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Free Certified Data Engineer Professional Exam Braindumps
Certified Data Engineer Professional Questions 129–136 (page: 18)

Updated On: 16-Apr-2026
Page 18 of 44
PDF with 339 Questions

Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

  1. configure
  2. fs
  3. workspace
  4. libraries

Answer(s): B



The following table consists of items found in user carts within an e-commerce website.



The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.



How would the following update be handled?

  1. The update throws an error because changes to existing columns in the target schema are not supported.
  2. The new nested Field is added to the target schema, and dynamically read as NULL for existing unmatched records.
  3. The update is moved to a separate "rescued" column because it is missing a column expected in the target schema.
  4. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.

Answer(s): B


Reference:



An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage

directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.

For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.

Which solution meets these requirements?

  1. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
  2. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.
  3. Deduplicate records in each batch by pk_id and overwrite the target table.
  4. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

Answer(s): D



An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:

user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT

New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.

Which implementation can be used to efficiently update the described account_current table as part of each hourly batch job assuming there are millions of user accounts and tens of thousands of records processed hourly?

  1. Filter records in account_history using the last_updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
  2. Use Auto Loader to subscribe to new files in the account_history directory; configure a Structured Streaming trigger available job to batch update newly detected files into the account_current table.
  3. Overwrite the account_current table with each batch using the results of a query against the account_history table grouping by user_id and filtering for the max value of last_updated.
  4. Filter records in account_history using the last_updated field and the most recent hour processed, as well as the max last_login by user_id write a merge statement to update or insert the most recent value for each user_id.

Answer(s): D



The business intelligence team has a dashboard configured to track various summary metrics for retail stores.

This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:



For demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table, named products_per_order, includes the following fields:



Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.

Which solution meets the expectations of the end users while controlling and limiting possible costs?

  1. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
  2. Use Structured Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
  3. Define a view against the products_per_order table and define the dashboard against this view.
  4. Use the Delta Cache to persist the products_per_order table in memory to quickly update the dashboard with each query.

Answer(s): A


Reference:



A Delta lake table with CDF enabled table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.

The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.

Which approach would simplify the identification of these changed records?

  1. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
  2. nvert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
  3. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
  4. Modify the overwrite logic to include a field populated by calling spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.

Answer(s): C



A view is registered with the following code:



Both users and orders are Delta Lake tables.

Which statement describes the results of querying recent_orders?

  1. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
  2. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
  3. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
  4. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

Answer(s): D



A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto- Optimize & Auto-Compaction cannot be used.

Which strategy will yield the best performance without shuffling data?

  1. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
  2. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
  3. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
  4. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.

Answer(s): A


Reference:



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