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Microsoft DP-750 Exam Syllabus Topics:

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

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q37-Q42):

                                                      NEW QUESTION # 37
                                                      What does the VACUUM command do in Delta Lake?

                                                      Answer: A

                                                      Explanation:
                                                      VACUUM removes old, unreferenced files that are no longer needed after updates or deletes. It helps manage storage but does not improve query performance directly. OPTIMIZE handles performance improvements. Schema changes are unrelated.


                                                      NEW QUESTION # 38
                                                      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?

                                                      Answer: C

                                                      Explanation:
                                                      The correct answer is A - INSERT INTO.
                                                      INSERT INTO appends all rows from the source to the target. Existing rows in table2 are untouched - the historical data stays intact - and the current year's rows from table1 are added. That is exactly what the requirement asks: 'preserve any existing data and minimise processing effort.' Option B (CREATE TABLE AS SELECT) would fail if table2 already exists, or if used with CREATE OR REPLACE it wipes table2 entirely before writing - historical data gone. Option C (INSERT OVERWRITE) replaces the entire table content with only the table1 rows, also destroying table2's existing data. Option D (CREATE OR REPLACE TABLE AS SELECT) explicitly drops and recreates the table, eliminating all historical records.
                                                      INSERT INTO is deliberately the simplest possible command here - one line, no special options, no risk of accidental data loss.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-insert-into


                                                      NEW QUESTION # 39
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Sales_orders.
                                                      Sales_orders stores historical sales data.
                                                      You receive a daily CSV file daily that contains new sales records only. The file does NOT contain updates to existing rows.
                                                      You need to load the daily data into Sales_orders. The solution must meet the following requirements:
                                                      - Preserve the existing data.
                                                      - Add only the new records.
                                                      - Minimize processing effort.
                                                      Which command should include in the loading strategy?

                                                      Answer: B

                                                      Explanation:
                                                      The best command for this scenario is INSERT INTO.
                                                      The INSERT INTO command appends new rows directly to the end of an existing Delta table.
                                                      Because your daily CSV file contains only new records and zero updates to existing rows, simply appending the data completely satisfies your requirements. It leaves all historical data untouched and requires the absolute lowest processing power because Databricks does not need to scan, modify, or rewrite any existing files.
                                                      Incorrect:
                                                      The UPDATE command is used to modify values in rows that already exist in the table based on a matching condition. It cannot be used to add entirely new rows to a table, and it requires a heavy scan of the data to find matches, which violates the requirement to keep processing low.
                                                      The INSERT OVERWRITE command deletes all the existing historical data in the table (or a specific partition) and replaces it with the data from the new CSV file. Using this would erase your historical sales data.
                                                      Reference:
                                                      https://medium.com/@jithujosekokken/understanding-data-patterns-in-medallion-architecture-full- incremental-and-change-only-loads-e33db28e51f4


                                                      NEW QUESTION # 40
                                                      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?

                                                      Answer: D

                                                      Explanation:
                                                      The correct answer is A. The Unity Catalog DDL for creating a schema inside a specific catalog and setting a custom managed storage path uses the three-part name (catalog.schema) and the MANAGED LOCATION clause:
                                                      CREATE SCHEMA catalog1.schema2 MANAGED LOCATION 'abfss://...';
                                                      The three-part name explicitly places the schema inside catalog1. MANAGED LOCATION tells Unity Catalog where to store managed tables and volumes created under this schema - any managed table without its own explicit location will inherit this path.
                                                      Option B uses CREATE CATALOG, which creates an entirely new catalog rather than a schema. Option C uses the LOCATION keyword without MANAGED - that syntax is for external locations, not for overriding the managed storage path of a schema. Option D uses WITH DBPROPERTIES, which stores arbitrary key- value metadata but has no effect on where Unity Catalog physically stores data.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-ddl-create- schema


                                                      NEW QUESTION # 41
                                                      You have an Azure Databricks workspace that contains a Git folder and uses Azure Repos as the Git provider.
                                                      From the main branch, you create a branch named Branch1. You commit changes to Branch1.
                                                      You need to incorporate the changes from Branch1 into main The solution must preserve the commit history in the repository. Which command should you run?

                                                      Answer: C

                                                      Explanation:
                                                      The correct answer is A - merge.
                                                      A Git merge combines the histories of two branches by creating a merge commit that joins them. Every individual commit from Branch1 remains visible in the repository log - the full development history is preserved. This is the requirement: 'the solution must preserve the commit history in the repository.' Option C (rebase) moves Branch1's commits on top of main by replaying them as new commits with new hashes. The end result looks like a linear history, but the original commit hashes are rewritten - the prior history is not preserved in its original form. For a shared repository, rebase rewrites public history, which is considered problematic.
                                                      Option B (pull) fetches remote changes and merges or rebases them into the current branch - it's used to sync with a remote, not to incorporate a feature branch. Option D (push) sends local commits to the remote but doesn't incorporate any branch into another.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/repos/git-operations-with-repos


                                                      NEW QUESTION # 42
                                                      ......

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