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NEW QUESTION: 1
アプリケーションがプロビジョニングされた容量よりも多くの読み取りまたは書き込みを実行するとどうなりますか？
A. プロビジョニングされた容量を超えるリクエストは実行されますが、400個のエラーコードを受け取ります。
B. プロビジョニングされた容量を超えるリクエストは実行されますが、200個のエラーコードを受け取ります。
C. なし
D. プロビジョニングされた容量を超えるリクエストは抑制され、400個のエラーコードを受け取ります。
Answer: D
Explanation:
DynamoDBについて言えば、アプリケーションがテーブルのプロビジョニングされたスループット容量を超える読み取り/秒または書き込み/秒を実行する場合、プロビジョニングされた容量を超えるリクエストは抑制され、400のエラーコードを受け取ります。
NEW QUESTION: 2
ネットワークエンジニアがsnmp-server user andy myv3 auth sha cisco priv aes 256 cisc0380739941コマンドを入力し、SNMP情報を10.255.254.1のホストに送信する必要があります。この目標を達成するコマンドはどれですか。
A. snmp-server host inside 10.255.254.1 snmpv3 myv3
B. snmp-server host inside 10.255.254.1 version 3 myv3
C. snmp-server host inside 10.255.254.1 version 3 andy
D. snmp-server host inside 10.255.254.1 snmpv3 andy
Answer: C
NEW QUESTION: 3
Answer:
Explanation:
NEW QUESTION: 4
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
