Practice Exams | MS Azure DP-700 Data Engineering Solutions

Description

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.

The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.

Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.

The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.

NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.

Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.

As a candidate for this exam, you should have subject matter expertise with data loading patterns, data architectures, and orchestration processes. Your responsibilities for this role include:

• Ingesting and transforming data.

• Securing and managing an analytics solution.

• Monitoring and optimizing an analytics solution.

You work closely with analytics engineers, architects, analysts, and administrators to design and deploy data engineering solutions for analytics.

You should be skilled at manipulating and transforming data by using Structured Query Language (SQL), PySpark, and Kusto Query Language (KQL).

Skills at a glance

• Implement and manage an analytics solution (30–35%)

• Ingest and transform data (30–35%)

• Monitor and optimize an analytics solution (30–35%)

Implement and manage an analytics solution (30–35%)

Configure Microsoft Fabric workspace settings

• Configure Spark workspace settings

• Configure domain workspace settings

• Configure OneLake workspace settings

• Configure data workflow workspace settings

Implement lifecycle management in Fabric

• Configure version control

• Implement database projects

• Create and configure deployment pipelines

Configure security and governance

• Implement workspace-level access controls

• Implement item-level access controls

• Implement row-level, column-level, object-level, and folder/file-level access controls

• Implement dynamic data masking

• Apply sensitivity labels to items

• Endorse items

• Implement and use workspace logging

Orchestrate processes

• Choose between a pipeline and a notebook

• Design and implement schedules and event-based triggers

• Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions

Ingest and transform data (30–35%)

Design and implement loading patterns

• Design and implement full and incremental data loads

• Prepare data for loading into a dimensional model

• Design and implement a loading pattern for streaming data

Ingest and transform batch data

• Choose an appropriate data store

• Choose between dataflows, notebooks, KQL, and T-SQL for data transformation

• Create and manage shortcuts to data

• Implement mirroring

• Ingest data by using pipelines

• Transform data by using PySpark, SQL, and KQL

• Denormalize data

• Group and aggregate data

• Handle duplicate, missing, and late-arriving data

Ingest and transform streaming data

• Choose an appropriate streaming engine

• Choose between native storage, followed storage, or shortcuts in Real-Time Intelligence

• Process data by using eventstreams

• Process data by using Spark structured streaming

• Process data by using KQL

• Create windowing functions

Monitor and optimize an analytics solution (30–35%)

Monitor Fabric items

• Monitor data ingestion

• Monitor data transformation

• Monitor semantic model refresh

• Configure alerts

Identify and resolve errors

• Identify and resolve pipeline errors

• Identify and resolve dataflow errors

• Identify and resolve notebook errors

• Identify and resolve eventhouse errors

• Identify and resolve eventstream errors

• Identify and resolve T-SQL errors

Optimize performance

• Optimize a lakehouse table

• Optimize a pipeline

• Optimize a data warehouse

• Optimize eventstreams and eventhouses

• Optimize Spark performance

• Optimize query performance

Course Info