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Free Certified Data Engineer Professional Exam Braindumps
Certified Data Engineer Professional Questions 73–80 (page: 11)

Updated On: 16-Apr-2026
Page 11 of 44
PDF with 339 Questions

A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.

Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

  1. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
  2. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
  3. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
  4. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the
    source is not queried too frequently.
  5. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.

Answer(s): C



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

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

Answer(s): D



A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.

In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

  1. Set the configuration delta.deduplicate = true.
  2. VACUUM the Delta table after each batch completes.
  3. Perform an insert-only merge with a matching condition on a unique key.
  4. Perform a full outer join on a unique key and overwrite existing data.
  5. Rely on Delta Lake schema enforcement to prevent duplicate records.

Answer(s): C



A data pipeline uses Structured Streaming to ingest data from Apache Kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka-generated timestamp, key, and value. Three months after the pipeline is deployed, the data engineering team has noticed some latency issues during certain times of the day.

A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recorded by Apache Spark) as well as the Kafka topic and partition. The team plans to use these additional metadata fields to diagnose the transient processing delays.

Which limitation will the team face while diagnosing this problem?

  1. New fields will not be computed for historic records.
  2. Spark cannot capture the topic and partition fields from a Kafka source.
  3. New fields cannot be added to a production Delta table.
  4. Updating the table schema will invalidate the Delta transaction log metadata.
  5. Updating the table schema requires a default value provided for each field added.

Answer(s): A



In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.

The function is displayed below with a blank:



Which response correctly fills in the blank to meet the specified requirements?






Answer(s): E



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?

  1. 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.
  2. 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.
  3. 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.
  4. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
  5. 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.

Answer(s): C



The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.

Which approach will ensure that this requirement is met?

  1. When a database is being created, make sure that the LOCATION keyword is used.
  2. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
  3. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
  4. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
  5. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Answer(s): C



The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing-specific fields have not been approved for the sales org.

Which of the following solutions addresses the situation while emphasizing simplicity?

  1. Create a view on the marketing table selecting only those fields approved for the sales team; alias the names of any fields that should be standardized to the sales naming conventions.
  2. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
  3. Use a CTAS statement to create a derivative table from the marketing table; configure a production job to propagate changes.
  4. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from the marketing table.
  5. Instruct the marketing team to download results as a CSV and email them to the sales organization.

Answer(s): A



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