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

A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

• A.  Set the pipelines, reset, allowed property to false on raw_iot
• B.  Set the skipChangeCommits flag to true on bpm_stats
• C.  Set the SkipChangeCommits flag to true raw_lot
• D.  Set the pipelines, reset, allowed property to false on bpm_stats

How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming ?

• A.  Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
• B.  Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
• C.  Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
• D.  Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.

The business reporting team requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts, transforms, and loads the data for their pipeline runs in 10 minutes. Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

• A.  Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
• B.  Configure a job that executes every time new data lands in a given directory.
• C.  Schedule a Structured Streaming job with a trigger interval of 60 minutes.
• D.  Schedule a job to execute the pipeline once an hour on a new job cluster.

Which statement describes the correct use of pyspark.sql.functions.broadcast?

• A.  It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
• B.  It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
• C.  It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
• D.  It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
• E.  It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de- duplicated and validated, which statement describes what will occur when this code is executed?

• A.  An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
• B.  A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
• C.  The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
• D.  An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
• E.  The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.