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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Catalog, schema, and table management 2. Data lineage and auditing
Topic 2: Prepare and process data	30-35%	- 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. Pipeline expectations and data quality constraints 2. Handling nulls, duplicates, and missing data 3. Schema enforcement and validation rules - Data ingestion 1. Batch ingestion using COPY INTO and CTAS 2. Auto Loader and CDC ingestion patterns 3. Streaming ingestion using Spark Structured Streaming
Topic 3: Configure and manage Azure Databricks environments	15-20%	- Security and authentication setup 1. Service principals and managed identities 2. Access control for compute resources 3. Azure Key Vault integration - Workspace and compute configuration 1. Runtime, Spark, and Photon configuration 2. Autoscaling, termination, and performance tuning 3. Cluster types and configuration (job, all-purpose, serverless)
Topic 4: Deploy and manage data pipelines and workloads	30-35%	- Operational reliability 1. Error handling and retries 2. Monitoring and logging (Azure Monitor integration) - Lakehouse architecture operations 1. Delta Live Tables pipelines 2. Delta Lake optimization and clustering strategies - 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:

1. You have an Azure Databricks workspace named Workspace! that uses a Git repository. The repository contains a Databricks notebook named Notebook1.
From the main branch, you create a feature branch named Branch! and commit changes to Notebooks Another user commits changes to Notebook1 in main.
When you attempt to merge Branch! into main, the merge fails due to conflicts.
You need to merge Branch! into the main branch. The solution must ensure that Notebook1 includes all the changes from both the branches.
What should you do?

A) Apply the changes directly to the main branch.
B) From Workspace1, clone Branch! as a new repository.
C) From Workspace1, clone the mam branch as a new repository.
D) Apply the main branch changes to Branch! and resolve the conflicts.

2. You have an Azure Databricks workspace named Workspace1 that contains a takehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a new native catalog in Unity Catalog. Does this meet the goal?

A) Yes
B) No

3. You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?

A) Schema1
B) Notebook1
C) Sales1
D) Space1

4. You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

5. You need to configure the telemetry pipeline to support the planned changes for pipeline orchestration and address the resiliency issues.
What should you do?

A) Use Lakeflow Jobs and define task dependencies between the ingestion, cleansing, and curation tasks.
B) Create a single Databricks notebook that contains all the ingestion, cleansing, and curation logic in sequential cells.
C) Create three separate jobs and schedule them to run at staggered times.
D) Use Azure Data Factory to orchestrate three separate Databricks notebook activities and define the task dependencies between each notebook activity.

Solutions:

Question # 1
Answer: D

Question # 2
Answer: B

Question # 3
Answer: D

Question # 4
Answer: Only visible for members

Question # 5
Answer: A