DP-750 Implementing Data Engineering Solutions Using Azure Databricks Exam
The DP-750 Implementing Data Engineering Solutions Using Azure Databricks Exam
is designed for data engineers who build, manage, optimize, and secure modern
analytics solutions using Microsoft Azure and Azure Databricks. The
certification validates practical skills in designing scalable data pipelines,
processing batch and streaming data, implementing Delta Lake architectures,
optimizing Spark workloads, managing data governance, and integrating Azure
services.
Candidates preparing for the DP-750 Implementing Data Engineering Solutions
Using Azure Databricks Exam are expected to understand Azure Databricks
workspaces, Apache Spark, Delta Lake, Unity Catalog, notebooks, workflows, SQL
Warehouses, data orchestration, performance tuning, security, monitoring, and
deployment best practices.
Professionals who earn the DP-750 certification demonstrate the ability to
design reliable enterprise-grade data engineering solutions using Azure
Databricks while following Microsoft best practices for scalability, governance,
and cost optimization.
Topics Covered in DP-750 Implementing Data Engineering Solutions Using Azure
Databricks Exam
Azure Databricks Architecture
Azure Databricks Workspace Management
Apache Spark Fundamentals
Spark DataFrames and Datasets
Spark SQL
Delta Lake Architecture
Delta Tables
Delta Live Tables (DLT)
Medallion Architecture
Bronze, Silver and Gold Layers
Batch Data Processing
Structured Streaming
Auto Loader
Data Ingestion Pipelines
ETL and ELT Workflows
Data Transformation
Data Cleansing
Data Quality Validation
Data Partitioning
File Formats (Parquet, CSV, JSON, Delta)
Unity Catalog
Data Governance
Data Lineage
Access Control
Role-Based Access Control (RBAC)
Secrets Management
Azure Key Vault Integration
Databricks Workflows
Job Scheduling
Notebook Development
Python for Data Engineering
SQL Development
PySpark
Performance Optimization
Query Optimization
Spark Configuration
Cluster Management
Autoscaling Clusters
Serverless Compute
SQL Warehouses
Machine Learning Integration
MLflow Basics
Azure Data Lake Storage Gen2
Azure Event Hubs
Azure Synapse Analytics Integration
Azure Data Factory Integration
Monitoring and Logging
Cost Optimization
CI/CD Deployment
Git Integration
Security Best Practices
Troubleshooting Data Pipelines
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Topic 1, Contoso Case Study
Overview
Contoso has a single Azure Databricks workspace named Workspace1 in the West US
Azure region.
Workspace1 is enabled for Unity Catalog.
Workspace1 contains all-purpose clusters for both development and production
workloads.
The company's Azure environment contains:
• In the West US, Central US, and East US Azure regions, Azure event hubs that
stream telemetry
data and an Azure Data Lake Storage Gen2 account in each region for each hub
• A single Azure SQL database in the West US region that hosts enterprise
resource planning (ERP) data
• An Azure Database for PostgreSQL server in the West US region that stores
operational maintenance data
Company information
Contoso, Inc. is a renewable energy provider that operates solar and wind farms
across North America.
Data Environment
Contoso ingests the following operational and business data:
• Telemetry data: More than 40,000 loT sensors across 28 sites emit JSON
telemetry events every
few seconds. Each site sends the events to the nearest event hub, which writes
the data into the
corresponding Data Lake Storage Gen2 account. These files frequently experience
schema drift.
• Maintenance logs: Maintenance systems generate historical repair logs, daily
incremental
updates, technician notes, and unstructured attachments that are stored in the
Data Lake Storage Gen2 accounts.
• Operational maintenance data: Structured operational maintenance data is
stored on the Azure Database for PostgreSQL server.
• External weather data: Hourly weather forecasts are retrieved from a REST API
and written to the Data Lake Storage Gen2 accounts.
• ERP data: Daily CSV extracts of 50 to 100 GB contain equipment metadata, work
orders, and purchase order information.
Problem Statements
The company's existing analytics environment has several issues:
Ingestion
• Telemetry pipelines fall behind during peak loads.
