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

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q26-Q31):

NEW QUESTION # 26
A data engineer notices slow query performance on a large Delta table in Azure Databricks. The table has frequent updates and deletes. Which action best improves query performance?

A. Increase cluster size
B. Run OPTIMIZE and ZORDER BY on frequently filtered columns
C. Convert table to Parquet format
D. Enable Delta caching only

Answer: B

Explanation:
OPTIMIZE compacts small files into larger ones, reducing file scan overhead. ZORDER improves data skipping by co-locating related column values. This combination significantly improves query performance on frequently filtered columns. Increasing cluster size does not address file fragmentation. Converting to Parquet removes Delta benefits like ACID transactions.

NEW QUESTION # 27
You have an Azure Databricks workspace
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically. You need to configure compute for the pipeline. The solution must minimize operational costs and effort. What should you use?

A. an all-purpose cluster that uses autoscaling
B. a job cluster that uses autoscaling
C. a single-node, all-purpose cluster
D. the existing SQL warehouse

Answer: B

Explanation:
The correct answer is C - a job cluster that uses autoscaling.
Job clusters (also called pipeline clusters in the SDP context) are created exclusively for a pipeline run and terminated when the pipeline stops. You pay only for what the pipeline uses, and there's no idle cost between runs. Autoscaling on a job cluster lets the pipeline expand during heavy processing and contract during lighter stages - the combination of on-demand lifecycle and elastic scaling gives the lowest operational cost.
Option A (all-purpose cluster) runs at a higher DBU rate and persists beyond the pipeline's lifecycle, meaning you're paying for it even when the pipeline isn't running. Option B (SQL warehouse) is optimised for interactive BI and ad-hoc queries, not for the batch/streaming workloads SDP pipelines run. Option D (single- node all-purpose) has no scaling, runs at the all-purpose DBU rate, and is capped at one node - unsuitable for any production pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/configure-pipeline

NEW QUESTION # 28
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1.table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?

A. INSERT INTO sales.schema1.table2 SELECT * FROM sales.schema1.table1;
B. CREATE OR REPLACE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;
C. INSERT OVERWRITE sales.schema1.table2 SELECT * FROM sales.schema1.table1;
D. CREATE TABLE sales.schema1.table2 AS SELECT * FROM sales.schema1.table1;

Answer: A

Explanation:
The correct answer is A - INSERT INTO.
INSERT INTO appends all rows from the source to the target. Existing rows in table2 are untouched - the historical data stays intact - and the current year's rows from table1 are added. That is exactly what the requirement asks: 'preserve any existing data and minimise processing effort.' Option B (CREATE TABLE AS SELECT) would fail if table2 already exists, or if used with CREATE OR REPLACE it wipes table2 entirely before writing - historical data gone. Option C (INSERT OVERWRITE) replaces the entire table content with only the table1 rows, also destroying table2's existing data. Option D (CREATE OR REPLACE TABLE AS SELECT) explicitly drops and recreates the table, eliminating all historical records.
INSERT INTO is deliberately the simplest possible command here - one line, no special options, no risk of accidental data loss.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-insert-into

NEW QUESTION # 29
Hotspot Question
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job1 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.

Answer:

Explanation:

NEW QUESTION # 30
You have an Azure Databricks workspace named Workspace1.
You create a compute cluster named Cluser1 that will be used to ingest data.
You need to install the required libraries on Cluster1. The solution must use Unity Catalog for access control.
What should you do?

A. Upload the libraries to a Unity Catalog volume and install the libraries on Cluster1.
B. Install the libraries on Cluster1 and manually restart the cluster.
C. Install the libraries by using pip from a notebook attached to Cluster1.
D. Upload the libraries to a Unity Catalog schema and install the libraries on Cluster1.

Answer: A

Explanation:
A Unity Catalog volume is the appropriate location because it provides governed file storage with permissions managed through Unity Catalog. After uploading the library package to the volume, it can be configured as a cluster library on Cluster1. This approach permits centralized access control, auditing, and lifecycle management. Option A installs the library without placing its source under Unity Catalog governance. Option B provides notebook-scoped dependency management but does not, by itself, satisfy the requirement that Unity Catalog control access to the library artifact. A schema is a logical container for tables, views, functions, models, and volumes; a library file cannot be uploaded directly to the schema itself, eliminating option D. Unity Catalog volumes explicitly support storing cluster libraries and job dependencies. Microsoft Learn

NEW QUESTION # 31
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