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NEW QUESTION: 1
PowerBIモデルにはCustomersとInvoiceという名前の2つのテーブルがあります。 Customersテーブルには、次のフィールドが含まれています。
* 顧客ID
*カスタマーシティ
*お客様の状態
* 顧客名
*お客様の住所1
*顧客の住所2
*顧客の郵便番号
請求書テーブルには、次のフィールドが含まれています。
*注文ID
*請求書ID
* 請求書の日付
* 顧客ID
* 合計金額
*合計アイテム数
Customersテーブルは、CustomerID列を介してInvoiceテーブルに関連付けられています。顧客は1か月以内に多くの請求書を持つことができます。
Power BIモデルは、次の情報を提供する必要があります。
*先月各州で請求された顧客の数
*各郵便番号の顧客あたりの平均請求額
CustomersテーブルからInvoiceテーブルへの関係を定義する必要があります。ソリューションはクエリのパフォーマンスを最適化する必要があります。
何を設定する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Reference:
https://docs.microsoft.com/en-us/power-bi/transform-model/desktop-relationships-understand

NEW QUESTION: 2
You are talking to customer about Nutanix as a true turnkey solution. The customer askshow his benefits an IT administrator. Which value proposition should you propose?
A. Reduce cost of license for end users
B. Reduce chance of overspending by enabling predictable financial analysis
C. Reduce time to deployment and complexity of management
D. Reduce amount of power consumed in the data center
Answer: C

NEW QUESTION: 3
Successfully resolve to server1.example.com where your DNS server is 172.24.254.254.
Answer:
Explanation:
see explanation below.
Explanation
* vi /etc/resolv.conf
nameserver 172.24.254.254
* host server1.example.com
On every clients, DNS server is specified in /etc/resolv.conf. When you request by name it tries to resolv from DNS server.

NEW QUESTION: 4
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?
A. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
B. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
C. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
D. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
Answer: A

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• H3C H3CNE-Cloud
• H3C Certified Entry Networking Technician