Implementing Data Engineering Solutions Using Azure Databricks: DP-750 Exam


"Implementing Data Engineering Solutions Using Azure Databricks", also known as DP-750 exam, is a Microsoft Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 93 Q&As to your DP-750 Exam preparation. In the DP-750 exam resources, you will cover every field and category in Microsoft Certified: Fabric Data Engineer Associate Certification helping to ready you for your successful Microsoft Certification.

  • Exam Code: DP-750
  • Exam Name: Implementing Data Engineering Solutions Using Azure Databricks
  • Total Questions: 93

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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Configure and manage Azure Databricks environments15-20%- Workspace and compute configuration
  • 1. Cluster types and configuration (job, all-purpose, serverless)
    • 2. Runtime, Spark, and Photon configuration
      • 3. Autoscaling, termination, and performance tuning
        - Security and authentication setup
        • 1. Service principals and managed identities
          • 2. Access control for compute resources
            • 3. Azure Key Vault integration
              Prepare and process data30-35%- Data transformation and modeling
              • 1. Joins, aggregations, and normalization/denormalization
                • 2. Delta Lake table design and SCD patterns
                  • 3. SQL and PySpark transformations
                    - 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
                                Secure and govern data using Unity Catalog15-20%- Data governance fundamentals
                                • 1. Data lineage and auditing
                                  • 2. Catalog, schema, and table management
                                    - Access control and policies
                                    • 1. Tags and policy enforcement
                                      • 2. Attribute-based access control (ABAC)
                                        • 3. Row-level and column-level security
                                          Deploy and manage data pipelines and workloads30-35%- 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

                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions:

                                                      Question 1

                                                      You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.


                                                      Question 2

                                                      You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
                                                      Job1 processes raw data files stored in Azure Storage.
                                                      New files arrive at unpredictable intervals.
                                                      You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
                                                      Which type of job trigger should you use?

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


                                                      Question 3

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


                                                      Question 4

                                                      You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
                                                      Metastore1 contains a catalog named catalog 1.
                                                      You need to create a new schema named schema2 that meets the following requirements:
                                                      * Is contained in catalog1
                                                      * Uses abfss://containergstorageaccount.dfs.core.windows.net/data as the Managed location Which SQL statement should you execute?

                                                      A. CREATE SCHEMA catalog1.schema2
                                                      WITH DBPROPERTIES (LOCATION= ' abfss://[email protected]/data
                                                      ' );
                                                      B. CREATE SCHEMA catalog1.schema2
                                                      MANAGED LOCATION ' abfss://[email protected]/data ' ;
                                                      C. CREATE CATALOG schema2
                                                      MANAGED LOCATION ' abfss://[email protected]/data ' ;
                                                      D. CREATE SCHEMA catalog1.schema2
                                                      LOCATION ' abfss://[email protected]/data ' ;


                                                      Question 5

                                                      You need to curate Unity Catalog objects that reference the ERP data. The solution must meet the governance requirements.
                                                      What should you do?

                                                      A. Run the ALTER TABLE command on the foreign catalog tables to add new columns that are required for analytics.
                                                      B. Create Delta tables directly inside the foreign catalog by running the CREATE TABLE AS SELECT (CTAS) command.
                                                      C. In the managed analytics catalog, create views that reference foreign catalog tables that use three-level naming.
                                                      D. Create a volume in the foreign catalog and store curated Delta tables in the volume.


                                                      Solutions:

                                                      Question 1
                                                      Answer: Only visible for members
                                                      Question 2
                                                      Answer: A
                                                      Question 3
                                                      Answer: D
                                                      Question 4
                                                      Answer: B
                                                      Question 5
                                                      Answer: C

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