DP-700: Fabric Data Engineer Associate Practice Test

PRACTICE - PRACTICE - PRACTICE: PRACTICE WILL MAKE YOU PERFECT & become Microsoft Certified: Fabric Data Engineer Associate

Course provides several practice sets similar to actual exam questions as per official exam syllabus and study guide for Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric.

Practice set contains questions from all 3 below domains and once you attended a practice set, you can review where you will get the actual answers along with EXPLANATION and official/course resource link.

This EXAM DP-700 contains 3 sections/domains having equal priority:

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

2. Ingest and transform data (30–35%)

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

Recommendation is to attend following EXAM DP-700 preparation course for better and effective preparation "Exam DP-700: Fabric Data Engineer Associate - Ultimate Guide" as it provides syllabus wise concept class followed by related hands-on Lab/demo session immediately to visualize how to use or implement it for effective learning.

IMPORTANT NOTE: As per Official update from Microsoft, "Microsoft Certified: Azure Data Engineer Associate Certification and its related Exam DP-203: Data Engineering on Microsoft Azure will all be retired on March 31, 2025."

So Exam DP-700 & Certification is VERY IMPORTANT as it is future of  Data Engineering certification from Microsoft: DP-700: Microsoft Fabric Data Engineer Associate

What you will learn from this course?

As per official syllabus and study guide, Practice sets provides questions including below questions ( with actual answers along with EXPLANATION and official/course resource link);

You can get concept/theory/fundamental followed by DEMO session for each the below questions from preparation course "Exam DP-700: Fabric Data Engineer Associate - Ultimate Guide" available from this instructor .

Fabric components/domain/concept/language part of this practice set:

1. Lakehouse 

2. Warehouse

3. Real time Hub / Real-Time Intelligence

4. dataflows

5. Notebook

6. Spark, PySpark, SQL, KQL (Kusto Query Language)

7. Data Factory Pipeline

8. Eventhouse

9. Eventstream

For Configure Spark workspace settings: 

1. What is Starter Pool ?

2. How to modify Starter Pool in Fabric and how its related Fabric settings impacts Starter Pool?

3. What is custom Spark Pool?

4. How to create custom Spark pool in Fabric and how its related settings impacts custom Spark pool

5. What is Environment and what are their features and related settings that affects these.

6. How to create Environment and how its related settings impacts compute configuration of Environment.

7. What is the impact when these pool/environment are made as default pool in workspace.

   
   

For Configure security and governance in Fabric,

1. What is workspace-level access controls? Overview & Concept

2. How to implement workspace-level access controls in Fabric through Hands on Lab/Demo.

3. What is item-level  access controls? Overview & Concept

4. How to implement item-level access controls in Fabric through Hands on Lab/Demo.

5. What is file-level  access controls? Overview & Concept

6. How to implement file-level access controls in Fabric through Hands on Lab/Demo.

7. What is object-level  access controls? Overview & Concept

8. How to implement object-level access controls in Fabric through Hands on Lab/Demo.

9. What is row-level  access controls? Overview & Concept - Row Level Security (RLS)

10. How to implement row-level access controls in Fabric through Hands on Lab/Demo.

11. What is column-level  access controls? Overview & Concept - Column Level Security (CLS)

12. How to implement column-level access controls in Fabric through Hands on Lab/Demo.

13. What is dynamic data masking in Fabric? Overview & Concept

14. How to implement dynamic data masking in Fabric through Hands on Lab/Demo.

For Transform data by using KQL (Ingest and transform batch data - Part 5)

1. KQL Fundamentals: Query Operator & | Pipe

2. KQL Fundamentals & Hands on Lab: Query Operator - Project , count,  getschema

3. How to translate SQL query to KQL Query

4. How to find relevant data using  distinct, take operator, Let statement in KQL

5. How to find relevant data using Filter/Where in KQL

6. How to find relevant data using  Case (like if/then/elseif ) in KQL

7. How to use KQL Search

8. How to implement sorting records using  Sort operator

9. How to returns  first N rows using  top operator in KQL

10. How to Create Columns using  Extend operator in KQL

11. How to Keep/Remove/Reorder Columns using KQL Project operators  - project, project-away , project-keep,project-reorder, project-rename

12. KQL join & best performance

13. How to implement left right outer, Left semi join,  Left anti join, Right semi join, Right anti join,full outer join in KQL

14. How to use summarize operator to perform Aggregation in KQL

15. How to perform Aggregation using KQL Aggregation  functions Count() ,Countif(),  sum() , sumif(), avg(), avgif() ,max(), maxif() ,min(), minif()

16. How to perform KQL Aggregation (Group and aggregate data) - summarize by (Group and aggregate data:) - single aggregation, multiple aggregation (GROUP BY)

For Transform data by using PySpark (Ingest and transform batch data - Part 3)

