A. Option C
B. Option A
C. Option B
D. Option D
Answer: B
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NEW QUESTION: 1
Evaluate the following SQL statement:
SQL> SELECT cust_id, cust_last_name "Last Name" FROM customers WHERE country_id = 10 UNION
SELECT cust_id CUST_NO, cust_last_name
FROM customers
WHERE country_id = 30;
Which ORDER BY clause are valid for the above query? (Choose all that apply.)
A. ORDER BY 2,cust_id
B. ORDER BY "CUST_NO"
C. ORDER BY CUST_NO
D. ORDER BY "Last Name"
E. ORDER BY 2,1
Answer: A,D,E
Explanation:
Using the ORDER BY Clause in Set Operations
-The ORDER BY clause can appear only once at the end of the compound query.
-Component queries cannot have individual ORDER BY clauses.
-The ORDER BY clause recognizes only the columns of the first SELECT query.
-By default, the first column of the first SELECT query is used to sort the output in an ascending order.
NEW QUESTION: 2
A. Option C
B. Option A
C. Option B
D. Option D
Answer: B
NEW QUESTION: 3
한 회사가 Amazon EC2 인스턴스에서 분석 소프트웨어를 실행합니다. 소프트웨어는 Amazon S3에 업로드 된 데이터를 처리하기 위해 사용자의 작업 요청을 수락합니다. 사용자가 제출한 일부 데이터가 처리되지 않는다고 보고합니다. Amazon CloudWatch는 EC2 인스턴스가 또는 근처에 100 % 회사는 시스템 성능을 개선하고 사용자 부하에 따라 시스템을 확장하고자 합니다. 솔루션 설계자는 이러한 요구 사항을 충족하기 위해 무엇을 해야합니까?
A. 인스턴스 사본 생성 Application Load Balancer 뒤에 모든 인스턴스 배치
B. 수신 요청을 Amazon Simple Queue Service (Amazon SQS)로 라우팅 대기열 크기에 따라 EC2 Auto Scaling 그룹 구성 대기열에서 읽을 소프트웨어 업데이트
C. EC2 인스턴스 중지 더 강력한 CPU와 더 많은 메모리가 있는 인스턴스 유형으로 수정 인스턴스 다시 시작
D. Amazon S3 용 S3 VPC 엔드 포인트 생성 엔드 포인트를 참조하도록 소프트웨어를 업데이트합니다.
Answer: A
NEW QUESTION: 4
機械学習モデルを使用してインテリジェントなソリューションを構築しています。
環境は、次の要件をサポートする必要があります。
*データサイエンティストはクラウド環境でノートブックを作成する必要があります
*データサイエンティストは、機械学習パイプラインで自動機能エンジニアリングとモデル構築を使用する必要があります。
*動的なワーカー割り当てでSparkインスタンスを使用して再トレーニングするには、ノートブックをデプロイする必要があります。
*ノートブックは、ローカルでバージョン管理できるようにエクスポート可能である必要があります。
環境を作成する必要があります。
どの4つのアクションを順番に実行する必要がありますか？回答するには、適切なアクションをアクションのリストから回答領域に移動し、正しい順序で配置します。
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