NCP-CI-Azure Practice Questions

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NEW QUESTION: 1
Why is it important to conduct a discover meeting with your clients?
A. It provides the perfect opportunity to showcase your client visualization hardware.
B. It helps you show your Knowledge of client-virtualization.
C. It provides an opportunity to talk about HP's integration with industry leading clientvirtualization ISVs
D. It helps you determine where they are in the process of moving towards client visualization.
Answer: C

NEW QUESTION: 2
Which components of an Exadata storage server image, if updated, may require that a patch also be applied to the database servers in an Exadata X6 Database Machine?
A. Storage server hard disk device drivers
B. InfiniBand HCA firmware on the storage server
C. Storage server flash device drivers
D. Linux operating system on the storage server
Answer: C
Explanation:
Exadata patches are intended for and include fixes for both the storage servers and
Compute servers, and optionally InfiniBand switches.
Patching order
You should patch the Exadata Database Machines in the following sequence
References: https://www.toadworld.com/platforms/oracle/w/wiki/11640.oracle-exadata-patching

NEW QUESTION: 3
開発者は、boto3スクリプトを使用してAmazon EC2インスタンスを起動または終了しようとすると、次のエラーメッセージを受け取ります。

このエラーメッセージを修正するには、開発者は何をする必要がありますか？
A. IAMロールをEC2インスタンスに割り当てて、クライアントに代わって必要なAPI呼び出しを許可します。
B. AmazonEC2に対して行われるAPIリクエストの数を最適化するための指数バックオフアルゴリズムを実装します。
C. 全体的なネットワーク帯域幅を増やして、より高いAPIリクエストレートを処理します。
D. 最新のAWS CLIバージョンにアップグレードして、boto3がより高いリクエストレートを処理できるようにします
Answer: D

NEW QUESTION: 4
You have an Apache Spark cluster in Azure HDInsight. The cluster includes 200 TB in five Apache Hive tables that have multiple foreign key relationships.
You have an Azure Machine Learning model that was built by using SPARK Accelerated Failure Time (AFT) Survival Regression Model (spark.survreg).
You need to prepare the Hive data into a single table as input for the Machine Learning model. The Hive data must be prepared in the least amount of time possible. What should you use to prepare the data?
A. Java Mapreduce jobs
B. a Hive user-defined function (UDF)
C. the GPU
D. Spark SQL
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Create features for data in an HDInsight Hadoop cluster using Hive queries. Feature engineering attempts to increase the predictive power of learning algorithms by creating features from raw data that facilitate the learning process. You can run HiveQL queries from Azure ML, and access data processed in Hive and stored in blob storage, by using the Import Data module.
References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-machine-learning-overview#azure- machine-learning-and-hive