Microsoft Design and Implement Big Data Analytics Solutions (070-475日本語版) - 070-475日本語 Exam Practice Test
Correct Answer:
Explanation
A table created without the EXTERNAL clause is called a managed table because Hive manages its data.
Correct Answer:
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
* Batch
* Speed
* Serving
Correct Answer:
Explanation
Box 1: No
Box 2: No
Output from the batch and speed layers are stored in the serving layer, which responds to ad-hoc queries by returning precomputed views or building views from the processed data.
Box 3: Yes.
We are excited to announce Interactive Queries, a new feature for stream processing with Apache Kafka.
Interactive Queries allows you to get more than just processing from streaming.
Note: Lambda architecture is a popular choice where you see stream data pipelines applied (speed layer).
Architects can combine Apache Kafka or Azure Event Hubs (ingest) with Apache Storm (event processing), Apache HBase (speed layer), Hadoop for storing the master dataset (batch layer), and, finally, Microsoft Power BI for reporting and visualization (serving layer).
Correct Answer:
Explanation
References:
https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/data-factory/v1/data-factory-use-custom-activ