Testking Microsoft DP-750 Learning Materials | Exam DP-750 Tutorial

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. 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
Topic 2: Secure and govern Unity Catalog objects	15–20%	- Implement data governance and security 1. Enforce data quality, lineage, and auditing 2. Configure access control: row-level, column-level, attribute-based security 3. Manage catalogs, schemas, tables, views, and volumes - Manage data sharing and permissions 1. Set up external locations and storage credentials 2. Grant and revoke permissions, manage groups and service principals
Topic 3: Deploy and maintain data pipelines and workloads	30–35%	- 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 - 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
Topic 4: 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. Ingest batch and streaming data from multiple sources 2. Transform using Spark SQL, PySpark, Scala, and Delta Lake 3. Implement schema enforcement, schema drift, and slowly changing dimensions

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q73-Q78):

NEW QUESTION # 73
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?

A. USE CATALOG and SELECT
B. USE CATALOG, USE SCHEMA, and SELECT
C. USE SCHEMA and SELECT
D. USE CATALOG and USE SCHEMA

Answer: B

NEW QUESTION # 74
Drag and Drop Question
You have an Azure Databricks workspace that contains an all-purpose compute cluster named Cluster1. Cluser1 is used for interactive development.
You need to configure Cluster1 to meet the following requirements:
- Automatically add and remove worker nodes based on workload demand.
- Automatically shut down when the cluster has been idle for a specific period.
What should you configure for each requirement? To answer, drag the appropriate options to the correct requirements. Each option 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:

NEW QUESTION # 75
You have an Azure Databricks workspace that uses Databricks SQL.
You have a table named sales_goals_source that contains the following columns:
* Salesperson
* Item
* 2019
* 2020
* 2021
You need to transform the year columns into rows and return the columns Salesperson, Item, Year, and Value.
How should you complete the SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
SELECT Salesperson, Item, Year, Value
FROM sales_goals_source
UNPIVOT
(
Value FOR [first dropdown] IN [second dropdown]
);

Answer:

Explanation:

Explanation:
First dropdown: Year
Second dropdown: (2019, 2020, 2021)
The UNPIVOT operator converts the separate 2019, 2020, and 2021 columns into rows. Value becomes the output column containing the values previously stored in those year columns. Year becomes the output name column that identifies the original column from which each value came. Therefore, the expression must use Value FOR Year IN (2019, 2020, 2021). The Salesperson and Item columns are not included in the IN list because they remain identifier columns and are repeated for every resulting year row. A single source row consequently produces three output rows-one for each listed year. Selecting (Year) would reference an output name rather than the source columns that must be rotated.

NEW QUESTION # 76
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains:
A catalog named Finance
A schema named Purchases in the Finance catalog
A table named Transactions in the Purchases schema
You need to ensure that a user named finance_user can query the Transactions table. The solution must follow the principle of least privilege.
Which permission should you grant to finance_user for each object? To answer, drag the appropriate permissions to the correct objects. Each permission 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 Finance catalog: USE CATALOG
The Purchases schema: USE SCHEMA
The Transactions table: SELECT

NEW QUESTION # 77
You have a Lakeflow Spark Declarative Pipelines {SDP) pipeline in Azure Databricks. The pipeline ingests transaction data into a table named Table1.
You need to ensure that in the event of an invalid record, the pipeline continues to run. The solution must meet the following requirements:
* Invalid records must NOT be written to Table 1.
* Invalid records must be preserved for review.
* Minimize development effort
What should you do?

A. Run were clauses in downstream queries to filter out invalid records.
B. Implement advanced logic to quarantine the invalid records.
C. Define a pipeline expectation.
D. Add a check constraint to Table1

Answer: C

Explanation:
The correct answer is B - define a pipeline expectation.
SDP expectations with @dlt.expect_or_drop are built precisely for this scenario: the pipeline keeps running, bad records are excluded from Table1, and those records are automatically captured in the pipeline's event log as expectation violations - available for review without any extra code.
Option A (custom quarantine logic) would work but requires writing and maintaining additional pipeline tables and routing logic. The whole point of SDP expectations is to handle this pattern declaratively, with far less code.
Option C (WHERE clauses in downstream queries) is a read-time filter, not a write-time guard. Invalid records would still land in Table1 and would simply be hidden from downstream views - they're not preserved for review in any structured way. Option D (check constraint on Table1) would throw an exception on write and halt the pipeline, violating the 'pipeline continues to run' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta-live-tables/expectations

NEW QUESTION # 78
......

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