2025 ACCURATE DP-203 RELIABLE EXAM VCE | 100% FREE LATEST DP-203 TEST FEE

2025 Accurate DP-203 Reliable Exam Vce | 100% Free Latest DP-203 Test Fee

2025 Accurate DP-203 Reliable Exam Vce | 100% Free Latest DP-203 Test Fee

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Microsoft DP-203 (Data Engineering on Microsoft Azure) exam is a certification that validates an individual's knowledge and skills in designing and implementing data solutions using Microsoft Azure. Data Engineering on Microsoft Azure certification is designed for data engineers who are responsible for designing and implementing data solutions using various Azure services, including Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure HDInsight. DP-203 Exam measures an individual's ability to design, implement, monitor and optimize data solutions using these services and ensures that the individual has the necessary skills to meet the growing demand for data engineers in the industry.

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Microsoft DP-203: Data Engineering on Microsoft Azure Exam is a valuable certification for professionals who want to specialize in data engineering on Azure. DP-203 Exam Tests the candidate's expertise in designing, implementing, and maintaining data processing solutions on Azure. It is an opportunity to enhance one's career prospects and showcase one's skills in the field of data engineering.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q229-Q234):

NEW QUESTION # 229
You have a SQL pool in Azure Synapse.
You plan to load data from Azure Blob storage to a staging table. Approximately 1 million rows of data will be loaded daily. The table will be truncated before each daily load.
You need to create the staging table. The solution must minimize how long it takes to load the data to the staging table.
How should you configure the table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Hash
Hash-distributed tables improve query performance on large fact tables. They can have very large numbers of rows and still achieve high performance.
Box 2: Clustered columnstore
When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed.
Box 3: Date
Table partitions enable you to divide your data into smaller groups of data. In most cases, table partitions are created on a date column.
Partition switching can be used to quickly remove or replace a section of a table.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables- partition
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables- distribute


NEW QUESTION # 230
You are designing an Azure Synapse Analytics dedicated SQL pool.
Groups will have access to sensitive data in the pool as shown in the following table.

You have policies for the sensitive dat
a. The policies vary be region as shown in the following table.

You have a table of patients for each region. The tables contain the following potentially sensitive columns.

You are designing dynamic data masking to maintain compliance.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview


NEW QUESTION # 231
You have an Azure Databricks workspace named workspace1 in the Standard pricing tier.
You need to configure workspace1 to support autoscaling all-purpose clusters. The solution must meet the following requirements:
* Automatically scale down workers when the cluster is underutilized for three minutes.
* Minimize the time it takes to scale to the maximum number of workers.
* Minimize costs.
What should you do first?

  • A. Enable container services for workspace1.
  • B. Create a cluster policy in workspace1.
  • C. Upgrade workspace1 to the Premium pricing tier.
  • D. Set Cluster Mode to High Concurrency.

Answer: C

Explanation:
For clusters running Databricks Runtime 6.4 and above, optimized autoscaling is used by all-purpose clusters in the Premium plan Optimized autoscaling:
Scales up from min to max in 2 steps.
Can scale down even if the cluster is not idle by looking at shuffle file state.
Scales down based on a percentage of current nodes.
On job clusters, scales down if the cluster is underutilized over the last 40 seconds.
On all-purpose clusters, scales down if the cluster is underutilized over the last 150 seconds.
The spark.databricks.aggressiveWindowDownS Spark configuration property specifies in seconds how often a cluster makes down-scaling decisions. Increasing the value causes a cluster to scale down more slowly. The maximum value is 600.
Note: Standard autoscaling
Starts with adding 8 nodes. Thereafter, scales up exponentially, but can take many steps to reach the max.
You can customize the first step by setting the spark.databricks.autoscaling.standardFirstStepUp Spark configuration property.
Scales down only when the cluster is completely idle and it has been underutilized for the last 10 minutes.
Scales down exponentially, starting with 1 node.
Reference:
https://docs.databricks.com/clusters/configure.html


NEW QUESTION # 232
You need to design the partitions 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.

Answer:

Explanation:

Explanation

Box 1: Sales date
Scenario: Contoso requirements for data integration include:
* 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.
Box 2: An Azure Synapse Analytics Dedicated SQL pool
Scenario: Contoso requirements for data integration include:
* Ensure that data storage costs and performance are predictable.
The size of a dedicated SQL pool (formerly SQL DW) is determined by Data Warehousing Units (DWU).
Dedicated SQL pool (formerly SQL DW) stores data in relational tables with columnar storage. This format significantly reduces the data storage costs, and improves query performance.
Synapse analytics dedicated sql pool
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overview-wha


NEW QUESTION # 233
You store files in an Azure Data Lake Storage Gen2 container. The container has the storage policy shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection Is worth one point.

Answer:

Explanation:

Explanation
Graphical user interface, text, application Description automatically generated

Box 1: moved to cool storage
The ManagementPolicyBaseBlob.TierToCool property gets or sets the function to tier blobs to cool storage.
Support blobs currently at Hot tier.
Box 2: container1/contoso.csv
As defined by prefixMatch.
prefixMatch: An array of strings for prefixes to be matched. Each rule can define up to 10 case-senstive prefixes. A prefix string must start with a container name.
Reference:
https://docs.microsoft.com/en-us/dotnet/api/microsoft.azure.management.storage.fluent.models.managementpoli


NEW QUESTION # 234
......

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