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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Configure and manage Azure Databricks environments15-20%- 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)
        - Security and authentication setup
        • 1. Service principals and managed identities
          • 2. Azure Key Vault integration
            • 3. Access control for compute resources
              Deploy and manage data pipelines and workloads30-35%- Pipeline design and orchestration
              • 1. Notebook-based vs declarative pipelines
                • 2. Databricks Jobs and Workflows
                  - Operational reliability
                  • 1. Error handling and retries
                    • 2. Monitoring and logging (Azure Monitor integration)
                      - Lakehouse architecture operations
                      • 1. Delta Lake optimization and clustering strategies
                        • 2. Delta Live Tables pipelines
                          Prepare and process data30-35%- Data quality and validation
                          • 1. Handling nulls, duplicates, and missing data
                            • 2. Pipeline expectations and data quality constraints
                              • 3. Schema enforcement and validation rules
                                - Data transformation and modeling
                                • 1. Delta Lake table design and SCD patterns
                                  • 2. SQL and PySpark transformations
                                    • 3. Joins, aggregations, and normalization/denormalization
                                      - Data ingestion
                                      • 1. Auto Loader and CDC ingestion patterns
                                        • 2. Batch ingestion using COPY INTO and CTAS
                                          • 3. Streaming ingestion using Spark Structured Streaming
                                            Secure and govern data using Unity Catalog15-20%- Access control and policies
                                            • 1. Attribute-based access control (ABAC)
                                              • 2. Row-level and column-level security
                                                • 3. Tags and policy enforcement
                                                  - Data governance fundamentals
                                                  • 1. Data lineage and auditing
                                                    • 2. Catalog, schema, and table management

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q74-Q79):

                                                      NEW QUESTION # 74
                                                      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.filter(df.order_amount != None)
                                                      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 # 75
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
                                                      dbl sales_orders is updated nightly and has change data feed (CDF) enabled.
                                                      You need to ingest all the changes from the dbl.sales.ordets table, including inserts, updates, and deletes, into a downstream pipeline.
                                                      How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Answer:

                                                      Explanation:

                                                      Explanation:
                                                      When Change Data Feed (CDF) is enabled on a Delta table, reading the full change stream - inserts, updates, and deletes - requires this pattern:
                                                      spark.readStream.format('delta').option('readChangeFeed', 'true').table('db1.sales_orders') The readChangeFeed option switches the reader from the default 'new rows only' mode to a mode that returns all change events. Each row in the resulting DataFrame includes a _change_type column (insert, update_preimage, update_postimage, delete) so downstream processing can distinguish what happened to each record.
                                                      Without readChangeFeed = true, streaming a Delta table only surfaces newly appended rows. Deletes and updates are invisible, making it unsuitable for true CDC pipelines. The stream also supports startingVersion or startingTimestamp options to begin from a specific point in table history rather than the current moment.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-change-data-feed


                                                      NEW QUESTION # 76
                                                      Hotspot Question
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog.
                                                      You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1.
                                                      You need to meet the following requirements:
                                                      - Records that violate rule1 must NOT be written to Table1, but the
                                                      pipeline must continue processing valid records.
                                                      - Data engineers must be able to review expectation metrics by using
                                                      minimal development effort.
                                                      What should you do? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Answer:

                                                      Explanation:


                                                      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?

                                                      Answer: B

                                                      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
                                                      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?

                                                      Answer: D


                                                      NEW QUESTION # 79
                                                      ......

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