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NEW QUESTION: 1
Note: This question is part of a series of questions that use the same scenario, For your convenience is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is the same in each question in this series.
Start of repeated scenario
You have six workbook queries that each extracts a table from a Microsoft Azure SQL database. The tables are loaded to the data model, but the data is not loaded to any worksheets. The data model is shown in the Data Model exhibit.
Your company has 100 product subcategories and more than 10,000 products.
End of repeated scenario.
You need to create a chart as shown in the following exhibit.
Which field should you use for each area? To answer, drag the appropriate fields to the correct areas. Each field may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION: 2
プルーフオブコンセプト（POC）を設定していて、オンプレミスのデータセンターとOracle Cloud Infrastructure（OCI）の間の安全を迅速に確立する必要があります。
どのOCIサービスを実装する必要がありますか？
A. FastConnect
B. IPSec VPN
C. VCN Peering
D. Internet Gateway
Answer: B
Explanation:
Explanation
You can set up a single IPSec VPN with a simple layout that you might use for a proof of concept (POC).
NEW QUESTION: 3
An ArcGIS user is trying to add data from an enterprise database to an ArcMap document but is unable to see any feature classes in the database.
Why is the ArcGIS user unable to add the data?
A. The ArcGIS user has inappropriate permissions to access the data
B. A folder connection between ArcCatalog and the data location is missing
C. Another user has a schema lock on that database
D. Another user owns the data in a public version
Answer: A
NEW QUESTION: 4
A company is using a fleet of Amazon EC2 instances to ingest data from on-premises data sources. The data is in JSON format and ingestion rates can be as high as 1 MB/s. When an EC2 instance is rebooted, the data in-flight is lost. The company's data science team wants to query ingested data in near-real time.
Which solution provides near-real-time data querying that is scalable with minimal data loss?
A. Publish data to Amazon Kinesis Data firehose with Amazon Redshift as the destination. Use Amazon Redshift to query the data.
B. Publish data to Amazon Kinesis Data Streams. Use Kinesis Data Analytics to query the data.
C. Store ingested data in an Amazon Elastic Block Store (Amazon EBS) volume. Publish data to Amazon ElastiCache for Redis. Subscribe to the Redis channel to query the data.
D. Store ingested data in an EC2 instance store Publish data to Amazon Kinesis Data Firehose with Amazon S3 as the destination. Use Amazon Athena to query the data.
Answer: B
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