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Microsoft DP-750 Exam Syllabus Topics:

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

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q62-Q67):

                                                      NEW QUESTION # 62
                                                      You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
                                                      Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed You need to configure the job alerting behavior.
                                                      What should trigger a notification?

                                                      Answer: B

                                                      Explanation:
                                                      The correct answer is B - a job failure.
                                                      The requirement draws a clear line: non-critical task failures should be logged silently; notifications should only fire when a critical failure causes the whole job to stop. Configuring the alert on 'Job Failure' achieves this precisely - the notification triggers when the job itself reaches a Failed terminal state, which only happens when at least one critical task has failed and the job cannot complete.
                                                      Option A (task failure) would send a notification for every task-level failure, including non-critical ones.
                                                      That's exactly the noise the question wants to avoid. Option C (job success) would never alert on failures at all. Option D (task success) confirms completion but doesn't catch failures.
                                                      Setting alerting at the job level rather than the task level is also simpler to configure - you don't need to mark individual tasks as critical or non-critical in the notification settings.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/alerts


                                                      NEW QUESTION # 63
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1.
                                                      Table1 stores customer profile data.
                                                      Business users must analyze how customer profile records change over time. They must also be able to query earlier versions of the table.
                                                      You need to implement a solution that:
                                                      * Maintains persistent historical versions of customer profile records for long-term analysis.
                                                      * Allows users to query earlier versions of the Delta table.
                                                      * Minimizes maintenance 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:

                                                      Explanation:
                                                      To record historical changes: Implement a Type 2 slowly changing dimension (SCD).
                                                      To support temporal analysis: Use Delta Lake time travel.
                                                      A Type 2 slowly changing dimension preserves customer-profile history by inserting a new record whenever a tracked attribute changes instead of overwriting the existing record. Effective dates, expiration dates, version values, or current-record indicators can identify which version applied during a particular period. This provides persistent business history for long-term analysis. Delta Lake time travel supports temporal analysis of the physical table by allowing users to query an earlier version with VERSION AS OF or TIMESTAMP AS OF. Time travel is useful for auditing and reproducing previous results, but its availability depends on retained Delta log entries and data files. Therefore, it should not replace a Type 2 SCD for permanent customer history. Together, the two features satisfy the historical-record and earlier-version requirements.


                                                      NEW QUESTION # 64
                                                      You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
                                                      You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
                                                      NOTE: Each correct selection is worth one point.

                                                      Answer: C,E

                                                      Explanation:
                                                      The correct answers are B and E.
                                                      The goal is to reduce storage costs while preserving 30 days of time travel. Two actions are needed:
                                                      Set delta.deletedFileRetentionDuration to a value aligned with the 30-day requirement (Option B). Note: the answer option states '10 days' which would be insufficient for 30-day time travel - in practice this property should be set to at least 30 days. This property defines the retention floor: VACUUM will not delete any file newer than this threshold.
                                                      Run VACUUM on Table1 (Option E). VACUUM physically removes unreferenced data files older than the retention duration from storage. Without running VACUUM, obsolete files accumulate indefinitely regardless of the retention property setting - the property tells VACUUM what to keep; VACUUM is what does the actual cleanup.
                                                      Option C (OPTIMIZE) compacts small files for better query performance but never deletes anything. Option D (logRetentionDuration) keeps the transaction log for time travel but doesn't free up the data file storage.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum


                                                      NEW QUESTION # 65
                                                      You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1, metastore1 contains a catalog named catalog1.
                                                      You need to create a new schema named schema2 that meets the following requirements:
                                                      - Is contained in catalog1
                                                      - Uses abfss://container@storageaccount.dfs.core.windows.net/data as
                                                      the managed location
                                                      Which SQL statement should you execute?

                                                      Answer: B

                                                      Explanation:
                                                      To create a new schema with a specific managed location in Azure Databricks Unity Catalog, use the CREATE SCHEMA command.
                                                      CREATE SCHEMA catalog_name.schema_name
                                                      MANAGED LOCATION 'abfss://container@storageaccount.dfs.core.windows.net/data'; Catalog Name: Replace catalog_name with your existing catalog.
                                                      Schema Name: Replace schema_name with your desired new schema name.Quotes: The storage path must be enclosed in single quotes.
                                                      Privileges: You must have CREATE SCHEMA privileges on the parent catalog and ownership (or adequate permissions) on the external location.
                                                      Reference:
                                                      https://dev.to/encorepartners/creating-your-first-catalog-schema-and-tables-in-databricks-20p3


                                                      NEW QUESTION # 66
                                                      You have an Azure Databricks workspace that contains the objects shown in the following table.
                                                      Name | Type
                                                      Catalog1 | Catalog
                                                      Schema1 | Schema
                                                      Sales1 | Table
                                                      Notebook1 | Notebook
                                                      Space1 | AI/BI Genie space
                                                      Users often use the following words to refer to a sale: transaction, event, order, and invoice.
                                                      You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
                                                      To which object should you add the instructions?

                                                      Answer: A

                                                      Explanation:
                                                      The instructions must be added to Space1 because a Genie knowledge store is scoped to the individual Genie space, now called a Genie Agent. Adding synonyms and business-language instructions there teaches Space1 that "transaction," "event," "order," and "invoice" refer to sales information in Sales1. The configuration affects only that Genie space, satisfying the requirement that other spaces remain unchanged. Adding instructions to Sales1 or Schema1 would modify shared Unity Catalog metadata and could affect other consumers of those objects. Notebook1 is unrelated to the semantic instructions used by Genie when converting natural-language questions into SQL. Genie knowledge stores contain space-specific definitions, synonyms, join relationships, SQL expressions, and prompt-matching guidance without changing the underlying Unity Catalog objects. Microsoft Learn


                                                      NEW QUESTION # 67
                                                      ......

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