1. How to use or  implement select take using PySpark in Fabric

2. How to implement  Filter/Where transformation PySpark  to clean data

3. How to implement  Drop,  distinct, printschema using PySpark 

4. How to implement  Sort()/OrderBy() to sort records using PySpark 

5. How to implement  WithColumn,  ColumnRenamed transformation using PySpark 

6. How to implement  joins using PySpark 

7. How to implement  Aggregations using PySpark 

8. How to implement  Group and aggregate data using PySpark 

For Process data by using eventstreams (Ingest and transform streaming data)

1. How to perform Manage fields transformation in eventstreams

2. How to perform filter transformation in eventstreams

3. How to perform aggregation transformation in eventstreams

4. How to perform group by transformation using tumbling window in eventstreams 

5. How to perform group by transformation using hopping window in eventstreams 

6. How to perform group by transformation using sliding window in eventstreams 

7. How to perform Expand transformation in eventstreams

8. How to perform union transformation in eventstreams

9. How to perform join transformation in eventstreams

   
   

For Configure version control, you will learn

1. What is Version control? Concept & Integration Process

2. What are related Fabric/Git permission and settings (and Tenant settings  ) required to configure version control in Fabric?

3. Hands-on Lab/Demo:  How to set up Azure Repo that to be used as part of version control configuration.

4. Hands-on Lab/Demo: How to configure version control/ Git integration with Azure Repo from Fabric workspace

For Implement database projects,

1. What is Database projects - concept & overview

2. Database projects - Setup & Architecture for demo

3. Why we need SQL database projects?

4. Hands-on Lab/Demo: How to implement database projects in Fabric

For Create and configure deployment pipelines

1. What is deployment pipeline in Fabric? overview

2. Architecture of deployment pipeline  for Demo  & prerequisites

3. Hands on Lab/Demo :How to Create and configure deployment pipelines

4. Hands on Lab/Demo :How to assign workspace to respective stages and deploy content from one stage to next stage.

For Configure domain workspace settings

1. What is  domain in Fabric ?

2. What are the Delegated Setting for domain

3. Hands on Lab/Demo: how these delegated settings impacts domain in Fabric

For SQL database projects:

1. We will understand why we need SQL database projects

2. How to Setup Demo components & understand through Architecture diagram

3. Hands on Lab/Demo - How to Implement database projects in Fabric

For Configure security and governance in Fabric,

1. What is sensitivity labels? Overview & Concept

2. Sensitivity labels: related admin settings in Fabric

3. How to apply sensitivity labels to items in Fabric?

For Orchestrate processes,

1. How to Choose between a pipeline and a notebook in Fabric?

2. Design and implement schedules triggers - Design components for demo

3. How to implement schedules triggers in Fabric Data Factory pipeline.

For Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expression

1. Data Factory pipeline good practice

2. What is Pipeline parameter and dynamic expression concept

3. How to implement parameters and dynamic expression in pipeline

4. How to configure pipeline to retry if pipeline run fails

5. How to implement orchestration patterns with notebooks and pipelines

For Design and implement loading patterns (Ingest and transform data), you will learn

1. How to design full and incremental data loads in Fabric

2. How to implement full and incremental data loads in Fabric through hands-on lab/demo

For Ingest and transform batch data - part 1, you will learn

1. how to choose an appropriate data store

2. how to choose between dataflows, notebooks, and T-SQL for data transformation

3. Shortcuts overview in fabric

4. Shortcuts type in Fabric

5. Shortcuts folder structure

6. How to create and manage shortcuts to data  in Fabric through hands-on lab/demo

For Ingest data by using pipelines (Ingest and transform batch data - Part 2), you will learn

1. How to design Ingest data by using pipelines into Lakehouse

2. How to ingest data by using pipelines into Lakehouse

3. How to design Ingest data by using pipelines into warehouse

4. How to ingest data by using pipelines into warehouse

5. How to design Ingest data by using pipelines into KQL Database

6. How to ingest data by using pipelines into KQL Database

   
   

For Transform data by using SQL (Ingest and transform batch data - Part 4),

1. How to implement SQL top distinct keyword

2. How to implement SQL Filter on data

3. How to  implement SQL Sort on data

4. How to implement Case & create dynamic or computed column

5. How to implement SQL Inner Join, left Join, right Join, outer Join

6. How to implement Aggregation in SQL

7. How to implement  SQL Group and aggregate data: Group by & Having Clause Aggregation

8. How to create Create Stored Procedure

9. How to transform the data using Stored Procedure activity in Data pipeline

   
   

For Optimize a lakehouse table (Optimize performance - Part 1)

1. How to optimize a lakehouse table using Optimize command in Fabric

2. How to optimize a lakehouse table using V-Order in Fabric

3. How to optimize a lakehouse table using VACUUM command in Fabric

4. How to optimize a lakehouse table using Optimizetwrite command in Fabric

5. How to optimize a lakehouse table using Partition in Fabric

6. How to optimize a lakehouse table using Table maintenance feature in Fabric

Get the answer of the questions like: You have a Fabric workspace that contains a semantic model named Model1. You need to dynamically execute and monitor the refresh progress of Model1. What should you use?

Study Guide for Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric:

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