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NEW QUESTION: 1
What generally means removing the risk to the company by paying for the protection of an insurance policy?
A. Collateral
B. Security
C. Self-assurance
D. Risk transfer
Answer: D
NEW QUESTION: 2
It is absolutely ______ that the individual assigned to defuse a bomb be extremely ______.
A. necessary ... excited
B. critical ... dexterous
C. essential ... mature
D. warranted ... tired
E. alright ... nervous
Answer: B
Explanation:
Explanation/Reference:
Here again, the strategy is to divide and conquer. Only Choices A, C, and E qualify as being suited with
absolutely. This leaves the second blank. Now the qualities for someone to defuse a bomb are almost a
given. Choice A, "mature," is not something good as it is tantamount to feeble--much shaking of the hands
is not a great characteristic to have as a bomb technician. Choice C, "excited," is just as bad as too
mature and shaking. This only leave Choice E, "dexterous," or someone with skillful hands.
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
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