• Telemetry ingestion fails when schema drift occurs.
• Streaming pipelines reprocess events after a pipeline restarts.
Compute
• Production and development workloads run on the same all-purpose clusters.
• Production and development workloads do NOT support autoscaling or workload
isolation.
Governance
• The ERP data is duplicated across systems and development teams.
• Naming conventions are inconsistent across development teams, regions, and
products.
• Ownership of the loT sensors changes over time, and analysts must track the
full history of the ownership.
• Occasionally, equipment manufacturers must correct data-entry mistakes in
equipment names.
Historical values are NOT required.
Pipeline operations
• Pipelines lack resiliency, alerting, and centralized scheduling.
Planned Changes
Contoso plans to implement the following changes:
• Implement scalable data pipeline orchestration.
• Create a managed analytics catalog in Unity Catalog.
• Implement a consistent approach to creating curated datasets.
• Establish a centralized governance model across ingestion, cleansed, and
curated layers.
• Grant data engineers access to the ERP tables by using minimal development
effort.
• Adopt a compute strategy that isolates production workloads and supports
autoscaling.
• Adopt a slowly changing dimension (SCD) approach to address current data
modeling issues.
Technical Requirements
Contoso identifies the following environment and compute requirements:
• Ensure that production ingestion workloads run on compute clusters that can
scale automatically
during telemetry spikes.
• Provide fast and consistent performance for business intelligence (Bl)
workloads.
• Prevent development activity from affecting production pipelines.
• Production ingestion workloads must run as scheduled, non-interactive
pipelines rather than on
shared interactive development clusters.
Contoso identifies the following data ingestion and processing requirements:
• Auto-scale ingestion pipelines to handle bursty workloads.
• Handle schema drift for the maintenance and telemetry data.
• Ingest file-based telemetry data by using minimal operational effort.
• Store all the ingested data in a format that supports incremental processing.
• Support the continuous ingestion of telemetry data from the event hubs by
using exactly-once semantics.
• Support the ingestion of the structured maintenance data from the Azure
Database for PostgreSQL server.
• Build a new telemetry pipeline that ingests raw events from the event hubs,
cleanses the data,
and publishes curated tables to Unity Catalog.
• Ensure that the Apache Spark Structured Streaming pipelines reading from the
event hubs write
the data into a managed Delta table named telemetry.raw_events. The pipelines
must support
schema drift and resume processing after failures without reprocessing the data.
Contoso identifies the following data modeling and optimization requirements:
• Build curated tables that standardize business logic.
• Overwrite equipment metadata attributes, such as name, manufacturer, model,
and
commissioning date, when the attributes change. Historical values are NOT
required.
Contoso identifies the following pipeline deployment and operation requirements:
|^ • Orchestrate
multi-step ingestion and transformation workflows.
• Define a clear execution order and dependencies.
• Automatically retry failed steps and notify operators.
• Schedule ingestion and transformation workloads consistently.
Governance Requirements
Contoso identifies the following governance requirements:
• Centralize the metadata catalog.
• Provide isolated development areas that follow standard naming conventions.
• Establish a consistent structure for organizing raw, cleansed, and curated
data.
• Provide a read-only mechanism to reference the ERP data through a foreign
catalog.
Business Requirements
Contoso identifies the following business requirements:
• Improve ingestion reliability and reduce operational effort.
• Standardize data definitions across development teams.
Question: 1
You need to develop the task logic for a new job in Lakeflow Jobs that processes
telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline.
The solution must
support the planned changes and meet the data ingestion and processing
requirements.
What should you do?
A. Use a single Databricks notebook task that performs ingestion, cleansing, and
curation in one script.
B. Create three tasks that each contains the identical logic and use task
retries.
C. Use a single SQL task that performs ingestion, cleansing, and curation by
running merge commands.
D. Create separate tasks for ingestion, cleansing, and curation.
Answer: D
Explanation:
CORRECT ANSWE R: D - Create separate tasks for ingestion, cleansing, and
curation.
