Free Databricks Databricks-Certified-Data-Engineer-Professional Exam Dumps Questions & Answers

A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

• A.
• B.
• C.
• D.

A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

• A.The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
• B.Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
• C.Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
• D.Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
• E.Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.

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?

• A.Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
• B.All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
• C.Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
• D.Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
• E.Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

• A.Use the "Validate" option in the notebook to check for syntax errors.
• B.Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
• C.Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
• D.Open the web terminal from the notebook and run a shell command to validate the pipeline code.

A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

• A.Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
• B.Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
• C.Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
• D.Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.