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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Secure and govern Unity Catalog objects	15–20%	- Manage data sharing and permissions 1. Grant and revoke permissions, manage groups and service principals 2. Set up external locations and storage credentials - Implement data governance and security 1. Configure access control: row-level, column-level, attribute-based security 2. Manage catalogs, schemas, tables, views, and volumes 3. Enforce data quality, lineage, and auditing
Prepare and process data	30–35%	- Optimize and manage data storage 1. Optimize Delta tables: partitioning, Z-ordering, vacuum, optimize 2. Handle structured, semi-structured, and unstructured data 3. Implement lakehouse architecture and manage table versions - Ingest and transform data 1. Implement schema enforcement, schema drift, and slowly changing dimensions 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake 3. Ingest batch and streaming data from multiple sources
Deploy and maintain data pipelines and workloads	30–35%	- Build and orchestrate pipelines 1. Design and implement Lakeflow Spark Declarative Pipelines 2. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs 3. Configure Lakeflow Jobs: schedules, triggers, alerts, retries - Monitor, troubleshoot, and maintain workloads 1. Apply SDLC practices and version control 2. Monitor performance, logs, and execution metrics 3. Troubleshoot failures, repair and restart jobs
Set up and configure an Azure Databricks environment	15–20%	- Select and configure compute resources 1. Choose compute types: serverless, job compute, SQL warehouse, classic compute 2. Manage workspace settings, permissions, and networking 3. Configure cluster policies, instance pools, and libraries - Integrate with Azure services 1. Configure monitoring with Azure Monitor and diagnostic settings 2. Connect to Azure Data Lake Storage, Azure Data Factory, Microsoft Entra ID

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q48-Q53):

NEW QUESTION # 48
Hotspot Question
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 contains three tasks named Task1, Task2, and Task3.
If Task1 fails, Task2 and Task3 must be prevented from running. Successfully completed tasks must NOT rerun during recovery.
You need to configure Job1 to support controlled failure handling and recovery.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 49
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.

Answer:

Explanation:

NEW QUESTION # 50
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?

A. Use a select statement with where conditions to validate the data before querying.
B. Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
C. Add a not null constraint to transactionjd and a check constraint to amount.
D. Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.

Answer: C

Explanation:
The correct answer is D - a NOT NULL constraint on transaction_id and a CHECK constraint on amount.
Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount
> 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.
Options A and C (SELECT with WHERE / views) are read-time constructs - they don't prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely.
Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-constraints

NEW QUESTION # 51
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount.isNotNull())
Does this meet the goal?

A. No
B. Yes

Answer: B

Explanation:
The correct answer is A - Yes.
df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark ' s filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed.
This works correctly because isNotNull() is explicitly null-aware - unlike the != None comparison in Q52, it doesn ' t rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark.
Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=[ ' order_amount ' ]) produce identical results. The choice between them is stylistic - isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics

NEW QUESTION # 52
What does the VACUUM command do in Delta Lake?

A. Compresses JSON files
B. Optimizes query performance
C. Rewrites table schema
D. Deletes old unreferenced files

Answer: D

Explanation:
VACUUM removes old, unreferenced files that are no longer needed after updates or deletes. It helps manage storage but does not improve query performance directly. OPTIMIZE handles performance improvements. Schema changes are unrelated.

NEW QUESTION # 53
......

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