According to Microsoft Learn, Lakeflow Jobs (formerly Databricks Workflows)
supports multi-task
pipelines where each task encapsulates a single, well-defined step. The official
documentation states
that best practice is to decompose complex pipelines into discrete tasks —
ingestion, cleansing, and
curation — so that each task contains only the logic appropriate for that stage.
This approach aligns
with the Contoso planned change to 'implement scalable data pipeline
orchestration' and the
requirement to 'define a clear execution order and dependencies.' Option A is
incorrect because
combining all logic in one notebook violates the single-responsibility principle
and makes
retry/recovery difficult. Option B is incorrect because duplicating identical
logic across tasks wastes
resources and defeats the purpose of a modular pipeline. Option C is incorrect
because a single SQL
MERGE task cannot cleanly separate the ingestion, cleansing, and curation
concerns required.
Question: 2
You need to configure compute for the ingestion of telemetry data. The solution
must meet the data ingestion and processing requirements.
What should you do?
A. Enable Photon acceleration for a job compute cluster.
B. Move the ingestion pipelines to shared compute.
C. Increase an all-purpose cluster to a larger fixed node type.
D. Disable autoscaling for a job compute cluster.
Answer: A
Explanation:
CORRECT ANSWE R: A - Enable Photon acceleration for a job compute cluster.
According to Microsoft Learn and the Azure Databricks documentation, Photon is a
high-performance
vectorized query engine written in C++ that accelerates Apache Spark workloads,
especially ingestion
and SQL operations. The Contoso technical requirement states: 'Ensure that
production ingestion
workloads run on compute clusters that can scale automatically during telemetry
spikes' and
'Provide fast and consistent performance for BI workloads.' Photon on a job
compute cluster directly
addresses both speed and consistency for ingestion pipelines. Option B is
incorrect because moving
ingestion to shared compute would violate the requirement to isolate production
from development.
Option C is incorrect because increasing a fixed-node all-purpose cluster does
not provide
autoscaling. Option D is incorrect because disabling autoscaling would prevent
the cluster from
handling bursty telemetry workloads, directly contradicting the stated
requirements.
Question: 3
DRAG DROP
Which SCD type should you use to support the planned data modeling changes? To
answer, drag the
appropriate types to the correct issues. Each type may be used once, more than
once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
CORRECT ANSWE R: SCD Type 1 for equipment metadata overwrite; SCD Type 2
for ownership historytracking.
According to Microsoft Learn on Delta Lake and slowly changing dimensions (SCD),
SCD Type 1
overwrites the existing record with new data and does not preserve historical
values — the Contoso
requirement states 'Overwrite equipment metadata attributes when they change;
Historical values
are NOT required,' which maps directly to SCD Type 1. SCD Type 2 preserves a
full history by creating
a new row for each change, including effective-date columns — Contoso requires
that 'analysts must
track the full history of ownership' for IoT sensors, matching SCD Type 2. Delta
Lake's MERGE INTO
statement natively supports both SCD Type 1 (UPDATE when matched) and SCD Type 2
(INSERT new
version + UPDATE old version) patterns. SCD Type 3 is not mentioned as it only
keeps current and one
previous value, which is insufficient for full ownership history.
Question: 4
DRAG DROP
Which ingestion option should you recommend for each data source? To answer,
drag the
appropriate options to the correct data sources. Each option may be used once,
more than once, or
not at all. You may need to drag the split bar between panes or scroll to view
content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
CORRECT ANSWE R: Auto Loader for file-based telemetry/maintenance data; Spark
Structured
Streaming with Event Hubs connector for real-time telemetry; JDBC connector for
Azure Database for
PostgreSQL; COPY INTO or Auto Loader for daily CSV ERP extracts.
According to Microsoft Learn, Auto Loader ('cloudFiles') is the recommended
approach for
incrementally ingesting files from cloud storage with schema inference and
evolution support —
directly meeting the requirement to 'ingest file-based telemetry data using
minimal operational
effort' and 'handle schema drift.' For Event Hubs, Apache Spark Structured
Streaming with the azureeventhubs-
spark connector provides exactly-once semantics and checkpoint-based recovery.
