Here you have the best Microsoft DP-201 practice exam questions
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.
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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 larger than 250MB.
Does the solution meet the goal?
The proposed solution does not fully meet the goal. Ensuring that files are larger than 250MB is a good start, but the best practice recommendation for Azure Data Lake Storage Gen1 is to have file sizes of at least 256MB or larger. This practice reduces the overhead associated with POSIX permissions and auditing, as well as improving performance metrics such as authentication checks, file connections, and copying/replication speeds. Therefore, the proposed solution of targeting file sizes just above 250MB falls short of this best practice threshold.
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: Implement compaction jobs to combine small files into larger files.
Does the solution meet the goal?
Implementing compaction jobs to combine small files into larger files is an effective solution for optimizing storage in HDInsight/Hadoop clusters using Azure Data Lake Gen1 Storage. Small files can introduce significant overhead due to numerous POSIX permission checks and auditing processes. By compacting these files into larger ones, the solution reduces the number of open file connections, lowers the overhead from authentication checks, speeds up copying and replication processes, and simplifies the management of permissions. Therefore, the proposed solution meets the goal by optimizing the storage effectively.
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?
POSIX permissions and diagnostics logging in Data Lake Storage Gen1 involve an overhead that becomes more significant with numerous small files. Small files lead to higher authentication checks, more open file connections, and slower copying/replication. To optimize storage and meet the goal, it is recommended to store larger files, ideally at least 256MB, rather than smaller ones.
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 Azure SQL Database that will use elastic pools. You plan to store data about customers in a table. Each record uses a value for
CustomerID.
You need to recommend a strategy to partition data based on values in CustomerID.
Proposed Solution: Separate data into customer regions by using vertical partitioning.
Does the solution meet the goal?
The proposed solution suggests using vertical partitioning to separate data into customer regions. Vertical partitioning involves splitting a table vertically into multiple tables with fewer columns, and it is typically used to distribute tables with different schemas across databases. However, the requirement is to partition data based on CustomerID values, which indicates a need to distribute rows across multiple partitions. This is best achieved using horizontal partitioning, or sharding, which evenly distributes rows of a table across multiple databases with identical schemas based on a partition key like CustomerID. Therefore, vertical partitioning does not meet the goal, and the correct answer is 'No'.
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 Azure SQL Database that will use elastic pools. You plan to store data about customers in a table. Each record uses a value for
CustomerID.
You need to recommend a strategy to partition data based on values in CustomerID.
Proposed Solution: Separate data into customer regions by using horizontal partitioning.
Does the solution meet the goal?
The proposed solution suggests separating data into customer regions using horizontal partitioning. However, the goal is to partition data based on values in CustomerID, not on regions. Horizontal partitioning, also known as sharding, should be used to partition the data based on the customer ID values to ensure efficient distribution and management of data across databases. Therefore, the proposed solution does not meet the goal as stated.