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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Configure and manage Azure Databricks environments	15-20%	- Workspace and compute configuration 1. Runtime, Spark, and Photon configuration 2. Cluster types and configuration (job, all-purpose, serverless) 3. Autoscaling, termination, and performance tuning - Security and authentication setup 1. Azure Key Vault integration 2. Access control for compute resources 3. Service principals and managed identities
Topic 2: Prepare and process data	30-35%	- Data ingestion 1. Auto Loader and CDC ingestion patterns 2. Streaming ingestion using Spark Structured Streaming 3. Batch ingestion using COPY INTO and CTAS - Data transformation and modeling 1. SQL and PySpark transformations 2. Delta Lake table design and SCD patterns 3. Joins, aggregations, and normalization/denormalization - Data quality and validation 1. Schema enforcement and validation rules 2. Handling nulls, duplicates, and missing data 3. Pipeline expectations and data quality constraints
Topic 3: Secure and govern data using Unity Catalog	15-20%	- Access control and policies 1. Attribute-based access control (ABAC) 2. Row-level and column-level security 3. Tags and policy enforcement - Data governance fundamentals 1. Catalog, schema, and table management 2. Data lineage and auditing
Topic 4: Deploy and manage data pipelines and workloads	30-35%	- Operational reliability 1. Monitoring and logging (Azure Monitor integration) 2. Error handling and retries - Pipeline design and orchestration 1. Databricks Jobs and Workflows 2. Notebook-based vs declarative pipelines - Lakehouse architecture operations 1. Delta Lake optimization and clustering strategies 2. Delta Live Tables pipelines

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks DP-750 Prüfungsfragen mit Lösungen (Q11-Q16):

11. Frage
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?

A. a Databricks notebook triggered by a job
B. a continuous job in Lakeflow Jobs
C. an Azure Data Factory pipeline that has data flows
D. a Lakeflow Spark Declarative Pipelines (SDP) pipeline

Antwort: A

Begründung:
A Databricks notebook provides the flexibility required to implement complex, highly customized Python and PySpark transformations. Scheduling that notebook as a Lakeflow Jobs task supplies native daily orchestration, monitoring, retries, and compute management without introducing another service. Azure Data Factory data flows are oriented toward visually designed transformations and would add external orchestration complexity for logic already implemented most naturally in Python. A continuous job is inappropriate because the workload runs once per day rather than continuously. Spark Declarative Pipelines is effective for declarative batch and streaming ETL, but it is less direct when the core requirement emphasizes highly customized procedural Python transformations. A notebook task therefore provides the necessary programming freedom while keeping scheduling and operation inside Azure Databricks.

12. Frage
You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:
Two task-level settings solve this:
Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
Task2 run condition set to 'All succeeded' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2's 'Depends on' setting in the job configuration.
These two settings are independent and composable. Task1's retry policy gives it multiple chances to recover.
Task2's dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries

13. Frage
Hotspot Question
You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming. The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

14. Frage
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to 'Skip' (or 'Allow only one concurrent run'). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to 'Skip' is 'Wait' (queue the new run), which ensures every scheduled run eventually executes
- but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs

15. Frage
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to profile a table to meet the following requirements:
* The count of null values per column must be evaluated repeatedly as new records are added to the table.
* Changes in the count of null values must be observable over the progression of the dataset.
Which type of profile should you create?

A. inference
B. snapshot
C. time series

Antwort: C

Begründung:
A time series profile repeatedly calculates data-quality metrics as a table changes and preserves those measurements over time. This makes it possible to observe whether the number of null values in each column is increasing, decreasing, or remaining stable as new records are added. A snapshot profile evaluates the table at a particular point in time and is therefore insufficient when the requirement is to analyze metric progression across multiple updates. Inference is associated with deriving information such as a schema or statistical characteristics; it is not the profile type used to maintain a historical sequence of monitoring measurements.
Because the question requires repeated evaluation and the ability to observe changes throughout the dataset's progression, the time series profile satisfies both requirements.

16. Frage
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