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Prepare for the Microsoft Data Engineering on Microsoft Azure exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.
QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Microsoft DP-203 exam and achieve success.
The questions for DP-203 were last updated on Dec 21, 2024.
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You manage an enterprise data warehouse in Azure Synapse Analytics.
Users report slow performance when they run commonly used queries. Users do not report performance changes for infrequently used queries.
You need to monitor resource utilization to determine the source of the performance issues.
Which metric should you monitor?
Monitor and troubleshoot slow query performance by determining whether your workload is optimally leveraging the adaptive cache for dedicated SQL pools.
You have two fact tables named Flight and Weather. Queries targeting the tables will be based on the join between the following columns.
You need to recommend a solution that maximum query performance.
What should you include in the recommendation?
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?
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.
You have a data warehouse in Azure Synapse Analytics.
You need to ensure that the data in the data warehouse is encrypted at rest.
What should you enable?
Azure SQL Database currently supports encryption at rest for Microsoft-managed service side and client-side encryption scenarios.
Support for server encryption is currently provided through the SQL feature called Transparent Data Encryption.
Client-side encryption of Azure SQL Database data is supported through the Always Encrypted feature.
https://docs.microsoft.com/en-us/azure/security/fundamentals/encryption-atrest
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains a table named Sales. Sales has row-level security (RLS) applied. RLS uses the following predicate filter.
A user named SalesUser1 is assigned the db_datareader role for Pool1. Which rows in the Sales table are returned when SalesUser1 queries the table?
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