Valid DP-203 Test Review & DP-203 Mock Exam

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Microsoft DP-203 certification exams are a great way to analyze and evaluate the skills of a candidate effectively. Big companies are always on the lookout for capable candidates. You need to pass the Microsoft DP-203 Certification Exam to become a certified professional. This task is considerably tough for unprepared candidates however with the right DP-203 prep material there remains no chance of failure.

To take the DP-203 exam, you should have a solid understanding of data processing technologies, data storage options, data security and compliance, and data integration and transformation techniques. You should also be familiar with Azure services such as Azure Data Factory, Azure Databricks, Azure Synapse Analytics, and Azure Stream Analytics. By passing DP-203 Exam, you can prove your ability to work with Azure tools and technologies, and showcase your expertise in data engineering on the Microsoft Azure platform.

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Microsoft DP-203 Mock Exam - DP-203 Latest Exam Questions

The software keeps track of the previous Data Engineering on Microsoft Azure (DP-203) practice exam attempts and shows the changes of each attempt. You don't need to wait days or weeks to get your performance report. The software displays the result of the Microsoft DP-203 Practice Test immediately, which is an excellent way to understand which area needs more attention.

To pass the DP-203 exam, candidates must demonstrate their ability to design and implement data solutions on Azure by answering a series of multiple-choice questions and scenario-based questions. DP-203 Exam is timed and lasts 150 minutes, and candidates must score at least 700 out of 1000 to pass.

Microsoft Data Engineering on Microsoft Azure Sample Questions (Q265-Q270):

NEW QUESTION # 265
You have an Azure Data Lake Storage Gen2 container.
Data is ingested into the container, and then transformed by a data integration application. The data is NOT modified after that. Users can read files in the container but cannot modify the files.
You need to design a data archiving solution that meets the following requirements:
* New data is accessed frequently and must be available as quickly as possible.
* Data that is older than five years is accessed infrequently but must be available within one second when requested.
* Data that is older than seven years is NOT accessed. After seven years, the data must be persisted at the lowest cost possible.
* Costs must be minimized while maintaining the required availability.
How should you manage the data? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

Explanation:

Box 1: Move to cool storage
Box 2: Move to archive storage
Archive - Optimized for storing data that is rarely accessed and stored for at least 180 days with flexible latency requirements, on the order of hours.
The following table shows a comparison of premium performance block blob storage, and the hot, cool, and archive access tiers.

Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-storage-tiers Explanation:
Box 1: Replicated
Replicated tables are ideal for small star-schema dimension tables, because the fact table is often distributed on a column that is not compatible with the connected dimension tables. If this case applies to your schema, consider changing small dimension tables currently implemented as round-robin to replicated.
Box 2: Replicated
Box 3: Replicated
Box 4: Hash-distributed
For Fact tables use hash-distribution with clustered columnstore index. Performance improves when two hash tables are joined on the same distribution column.
Reference:
https://azure.microsoft.com/en-us/updates/reduce-data-movement-and-make-your-queries-more-efficient-with-th
https://azure.microsoft.com/en-us/blog/replicated-tables-now-generally-available-in-azure-sql-data-warehouse/

NEW QUESTION # 266
The following code segment is used to create an Azure Databricks cluster.

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://adatis.co.uk/databricks-cluster-sizing/
https://docs.microsoft.com/en-us/azure/databricks/jobs
https://docs.databricks.com/administration-guide/capacity-planning/cmbp.html
https://docs.databricks.com/delta/index.html

NEW QUESTION # 267
From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays.
The data contains the following columns.

You need to design a star schema to support analytical queries of the data. The star schema will contain four tables including a date dimension.
To which table should you add each column? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/power-bi/guidance/star-schema

NEW QUESTION # 268
You are creating dimensions for a data warehouse in an Azure Synapse Analytics dedicated SQL pool.
You create a table by using the Transact-SQL statement 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:

Reference:
https://docs.microsoft.com/en-us/learn/modules/populate-slowly-changing-dimensions-azure-synapse-analytics-pipelines/3-choose-between-dimension-types

NEW QUESTION # 269
You are designing an Azure Databricks cluster that runs user-defined local processes. You need to recommend a cluster configuration that meets the following requirements:
* Minimize query latency.
* Maximize the number of users that can run queues on the cluster at the same time to Reduce overall costs without compromising other requirements Which cluster type should you recommend?

A. Standard with Autoscaling
B. High Concurrency with Autoscaling
C. High Concurrency with Auto Termination
D. Standard with Auto termination

Answer: B

Explanation:
A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies.
Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don't need to provision the cluster to match a workload.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/clusters/configure

NEW QUESTION # 270
......

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