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Microsoft DP-750 Exam Syllabus Topics:

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

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

Question #1

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed You need to configure the job alerting behavior.
What should trigger a notification?

A. a task failure
B. job success
C. task success
D. a job failure

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Question #2

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:
When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream - inserts, updates, and deletes - requires this pattern:
spark.readStream.format( ' delta ' ).option( ' readChangeFeed ' , ' true ' ).table( ' db1.sales_orders ' ) The readChangeFeed option switches the reader from the default ' new rows only ' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.
Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-change-data-feed

Question #3

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
Deleted data must be retained for 30 days to support audits.
Deleted data that is older than 30 days must be removed permanently.
The solution must minimize administrative effort.
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. delta.autoOptimize.autoCompact
B. delta.logRetentionDuration
C. delta.enableDeletionVectors
D. delta.timeUntilArchived
E. delta.deletedFileRetentionDuration

Reveal Solution Discussion 0

Correct Answer: B,E 🗳️

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Question #4

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains:
* A catalog named Corpdb
* A schema named Finance in the Corpdb catalog
* A table named Sales in the Finance schema
You have a group named Analysts.
You assign the following permissions to Analysts:
* USE CATALOG on the Corpdb catalog
* USE SCHEMA on the Finance schema
* SELECT on the Sales table
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:

The Analysts group can query the Sales table because it has all three privileges required by the Unity Catalog hierarchy: USE CATALOG on Corpdb, USE SCHEMA on Finance, and SELECT on Sales. These permissions allow the group to navigate through the catalog and schema and read the table. However, USE SCHEMA does not grant permission to create tables. Creating a table in Finance would additionally require CREATE TABLE on that schema. Similarly, USE CATALOG only permits access to the catalog; it does not permit schema creation. Creating a schema in Corpdb would require the CREATE SCHEMA privilege on the catalog. Therefore, only the first statement is true.

Question #5

You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?