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 in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions.

As an Azure data engineer, you help stakeholders understand the data through exploration, and build and maintain secure and compliant data processing pipelines by using different tools and techniques. You use various Azure data services and frameworks to store and produce cleansed and enhanced datasets for analysis. This data store can be designed with different architecture patterns based on business requirements, including:

Management data warehouse (MDW)

Big data

Lakehouse architecture

As an Azure data engineer, you also help to ensure that the operationalization of data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. You help to identify and troubleshoot operational and data quality issues. You also design, implement, monitor, and optimize data platforms to meet the data pipelines.

As a candidate for this exam, you must have solid knowledge of data processing languages, including:

SQL

Python

Scala

You need to understand parallel processing and data architecture patterns. You should be proficient in using the following to create data processing solutions:

Azure Data Factory

Azure Synapse Analytics

Azure Stream Analytics

Azure Event Hubs

Azure Data Lake Storage

Azure Databricks

Skills at a glance

Design and implement data storage (15–20%)

Develop data processing (40–45%)

Secure, monitor, and optimize data storage and data processing (30–35%)

Design and implement data storage (15–20%)

Implement a partition strategy

Implement a partition strategy for files

Implement a partition strategy for analytical workloads

Implement a partition strategy for streaming workloads

Implement a partition strategy for Azure Synapse Analytics

Identify when partitioning is needed in Azure Data Lake Storage Gen2

Design and implement the data exploration layer

Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster

Recommend and implement Azure Synapse Analytics database templates

Push new or updated data lineage to Microsoft Purview

Browse and search metadata in Microsoft Purview Data Catalog

Develop data processing (40–45%)

Ingest and transform data

Design and implement incremental loads

Transform data by using Apache Spark

Transform data by using Transact-SQL (T-SQL) in Azure Synapse Analytics

Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory

Transform data by using Azure Stream Analytics

Cleanse data

Handle duplicate data

Avoiding duplicate data by using Azure Stream Analytics Exactly Once Delivery

Handle missing data

Handle late-arriving data

Split data

Shred JSON

Encode and decode data

Configure error handling for a transformation

Normalize and denormalize data

Perform data exploratory analysis

Develop a batch processing solution

Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory

Use PolyBase to load data to a SQL pool

Implement Azure Synapse Link and query the replicated data

Create data pipelines

Scale resources

Configure the batch size

Create tests for data pipelines

Integrate Jupyter or Python notebooks into a data pipeline

Upsert data

Revert data to a previous state

Configure exception handling

Configure batch retention

Read from and write to a delta lake

Develop a stream processing solution

Create a stream processing solution by using Stream Analytics and Azure Event Hubs

Process data by using Spark structured streaming

Create windowed aggregates

Handle schema drift

Process time series data

Process data across partitions

Process within one partition

Configure checkpoints and watermarking during processing

Scale resources

Create tests for data pipelines

Optimize pipelines for analytical or transactional purposes

Handle interruptions

Configure exception handling

Upsert data

Replay archived stream data

Manage batches and pipelines

Trigger batches

Handle failed batch loads

Validate batch loads

Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines

Schedule data pipelines in Data Factory or Azure Synapse Pipelines

Implement version control for pipeline artifacts

Manage Spark jobs in a pipeline

Secure, monitor, and optimize data storage and data processing (30–35%)

Implement data security

Implement data masking

Encrypt data at rest and in motion

Implement row-level and column-level security

Implement Azure role-based access control (RBAC)

Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2

Implement a data retention policy

Implement secure endpoints (private and public)

Implement resource tokens in Azure Databricks

Load a DataFrame with sensitive information

Write encrypted data to tables or Parquet files

Manage sensitive information

Monitor data storage and data processing

Implement logging used by Azure Monitor

Configure monitoring services

Monitor stream processing

Measure performance of data movement

Monitor and update statistics about data across a system

Monitor data pipeline performance

Measure query performance

Schedule and monitor pipeline tests

Interpret Azure Monitor metrics and logs

Implement a pipeline alert strategy

Optimize and troubleshoot data storage and data processing

Compact small files

Handle skew in data

Handle data spill

Optimize resource management

Tune queries by using indexers

Tune queries by using cache

Troubleshoot a failed Spark job

Troubleshoot a failed pipeline run, including activities executed in external services