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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and maintain data pipelines and workloads30โ€“35%- Build and orchestrate pipelines
  • 1. Implement CI/CD with Git, Databricks Asset Bundles, CLI, and APIs
  • 2. Configure Lakeflow Jobs: schedules, triggers, alerts, retries
  • 3. Design and implement Lakeflow Spark Declarative Pipelines
- Monitor, troubleshoot, and maintain workloads
  • 1. Apply SDLC practices and version control
  • 2. Troubleshoot failures, repair and restart jobs
  • 3. Monitor performance, logs, and execution metrics
Set up and configure an Azure Databricks environment15โ€“20%- Select and configure compute resources
  • 1. Manage workspace settings, permissions, and networking
  • 2. Choose compute types: serverless, job compute, SQL warehouse, classic compute
  • 3. Configure cluster policies, instance pools, and libraries
- 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
Prepare and process data30โ€“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. Ingest batch and streaming data from multiple sources
  • 2. Implement schema enforcement, schema drift, and slowly changing dimensions
  • 3. Transform using Spark SQL, PySpark, Scala, and Delta Lake
Secure and govern Unity Catalog objects15โ€“20%- Implement data governance and security
  • 1. Configure access control: row-level, column-level, attribute-based security
  • 2. Enforce data quality, lineage, and auditing
  • 3. Manage catalogs, schemas, tables, views, and volumes
- Manage data sharing and permissions
  • 1. Grant and revoke permissions, manage groups and service principals
  • 2. Set up external locations and storage credentials

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q88-Q93):

NEW QUESTION # 88
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a daily batch data process that requires complex and highly customized Python transformations. The solution must minimize additional complexity.
What should you include in the solution?

Answer: B

Explanation:
A Databricks notebook provides the flexibility required to implement complex, highly customized Python and PySpark transformations. Scheduling that notebook as a Lakeflow Jobs task supplies native daily orchestration, monitoring, retries, and compute management without introducing another service. Azure Data Factory data flows are oriented toward visually designed transformations and would add external orchestration complexity for logic already implemented most naturally in Python. A continuous job is inappropriate because the workload runs once per day rather than continuously. Spark Declarative Pipelines is effective for declarative batch and streaming ETL, but it is less direct when the core requirement emphasizes highly customized procedural Python transformations. A notebook task therefore provides the necessary programming freedom while keeping scheduling and operation inside Azure Databricks.


NEW QUESTION # 89
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?

Answer: C

Explanation:
The best trigger type for this scenario is the Continuous trigger.
Immediate Processing: The Continuous trigger mode processes new records as soon as they arrive at the configured data sources. This matches your requirement to ingest and process records without waiting for an artificial time interval.
Native Serverless Support: When paired with serverless compute, Lakeflow Jobs efficiently manage resources by automatically scaling up or down according to the real-time stream volume.
Built-in Fault Tolerance: Continuous pipelines on Databricks automatically handle failures by retrying with an exponential backoff policy, keeping your automated ingestion operational 24/7 without manual intervention.
Reference:
https://docs.databricks.com/aws/en/jobs/continuous


NEW QUESTION # 90
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
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.fillna(0, subset=['order_amount'])
Does this meet the goal?

Answer: B

Explanation:
Correct:
* You run the following expression.
df.dropna(subset=["order_amount"])
The expression df.dropna(subset=["order_amount"]) is an appropriate and effective way to exclude rows where order_amount is null.
* You run the following expression.
df.filter(df.order_amount.isNotNull())
To exclude rows where the order amount is null, you can use the isNotNull() method or a SQL expression within the filter() or where() functions.Here are the standard, appropriate expressions:
Option 1: Python/PySpark API (Recommended)
pythondf_clean = df.filter(df["order_amount"].isNotNull())
Incorrect:
* You run the following expression.
df.fillna(0, subset=['order_amount'])
* You run the following expression.
df.filter(df.order_amount != None)
Reference:
https://www.geeksforgeeks.org/python/filter-pyspark-dataframe-columns-with-none-or-null-values/
https://learn.microsoft.com/en-us/azure/databricks/pyspark/reference/classes/dataframe/dropna


NEW QUESTION # 91
Which feature helps reduce data scan during query execution in Delta Lake?

Answer: B

Explanation:
Delta Lake uses data skipping based on file-level statistics (min/max values). This reduces unnecessary file scans and improves query performance. VACUUM removes old files but does not improve query speed. Cluster restart has no impact on query optimization.


NEW QUESTION # 92
Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.
SCD Type 1 overwrites the existing record whenever an attribute changes - no history is kept. Contoso's requirement for equipment metadata (name, manufacturer, model, commissioning date) states 'historical values are NOT required,' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.
SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that 'analysts must track the full history of ownership' as sensors change hands over time - that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn't satisfy the 'full history' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/merge


NEW QUESTION # 93
......

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