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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Configure and manage Azure Databricks environments	15-20%	- 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 - Security and authentication setup 1. Azure Key Vault integration 2. Service principals and managed identities 3. Access control for compute resources
Secure and govern data using Unity Catalog	15-20%	- Access control and policies 1. Tags and policy enforcement 2. Row-level and column-level security 3. Attribute-based access control (ABAC) - Data governance fundamentals 1. Data lineage and auditing 2. Catalog, schema, and table management
Prepare and process data	30-35%	- Data ingestion 1. Auto Loader and CDC ingestion patterns 2. Batch ingestion using COPY INTO and CTAS 3. Streaming ingestion using Spark Structured Streaming - Data transformation and modeling 1. Joins, aggregations, and normalization/denormalization 2. SQL and PySpark transformations 3. Delta Lake table design and SCD patterns - Data quality and validation 1. Handling nulls, duplicates, and missing data 2. Schema enforcement and validation rules 3. Pipeline expectations and data quality constraints
Deploy and manage data pipelines and workloads	30-35%	- Lakehouse architecture operations 1. Delta Live Tables pipelines 2. Delta Lake optimization and clustering strategies - 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

Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

1. You have an Azure Databricks workspace that contains a Delta table named Customer.
A job named Job1 performs frequent upserts into Customer.
You discover that Job1 has created many small Parquet files in Customer, and the small files are degrading query performance.
You need to improve query performance for the current data already stored in Customer. The solution must not affect the travel for the Customer table.
What should you do?

A) Run the OPTIMIZE command on the Customer table.
B) Set the delta.autoOptimize.optimizeWrite Apache Spark configuration to true.
C) Run the VACUUM command on the Customer table.
D) Set the delta.deletedFileRetentionDuration table property to 1 day.

2. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Tabid.
Table! is written by batch jobs every hour and is queried frequently by filtering two columns named Customerld and EventDate.
You expect Table1 to grow significantly over time.
The rows in Table1 are frequently updated and deleted to support compliance requests.
You need to keep query performance consistent as Table1 grows. The solution must minimize update and deletion effort.
What should you include in the solution? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

3. You have an Azure Databricks workspace that is enabled for Unity Catalog You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?

A) Configure a checkpoint location for the streaming query.
B) Increase the trigger interval of the streaming query.
C) Enable change data feed (CDF) for the target table.
D) Configure a watermark for the streaming query.

4. You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
Table1 stores customer profile data.
Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
You need to implement a solution that:
* Maintains persistent historical versions of customer profile records for long-term analysis.
* Allows users to query earlier versions of the Delta table.
* Minimizes maintenance effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

5. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have 500 GB of sales data stored as multiple CSV files in cloud storage.
You plan to load the data into a Delta table.
You need to ingest the bulk data by using a solution that meets the following requirements:
* Minimize how long it takes to implement the solution.
* Minimize the amount of custom code required.
What should you use?

A) COPY INTO
B) Auto Loader
C) file upload
D) Apache Spark read APIs

Solutions:

Question # 1
Answer: A

Question # 2
Answer: Only visible for members

Question # 3
Answer: A

Question # 4
Answer: Only visible for members

Question # 5
Answer: A