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| Section | Weight | Objectives |
|---|---|---|
| Data Modelling | 6% | - Delta Lake table design - Medallion Architecture implementation - Schema design and management |
| Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization |
| Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation |
| Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Monitoring and Alerting | 10% | - Pipeline observability and logging - Performance and health monitoring - Setting up alerts and notifications |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
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NEW QUESTION # 145
In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone.
A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?
Answer: C
Explanation:
In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations like vacuum on these clones.
Reference: Databricks documentation on Delta Lake, particularly the sections on cloning tables (shallow and deep cloning) and data retention with the vacuum command (https://docs.databricks.com/delta/index.html).
NEW QUESTION # 146
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?
Answer: D
Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints.
Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.References:
* Databricks Documentation on Delta Lake: Delta Lake Guide
* Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake
NEW QUESTION # 147
A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog- enabled workspace . The data engineering team shares large Delta tables internally via Databricks-to- Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types.
What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to- Databricks or Open Sharing?
Answer: B
Explanation:
According to Databricks' documentation, Open Sharing allows secure sharing of Delta tables to any recipient via a REST-based protocol without requiring a Databricks account. However, the Open Sharing protocol is limited to static Delta tables -it does not support sharing of Unity Catalog objects like Volumes, Machine Learning models, or notebooks . Only Databricks-to-Databricks sharing supports dynamic data sharing with update visibility and streaming reads. Thus, the inability to share non-table objects in Open Sharing represents a known limitation of the protocol. Option A accurately reflects this constraint as described in Delta Sharing design principles and documentation.
NEW QUESTION # 148
While investigating a data issue, you wanted to review yesterday's version of the table using below command, while querying the previous version of the table using time travel you realized that you are no longer able to view the historical data in the table and you could see it the table was updated yesterday based on the table history(DESCRIBE HISTORY table_name) command what could be the reason why you can not access this data?
SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)
Answer: C
Explanation:
Explanation
The answer is, VACUUM table_name RETAIN 0 was ran
The VACUUM command recursively vacuums directories associated with the Delta table and re-moves data files that are no longer in the latest state of the transaction log for the table and are older than a retention threshold. The default is 7 Days.
When VACUUM table_name RETAIN 0 is ran all of the historical versions of data are lost time travel can only provide the current state.
NEW QUESTION # 149
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control. The team decided to use Databricks system tables, available under the system catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the system catalog to achieve this?
Answer: A
Explanation:
Databricks documents system.billing.usage as the correct system table for billable usage analysis, and it documents identity_metadata.run_as as the field that records who ran supported workloads such as jobs, notebooks, and Lakeflow Spark Declarative Pipelines. For "top users consuming compute resources," summing usage_quantity by identity_metadata.run_as is the correct conceptual approach. ( Databricks Documentation ) The other options are not aligned with the documented schema or metric usage. identity_metadata.created_by is not the general compute-consumer identity field for jobs and notebook workloads; it applies to specific products such as Databricks Apps and certain agent workloads. usage_quantity should be summed, not counted, and usage_unit is not something you multiply into DBUs in the way shown. usage_metadata.
run_name is not the documented user identity field for this purpose. As written, option C is the only option that matches the official identity model for user-attributed compute consumption. ( Databricks Documentation )
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NEW QUESTION # 150
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