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Microsoft DP-750 Exam Syllabus Topics:

SectionWeightObjectives
Deploy and manage data pipelines and workloads30-35%- Operational reliability
  • 1. Error handling and retries
    • 2. Monitoring and logging (Azure Monitor integration)
      - 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
              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. Row-level and column-level security
                      • 3. Attribute-based access control (ABAC)
                        Prepare and process data30-35%- 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
                                    - Data quality and validation
                                    • 1. Pipeline expectations and data quality constraints
                                      • 2. Handling nulls, duplicates, and missing data
                                        • 3. Schema enforcement and validation rules
                                          Configure and manage Azure Databricks environments15-20%- Workspace and compute configuration
                                          • 1. Cluster types and configuration (job, all-purpose, serverless)
                                            • 2. Autoscaling, termination, and performance tuning
                                              • 3. Runtime, Spark, and Photon configuration
                                                - Security and authentication setup
                                                • 1. Service principals and managed identities
                                                  • 2. Azure Key Vault integration
                                                    • 3. Access control for compute resources

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                                                      Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q42-Q47):

                                                      NEW QUESTION # 42
                                                      You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
                                                      Job! runs every hour.
                                                      Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
                                                      You need to configure the job scheduling behavior.
                                                      What should you configure? To answer, select the appropriate options in the answer area.
                                                      NOTE: Each correct selection is worth one point.

                                                      Answer:

                                                      Explanation:

                                                      Explanation:
                                                      Two settings address the overlapping-run problem:
                                                      Concurrent Runs policy set to 'Skip' (or 'Allow only one concurrent run'). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
                                                      Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
                                                      Combined with the concurrency setting, the job runs hourly but never overlaps.
                                                      An alternative to 'Skip' is 'Wait' (queue the new run), which ensures every scheduled run eventually executes
                                                      - but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs


                                                      NEW QUESTION # 43
                                                      You have an Azure Databricks workspace that contains multiple all-purpose clusters.
                                                      You discover that some clusters remain idle for long periods after users finish their work.
                                                      You need to reduce compute costs without affecting active workloads.
                                                      What should you do?

                                                      Answer: B

                                                      Explanation:
                                                      To reduce compute costs from idle clusters without impacting active workloads, you must configure Auto-Termination and use Cluster Policies.
                                                      Core Remedies
                                                      *-> Auto-Termination: Set a strict inactivity timeout (e.g., 20-30 minutes) on all-purpose clusters to automatically shut them down when idle.
                                                      Cluster Policies: Enforce maximum auto-termination limits across the workspace so users cannot disable or set excessively long idle timeouts.
                                                      Single User Access Mode: Use this mode where possible, as it tracks idleness more accurately than Shared mode by monitoring the specific user's activity.
                                                      Reference:
                                                      https://medium.com/@sujathamudadla1213/databricks-lakehouse-platform-describe-how- clusters-are-terminated-and-the-impact-of-terminating-a-b6236689fd2e


                                                      NEW QUESTION # 44
                                                      You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
                                                      You need to implement a data retention solution that meets the following requirements:
                                                      Deleted data must be retained for 30 days to support audits.
                                                      Deleted data that is older than 30 days must be removed permanently.
                                                      The solution must minimize administrative effort.
                                                      Which two properties should you configure? Each correct answer presents part of the solution.
                                                      NOTE: Each correct selection is worth one point.

                                                      Answer: B,D

                                                      Explanation:
                                                      The correct answers are B and D. Delta Lake's data retention behaviour is controlled by two distinct properties that work together:
                                                      delta.deletedFileRetentionDuration governs how long physically deleted data files are kept before VACUUM can remove them. Setting this to 30 days ensures deleted records remain on storage for the full audit window.
                                                      delta.logRetentionDuration controls how long the transaction log is retained. Time-travel queries (SELECT ...
                                                      VERSION AS OF or TIMESTAMP AS OF) rely on the log to reconstruct historical table states - without 30 days of log, you can't query 30-day-old snapshots even if the data files are still there.
                                                      Both properties need to be set to 30 days to fully honour the requirement. Option A (delta.timeUntilArchived) doesn't exist in Delta Lake. Option C (autoCompact) manages file sizing, not retention. Option E (enableDeletionVectors) speeds up deletes but has no bearing on how long data is kept.
                                                      Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/history#configure-data-retention


                                                      NEW QUESTION # 45
                                                      You have an Azure Databricks workspace named Workspace1 that contains a cluster named Cluster1.
                                                      You need to enable a user named User1 to install a Maven package on all the nodes of Cluster1. The solution must follow the principle of least privilege.
                                                      What should you do?

                                                      Answer: D

                                                      Explanation:
                                                      Installing a Maven package on every node requires changing Cluster1's library configuration. The CAN MANAGE permission grants the cluster-level management capabilities required to add or remove libraries without giving User1 broader control over the entire workspace or Azure resource group. CAN RESTART allows a user to restart the cluster but does not provide sufficient authority to change its library configuration.
                                                      Assigning the Azure Contributor role would grant excessive control over the resource group and does not represent the appropriate Databricks object-level permission. Making User1 a workspace administrator would provide substantially more privileges than necessary. Granting CAN MANAGE directly on Cluster1 therefore satisfies the operational requirement while limiting User1's authority to the specific compute resource that must be modified.


                                                      NEW QUESTION # 46
                                                      What does the VACUUM command do in Delta Lake?

                                                      Answer: C

                                                      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 # 47
                                                      ......

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