For
PostgreSQL, the Databricks JDBC connector enables structured ingestion from
relational databases.
For bulk CSV ERP files (50-100 GB), COPY INTO provides idempotent, incremental
batch loading into
Delta tables.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/
Questions and Answers PDF 9/70
Question: 5
HOTSPOT
You need to complete the PySpark code for the Spark Structured Streaming
pipelines. The solution must meet the data ingestion and processing
requirements.
How should you complete the code segment? To answer, select the appropriate
options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
CORRECT ANSWE R: Use spark.readStream with format('cloudFiles') for Auto Loader
ingestion; use
.writeStream with mergeSchema=true and a checkpointLocation to write to the
managed Delta table telemetry.raw_events.
According to Microsoft Learn, the Auto Loader (cloudFiles) source in Apache
Spark Structured
Streaming supports incremental file processing with schema inference and
evolution
(mergeSchema). The Contoso requirement states the pipeline must 'support schema
drift and
resume processing after failures without reprocessing the data.' The
checkpointLocation option is
mandatory for fault-tolerant streaming — it stores the offset and schema state
so the pipeline can
resume exactly where it stopped without reprocessing. The mergeSchema option
enables schema
Questions and Answers PDF 11/70
evolution so that new columns in incoming JSON files are automatically added to
the target Delta
table. Without checkpointLocation, the stream would replay from the beginning on
restart, violating
the exactly-once and no-reprocessing requirements.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/schema
Topic 2, Misc. Questions
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One of the most useful preparation resources I used before taking the DP-750 exam.
Most Asked FAQs (Google, Reddit & AI Search Trends)
1. What is the DP-750 Implementing Data Engineering Solutions Using Azure
Databricks Exam?
It is a Microsoft certification exam that validates skills in building and
managing data engineering solutions using Azure Databricks.
2. Who should take the DP-750 exam?
Data engineers, Azure professionals, analytics engineers, and developers working
with Azure Databricks.
3. What topics are covered in the DP-750 exam?
Azure Databricks, Apache Spark, Delta Lake, Unity Catalog, streaming, ETL
pipelines, security, performance optimization, and Azure integrations.
4. Is the DP-750 exam difficult?
The exam can be challenging if you lack hands-on experience, but consistent
study and practical exercises can significantly improve your readiness.
5. Are hands-on Azure Databricks skills necessary?
Yes. Practical experience with notebooks, Spark, Delta Lake, and data pipelines
is highly beneficial.
6. How long should I study for DP-750?
Most candidates prepare over several weeks, depending on their existing Azure
and Databricks experience.
7. What programming languages should I know?
Python (PySpark) and SQL are the most commonly used languages for exam
preparation.
8. Is Apache Spark included in the exam?
Yes. Spark fundamentals, DataFrames, Spark SQL, and optimization are important
exam objectives.
9. What is Delta Lake, and why is it important?
Delta Lake provides reliable storage with ACID transactions, schema enforcement,
and improved data quality, making it a core topic for the exam.
10. Does the exam include streaming data?
Yes. Structured Streaming and real-time data processing are commonly tested.
11. What Azure services integrate with Azure Databricks?
Candidates should understand integrations with Azure Data Lake Storage Gen2,
Azure Data Factory, Azure Event Hubs, Azure Synapse Analytics, and Azure Key
Vault.
12. What are the career benefits of passing DP-750?
The certification demonstrates expertise in Azure-based data engineering and can
strengthen qualifications for cloud and analytics roles.
13. What study resources are recommended?
The official Microsoft learning path, Azure Databricks documentation, hands-on
labs, practice questions, and mock exams are all valuable resources.
14. What mistakes do candidates commonly make?
Common issues include focusing only on theory, skipping practical Spark
exercises, overlooking governance and security topics, and not reviewing
performance optimization.
15. How can I improve my chances of passing?
Study the official skills outline, gain hands-on experience in Azure Databricks,
practice with realistic scenario-based questions, review explanations carefully,
and reinforce weak areas with additional labs.
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