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Microsoft DP-750 Exam Syllabus Topics:

Section Weight Objectives
Topic 1: Prepare and process data 30-35% - Data quality and validation
  • 1. Schema enforcement and validation rules
  • 2. Pipeline expectations and data quality constraints
  • 3. Handling nulls, duplicates, and missing data
- Data ingestion
  • 1. Auto Loader and CDC ingestion patterns
  • 2. Streaming ingestion using Spark Structured Streaming
  • 3. Batch ingestion using COPY INTO and CTAS
- Data transformation and modeling
  • 1. Joins, aggregations, and normalization/denormalization
  • 2. Delta Lake table design and SCD patterns
  • 3. SQL and PySpark transformations
Topic 2: Deploy and manage data pipelines and workloads 30-35% - Lakehouse architecture operations
  • 1. Delta Live Tables pipelines
  • 2. Delta Lake optimization and clustering strategies
- Pipeline design and orchestration
  • 1. Databricks Jobs and Workflows
  • 2. Notebook-based vs declarative pipelines
- Operational reliability
  • 1. Monitoring and logging (Azure Monitor integration)
  • 2. Error handling and retries
Topic 3: Secure and govern data using Unity Catalog 15-20% - Data governance fundamentals
  • 1. Data lineage and auditing
  • 2. Catalog, schema, and table management
- Access control and policies
  • 1. Attribute-based access control (ABAC)
  • 2. Row-level and column-level security
  • 3. Tags and policy enforcement
Topic 4: Configure and manage Azure Databricks environments 15-20% - Security and authentication setup
  • 1. Access control for compute resources
  • 2. Service principals and managed identities
  • 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)

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q44-Q49):

NEW QUESTION # 44
You have an Azure Databricks workspace that uses serverless compute.
You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available.
Which type of job trigger should you use for the ingestion?

  • A. scheduled
  • B. file arrival
  • C. continuous
  • D. manual

Answer: C

Explanation:
The correct answer is D - Continuous trigger.
A Continuous trigger keeps the job running as a perpetual loop. As soon as one micro-batch or iteration completes, the next begins. New records are picked up with the shortest possible latency - as close to real- time as a Lakeflow Jobs pipeline gets.
File Arrival (Option B) is event-driven but introduces per-file trigger overhead and is best suited for file-based ingestion rather than continuous streaming workloads. Scheduled (Option C) runs at fixed clock intervals - if new data arrives between runs, it waits until the next scheduled execution. Manual (Option A) requires a human to start each run.
The question specifies serverless compute, which pairs naturally with Continuous trigger because serverless handles cluster lifecycle automatically - the job stays active without managing a persistent cluster. 'New records must be processed as soon as they become available' is the exact use case the Continuous trigger is designed for.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/triggers

 

NEW QUESTION # 45
You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?

  • A. Disable autoscaling for a job compute cluster.
  • B. Increase an all-purpose cluster to a larger fixed node type.
  • C. Enable Photon acceleration for a job compute cluster.
  • D. Move the ingestion pipelines to shared compute.

Answer: C

Explanation:
The correct answer is A. Photon is Azure Databricks ' native vectorized query engine, written in C++, designed to accelerate data ingestion and SQL-heavy workloads significantly over the standard Spark JVM path. Enabling it on a job compute cluster directly addresses Contoso ' s requirement for ' fast and consistent performance for BI workloads ' and ' production ingestion workloads that can scale automatically during telemetry spikes. ' Photon integrates transparently - no code changes are needed - and pairs well with autoscaling job clusters to handle the bursty 40,000-sensor telemetry load.
Option B contradicts the isolation requirement: Contoso explicitly needs production and development separated, not merged onto shared compute. Option C with a fixed large node gives peak capacity at all times, driving up costs even during quiet periods. Option D disabling autoscaling is the opposite of what ' s needed
- telemetry spikes require elastic scaling, not a locked node count.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/photon

 

NEW QUESTION # 46
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 # 47
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Column1. Table2 contains a column named Column2.
You run the following query.
SELECT Colum1
FROM Table1
GROUP BY Column1
HAVING COUNT(*) > 1
INTERSECT
SELECT Column2
FROM Table2
GROUP BY Column2
HAVING COUNT(*) > 1;
What occurs when you run the query?

  • A. Values appear in either table more than once.
  • B. Values appear in Table2 but NOT Table1.
  • C. Values appear in both tables more than once.
  • D. Values appear in Table1 more than once.

Answer: C

Explanation:
The query will output a single column containing only the values that are duplicated within Table1 and also duplicated within Table2.
Duplicates Isolated First: The GROUP BY and HAVING COUNT(*) > 1 clauses filter each table independently to find only their duplicate rows.
Matching Values Found: The INTERSECT operator compares the filtered results of both tables.
Shared Values Returned: Only values that exist in both filtered datasets are included in the final output.
Automatic Distinct Filtering: INTERSECT automatically removes any duplicate values from the final result set, ensuring each matching value appears exactly once.
Column Naming: The output column will inherit its name from the first SELECT statement (Column1).
Reference:
https://quizlet.com/study-guides/inf3707-chapter-4-data-retrieval-from-2-or-more-tables-
6ec17b26-76a8-4c33-8fa7-18d64154512d

 

NEW QUESTION # 48
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. Sales1
  • B. Space1
  • C. Schema1
  • D. Notebook1

Answer: B

Explanation:
The instructions must be added to Space1 because a Genie knowledge store is scoped to the individual Genie space, now called a Genie Agent. Adding synonyms and business-language instructions there teaches Space1 that "transaction," "event," "order," and "invoice" refer to sales information in Sales1. The configuration affects only that Genie space, satisfying the requirement that other spaces remain unchanged. Adding instructions to Sales1 or Schema1 would modify shared Unity Catalog metadata and could affect other consumers of those objects. Notebook1 is unrelated to the semantic instructions used by Genie when converting natural-language questions into SQL. Genie knowledge stores contain space-specific definitions, synonyms, join relationships, SQL expressions, and prompt-matching guidance without changing the underlying Unity Catalog objects. Microsoft Learn

 

NEW QUESTION # 49
......

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