Microsoft DP-203 Practice Test - Questions Answers, Page 20
List of questions
Question 191
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You have an activity in an Azure Data Factory pipeline. The activity calls a stored procedure in a data warehouse in Azure Synapse Analytics and runs daily.
You need to verify the duration of the activity when it ran last. What should you use?
Explanation:
Monitor activity runs. To get a detailed view of the individual activity runs of a specific pipeline run, click on the pipeline name. Example:
The list view shows activity runs that correspond to each pipeline run. Hover over the specific activity run to get run-specific information such as the JSON input, JSON output, and detailed activity-specific monitoring experiences.
You can check the Duration.
Incorrect Answers:
C: sys.dm_pdw_wait_stats holds information related to the SQL Server OS state related to instances running on the different nodes.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-visually
Question 192
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HOTSPOT
You need to design a data storage structure for the product sales transactions. The solution must meet the sales transaction dataset requirements. What should you include in the solution? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Hash Scenario:
Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible. A hash distributed table can deliver the highest query performance for joins and aggregations on large tables. Box 2: Set the distribution column to the sales date.
Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
Reference: https://rajanieshkaushikk.com/2020/09/09/how-to-choose-right-data-distribution-strategy-for-azure-synapse/
Question 193
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DRAG DROP
You use PySpark in Azure Databricks to parse the following JSON input.
You need to output the data in the following tabular format.
How should you complete the PySpark code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the spit bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: select
Box 2: explode
Bop 3: alias pyspark.sql.Column.alias returns this column aliased with a new name or names (in the case of expressions that return more than one column, such as explode).
Reference:
https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.Column.alias.html
https://docs.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/explode
Question 194
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HOTSPOT
You are designing an application that will store petabytes of medical imaging data. When the data is first created, the data will be accessed frequently during the first week. After one month, the data must be accessible within 30 seconds, but files will be accessed infrequently. After one year, the data will be accessed infrequently but must be accessible within five minutes.
You need to select a storage strategy for the data. The solution must minimize costs. Which storage tier should you use for each time frame? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Hot
Hot tier - An online tier optimized for storing data that is accessed or modified frequently. The Hot tier has the highest storage costs, but the lowest access costs. Box 2: Cool
Cool tier - An online tier optimized for storing data that is infrequently accessed or modified. Data in the Cool tier should be stored for a minimum of 30 days. The Cool tier has lower storage costs and higher access costs compared to the Hot tier.
Box 3: Cool
Not Archive tier - An offline tier optimized for storing data that is rarely accessed, and that has flexible latency requirements, on the order of hours. Data in the Archive tier should be stored for a minimum of 180 days.
Reference: https://docs.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview https://www.altaro.com/hyperv/azure-archive-storage/
Question 195
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HOTSPOT
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.
Explanation:
Box 1: {date}/product.csv
In the 2nd exhibit we see: Location: refdata / 2020-03-20
Note: 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.csv
Example 2: products/{date}/product-list.csv
Example 3: product-list.csv
Box 2: YYYY-MM-DD
Note: 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.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
Question 196
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HOTSPOT
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.
Explanation:
Box 1: No
Note: 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: Yes
When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.
Box 3: Yes
Streaming 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.
Reference:
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
Question 197
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HOTSPOT
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.
Explanation:
Box 1: CREATE EXTERNAL TABLE
An 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 } ( [ ,...n ] )
WITH (
LOCATION = 'folder_or_filepath',
DATA_SOURCE = external_data_source_name,
FILE_FORMAT = external_file_format_name
Box 2. OPENROWSET
When 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:
AS
SELECT decennialTime, stateName, SUM(population) AS population FROM
OPENROWSET(BULK
'https://azureopendatastorage.blob.core.windows.net/censusdatacontainer/release/us_population_county/year=*/*.parquet', FORMAT='PARQUET') AS [r]
GROUP BY decennialTime, stateName GO
Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables
Question 198
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HOTSPOT
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 and an Azure Data Lake Storage Gen2 account named Account1. You plan to access the files in Account1 by using an external table. You need to create a data source in Pool1 that you can reference when you create the external table. 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.
Explanation:
Box 1: blob
The following example creates an external data source for Azure Data Lake Gen2 CREATE EXTERNAL DATA SOURCE YellowTaxi
WITH ( LOCATION = 'https://azureopendatastorage.blob.core.windows.net/nyctlc/yellow/', TYPE = HADOOP) Box 2: HADOOP
Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables
Question 199
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DRAG DROP
You plan to create a table in an Azure Synapse Analytics dedicated SQL pool. Data in the table will be retained for five years. Once a year, data that is older than five years will be deleted. You need to ensure that the data is distributed evenly across partitions. The solution must minimize the amount of time required to delete old data. How should you complete the Transact-SQL statement? To answer, drag the appropriate values to the correct targets. Each value 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.
Explanation:
Box 1: HASH
Box 2: OrderDateKey
In most cases, table partitions are created on a date column. A way to eliminate rollbacks is to use Metadata Only operations like partition switching for data management. For example, rather than execute a DELETE statement to delete all rows in a table where the order_date was in October of 2001, you could partition your data early. Then you can switch out the partition with data for an empty partition from another table.
Reference: https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool
Question 200
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HOTSPOT
You are designing an enterprise data warehouse in Azure Synapse Analytics that will store website traffic analytics in a star schema. You plan to have a fact table for website visits. The table will be approximately 5 GB. You need to recommend which distribution type and index type to use for the table. The solution must provide the fastest query performance. What should you recommend? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Hash
Consider using a hash-distributed table when:
The table size on disk is more than 2 GB.
The table has frequent insert, update, and delete operations. Box 2: Clustered columnstore
Clustered columnstore tables offer both the highest level of data compression and the best overall query performance.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribute https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-index
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