Note: This question is part of a series of questions that present the same scenario. Each question
in the series contains a unique solution that might meet the stated goals. Some question sets
might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these
questions will not appear in the review screen.
You are designing an HDInsight/Hadoop cluster solution that uses Azure Data Lake Gen1 Storage.
The solution requires POSIX permissions and enables diagnostics logging for auditing.
You need to recommend solutions that optimize storage.
Proposed Solution: Ensure that files stored are smaller than 250MB.
Does the solution meet the goal?

• A.Yes
• B.No

Correct Answer: B

Explanation/Reference:
Explanation:
Ensure that files stored are larger, not smaller than 250MB.
You can have a separate compaction job that combines these files into larger ones.
Note: The file POSIX permissions and auditing in Data Lake Storage Gen1 comes with an overhead that
becomes apparent when working with numerous small files. As a best practice, you must batch your data
into larger files versus writing thousands or millions of small files to Data Lake Storage Gen1. Avoiding
small file sizes can have multiple benefits, such as:
Lowering the authentication checks across multiple files

Reduced open file connections

Faster copying/replication

Fewer files to process when updating Data Lake Storage Gen1 POSIX permissions

References:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-best-practices

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You need to design the SensorData collection.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: Eventual
Traffic data insertion rate must be maximized.
Sensor data must be stored in a Cosmos DB named treydata in a collection named SensorData With Azure Cosmos DB, developers can choose from five well-defined consistency models on the consistency spectrum. From strongest to more relaxed, the models include strong, bounded staleness, session, consistent prefix, and eventual consistency.
Box 2: License plate
This solution reports on all data related to a specific vehicle license plate. The report must use data from the SensorData collection.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels

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You are designing a Spark job that performs batch processing of daily web log traffic.
When you deploy the job in the production environment, it must meet the following requirements:
* Run once a day.
* Display status information on the company intranet as the job runs.
You need to recommend technologies for triggering and monitoring jobs.
Which technologies should you recommend? To answer, drag the appropriate technologies to the correct locations. Each technology 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.

Correct Answer:

Explanation

Box 1: Livy
You can use Livy to run interactive Spark shells or submit batch jobs to be run on Spark.
Box 2: Beeline
Apache Beeline can be used to run Apache Hive queries on HDInsight. You can use Beeline with Apache Spark.
Note: Beeline is a Hive client that is included on the head nodes of your HDInsight cluster. Beeline uses JDBC to connect to HiveServer2, a service hosted on your HDInsight cluster. You can also use Beeline to access Hive on HDInsight remotely over the internet.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-livy-rest-interface
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-use-hive-beeline

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You need to design the storage for the Health Insights data platform.
Which types of tables should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation

Box 1: Hash-distributed tables
The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables.
Hash-distributed tables improve query performance on large fact tables.
Box 2: Round-robin distributed tables
A round-robin distributed table distributes table rows evenly across all distributions. The assignment of rows to distributions is random.
Scenario:
ADatum identifies the following requirements for the Health Insights application:
* The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

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You need to design the Planning Assistance database.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Correct Answer:

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