DP-203 Exam Question 71

You use Azure Data Lake Storage Gen2.
You need to ensure that workloads can use filter predicates and column projections to filter data at the time the data is read from disk.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
  • DP-203 Exam Question 72

    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.

    DP-203 Exam Question 73

    You are designing a sales transactions table in an Azure Synapse Analytics dedicated SQL pool. The table will contains approximately 60 million rows per month and will be partitioned by month. The table will use a clustered column store index and round-robin distribution.
    Approximately how many rows will there be for each combination of distribution and partition?
  • DP-203 Exam Question 74

    You plan to create an Azure Data Lake Storage Gen2 account
    You need to recommend a storage solution that meets the following requirements:
    * Provides the highest degree of data resiliency
    * Ensures that content remains available for writes if a primary data center fails What should you include in the recommendation? To answer, select the appropriate options in the answer area.

    DP-203 Exam Question 75

    You have an Azure Data Factory that contains 10 pipelines.
    You need to label each pipeline with its main purpose of either ingest, transform, or load. The labels must be available for grouping and filtering when using the monitoring experience in Data Factory.
    What should you add to each pipeline?