109170

NEW QUESTION: 2
Which statement about Auto-RP is true ?
A. All interfaces must be configured in sparse-dense mode.
B. All interfaces must be configured in dense modE.
C. All interface s must be configured in sparse modE.
D. An RP that it tied to a loopback address must be configured.
Answer: A

NEW QUESTION: 3
You are building an intelligent solution using machine learning models.
The environment must support the following requirements:
Data scientists must build notebooks in a cloud environment
Data scientists must use automatic feature engineering and model building in machine learning pipelines.
Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
Notebooks must be exportable to be version controlled locally.
You need to create the environment.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library Step 2: Install Microsot Machine Learning for Apache Spark You install AzureML on your Azure HDInsight cluster.
Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.
Step 3: Create and execute the Zeppelin notebooks on the cluster
Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.
Notebooks must be exportable to be version controlled locally.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook
https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

NEW QUESTION: 4

A. Option A
B. Option E
C. Option B
D. Option C
E. Option D
Answer: A,B,D