You have an Azure Synapse Analytics Apache Spark pool named Pool1.You plan to load JSON files from an Azure Data Lake Storage Gen2 container into the tables in Pool1. The structure and data types vary by file.You need to load the files into the tables. The solution must maintain the source data types. What should you do?
Answer(s): D
You have an Azure Databricks workspace named workspace1 in the Standard pricing tier. Workspace1 contains an all-purpose cluster named cluster1.You need to reduce the time it takes for cluster1 to start and scale up. The solution must minimize costs. What should you do first?
You can use Databricks Pools to Speed up your Data Pipelines and Scale Clusters Quickly.Databricks Pools, a managed cache of virtual machine instances that enables clusters to start and scale 4 times faster.
https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html
HOTSPOT (Drag and Drop is not supported)You are building an Azure Stream Analytics job that queries reference data from a product catalog file. The file is updated daily.The reference data input details for the file are shown in the Input exhibit. (Click the Input tab.)The storage account container view is shown in the Refdata exhibit. (Click the Refdata tab.)You need to configure the Stream Analytics job to pick up the new reference data.What should you configure? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.Hot Area:
Answer(s): A
Box 1: {date}/product.csvIn the 2nd exhibit we see: Location: refdata / 2020-03-20Note: Path Pattern: This is a required property that is used to locate your blobs within the specified container. Within the path, you may choose to specify one or more instances of the following 2 variables:{date}, {time}Example 1: products/{date}/{time}/product-list.csvExample 2: products/{date}/product-list.csvExample 3: product-list.csvBox 2: YYYY-MM-DDNote: Date Format [optional]: If you have used {date} within the Path Pattern that you specified, then you can select the date format in which your blobs are organized from the drop-down of supported formats.Example: YYYY/MM/DD, MM/DD/YYYY, etc.
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
HOTSPOT (Drag and Drop is not supported)You have the following Azure Stream Analytics query.For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.Hot Area:
Box 1: NoNote: You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.The outcome is a stream that has the same partition scheme. Please see below for an example: WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10),step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)SELECT * INTO [output] FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.Box 2: YesWhen joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.Box 3: YesStreaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job.In general, the best practice is to start with 6 SUs for queries that don't use PARTITION BY. Here there are 10 partitions, so 6x10 = 60 SUs is good.Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/ https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption
HOTSPOT (Drag and Drop is not supported)You are building a database in an Azure Synapse Analytics serverless SQL pool. You have data stored in Parquet files in an Azure Data Lake Storege Gen2 container. Records are structured as shown in the following sample.{"id": 123,"address_housenumber": "19c", "address_line": "Memory Lane", "applicant1_name": "Jane", "applicant2_name": "Dev"}The records contain two applicants at most.You need to build a table that includes only the address fields.How should you complete the Transact-SQL statement? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point. Hot Area:
Box 1: CREATE EXTERNAL TABLEAn external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.Syntax:CREATE EXTERNAL TABLE { database_name.schema_name.table_name | schema_name.table_name | table_name }( <column_definition> [ ,...n ] ) WITH (LOCATION = 'folder_or_filepath', DATA_SOURCE = external_data_source_name, FILE_FORMAT = external_file_format_nameBox 2. OPENROWSETWhen using serverless SQL pool, CETAS is used to create an external table and export query results to Azure Storage Blob or Azure Data Lake Storage Gen2.Example:ASSELECT decennialTime, stateName, SUM(population) AS population FROMOPENROWSET(BULK 'https://azureopendatastorage.blob.core.windows.net/censusdatacontainer/release/ us_population_county/year=*/*.parquet',FORMAT='PARQUET') AS [r]GROUP BY decennialTime, stateName GO
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables
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