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NEW QUESTION: 1
この質問では、下線付きテキストを評価して、正しいかどうかを判断する必要があります。
Azure Databricksは、Apache Sparkベースの分析サービスです。
手順：下線付きテキストを確認します。ステートメントが正しい場合は、「変更は不要です」を選択します。ステートメントが間違っている場合は、ステートメントが正しい答えの選択肢を選択してください。
A. Azure DevOps
B. Azure HDInsight
C. Azure Data Factory
D. 変更する必要はありません。
Answer: D
Explanation:
Explanation
Azure Databricks is an Apache Spark-based analytics platform. The platform consists of several components including 'MLib'. Mlib is a Machine Learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.
References:
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks#apache-spark-based-analytics-

NEW QUESTION: 2
Which of the following statements is CORRECT?
1.The only phaseof the Service Management Lifecycle where value can be measured is Service
Operation
2.All of the phases of the lifecycle are concerned with the value of IT services
A. 1 only
B. Neither of the above
C. 2 only
D. Both of the above
E. Explanation:
QUESTIONNO: 269 Which of the following is NOT an aim of the Change Management process?
A. Overall business risk is optimized
B. Standardized methods and procedures are used for efficient and prompt handling of all Changes
C. All budgets and expenditures areaccounted for
D. All changes to Service Assets and Configuration Items (CIs) are recorded in the Configuration Management system
Answer: C

NEW QUESTION: 3
조직에는 매일 10GB의 원격 측정 데이터를 생성하는 10,000 개의 장치가 있으며 각 레코드 크기는 약 10KB입니다. 각 레코드에는 100 개의 필드가 있으며 하나의 필드는 영어로 된 "문자열"데이터 유형의 비정형 로그 데이터로 구성됩니다. 일부 필드는 실시간 대시 보드에 필요하지만 모든 필드는 장기 생성에 사용할 수 있어야 합니다.
또한 한 달 내에 AWS로 마이그레이션해야 하는 SAN에 10 PB의 이전에 정리되고 구조화 된 데이터가 Date로 분할되어 있습니다. 현재 조직에는 솔루션에 실시간 기능이 없습니다. 온-프레미스 데이터웨어 하우스의 스토리지 제한으로 인해 시각화를 위해 JDBC를 통해 ANSI SQL 쿼리로 장기 추세를 생성하는 동안 선택적 데이터가 로드됩니다. 조직은 일회성 데이터로드 외에도 비용 효율적이고 실시간 솔루션이 필요합니다.
이러한 요구 사항을 어떻게 충족시킬 수 있습니까? (2 개를 선택하십시오.)
A. AWS와 온 프레미스 데이터 센터간에 Direct Connect 연결을 생성하고 S3 가속을 사용하여 데이터를 Amazon S3에 복사하십시오. Amazon Athena를 사용하여 데이터를 쿼리하십시오.
B. IoT 규칙 엔진을 사용하여 AWS IoT를 사용하여 디바이스에서 Amazon Kinesis Data Streams로 데이터를 보냅니다.
Kinesis 스트림에 연결된 하나의 Kinesis Data Firehose 스트림을 사용하여 날짜별로 분할 된 데이터를 일괄 처리하고 스트리밍합니다. 동일한 Kinesis 스트림에 연결된 다른 Kinesis Firehose 스트림을 사용하여 실시간 분석을 위해 Elasticsearch로 수집하는데 필요한 필드를 필터링하십시오.
C. 여러 AWS Snowball Edge 디바이스를 사용하여 데이터를 Amazon S3로 전송하고 Amazon Athena를 사용하여 데이터를 쿼리하십시오.
D. AWS IoT를 사용하여 디바이스에서 Amazon SQS 대기열로 데이터를 전송하고, Auto Scaling 그룹에서 작업자 세트를 생성하고, 대기열에서 일괄 적으로 레코드를 읽어 데이터를 처리하고 저장합니다. 요청 데이터 세트를 필터링하고 실시간 분석을 위해 Amazon Elasticsearch Service에 저장하려면 AWS Lambda 함수가 연결된 Amazon SNS 대기열로 팬 아웃하십시오.
E. IoT 규칙 엔진을 사용하여 AWS IoT를 사용하여 디바이스에서 Amazon Kinesis Data Streams로 데이터를 보냅니다.
Kinesis 스트림에 연결된 하나의 Kinesis Data Firehose 스트림을 사용하여 날짜별로 분할 된 Amazon S3 버킷으로 데이터를 스트리밍합니다. 동일한 Kinesis 스트림과 함께 AWS Lambda 함수를 연결하여 실시간 분석을 위해 Amazon DynamoDB에 수집하는데 필요한 필드를 필터링하십시오.
Answer: C,E