最新的Microsoft Design and Implement Big Data Analytics Solutions (70-475日本語版) - 70-475日本語免費考試真題

Explanation

Box 1: Every three days at 10.00
anchorDateTime defines the absolute position in time used by the scheduler to compute dataset slice boundaries.
"frequency": "<Specifies the time unit for data slice production. Supported frequency: Minute, Hour, Day, Week, Month>",
"interval": "<Specifies the interval within the defined frequency. For example, frequency set to 'Hour' and interval set to 1 indicates that new data slices should be produced hourly> Box 2: Every minute up to three times.
retryInterval is the wait time between a failure and the next attempt. This setting applies to present time. If the previous try failed, the next try is after the retryInterval period.
Example: 00:01:00 (1 minute)
Example: If it is 1:00 PM right now, we begin the first try. If the duration to complete the first validation check is 1 minute and the operation failed, the next retry is at 1:00 + 1min (duration) + 1min (retry interval) =
1:02 PM.
For slices in the past, there is no delay. The retry happens immediately.
retryTimeout is the timeout for each retry attempt.
maximumRetry is the number of times to check for the availability of the external data.

Explanation

Box 1: Azure Data Lake Store
Stream Analytics supports Azure Data Lake Store. Azure Data Lake Store is an enterprise-wide hyper-scale repository for big data analytic workloads. Data Lake Store enables you to store data of any size, type and ingestion speed for operational and exploratory analytics. Stream Analytics has to be authorized to access the Data Lake Store.
Box 2: Azure Cosmos DB
Stream Analytics can target Azure Cosmos DB for JSON output, enabling data archiving and low-latency queries on unstructured JSON data.
Box 3: Azure Blob Storage
Blob storage offers a cost-effective and scalable solution for storing large amounts of unstructured data in the cloud.
Incorrect Asnwers:
Azure SQL Database:
Azure SQL Database can be used as an output for data that is relational in nature or for applications that depend on content being hosted in a relational database. Stream Analytics jobs write to an existing table in an Azure SQL Database.
Azure Service Bus Queue:
Service Bus Queues offer a First In, First Out (FIFO) message delivery to one or more competing consumers.
Typically, messages are expected to be received and processed by the receivers in the temporal order in which they were added to the queue, and each message is received and processed by only one message consumer.
Azure Table Storage
Azure Table storage offers highly available, massively scalable storage, so that an application can automatically scale to meet user demand. Table storage is Microsoft's NoSQL key/attribute store, which one can leverage for structured data with fewer constraints on the schema. Azure Table storage can be used to store data for persistence and efficient retrieval.
References: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs

Explanation

Group A: Tumbling
Tumbling Windows define a repeating, non-overlapping window of time.
Group B: Hopping
Like Tumbling Windows, Hopping Windows move forward in time by a fixed period but they can overlap with one another.

Explanation

References:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/data-factory/v1/data-factory-use-custom-activ

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