Databricks Databricks Certified Professional Data Engineer Databricks Certified Data Engineer Professional Exam Online Training
Databricks Databricks Certified Professional Data Engineer Online Training
The questions for Databricks Certified Professional Data Engineer were last updated at Feb 13,2025.
- Exam Code: Databricks Certified Professional Data Engineer
- Exam Name: Databricks Certified Data Engineer Professional Exam
- Certification Provider: Databricks
- Latest update: Feb 13,2025
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake’s time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?
- A . Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
- B . Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
- C . Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
- D . Because Delta Lake’s delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
- E . Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
- A . Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
- B . The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
- C . Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
- D . One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
- E . The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
- A . Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
- B . The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.
- C . Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
- D . One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
- E . The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
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 auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. 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?
- A . Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
- B . Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
- C . Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake’s versioning ability to create an audit log.
- D . 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.
- E . Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
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.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?
- A . Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
- B . 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.
- C . Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent
value for each user id. - D . Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
- E . 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.
A 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?
- A . 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.
- B . Convert 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.
- C . Calculate the difference between the previous model predictions and the current
customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions. - D . 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. - E . 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.
A table is registered with the following code:
Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?
- A . 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.
- B . All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
- C . Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
- D . 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.
- E . The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
- A . Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
- B . Z-order indices calculated on the table are preventing file compaction
- C . Bloom filler indices calculated on the table are preventing file compaction
- D . Databricks has autotuned to a smaller target file size based on the overall size of data in the table
- E . Databricks has autotuned to a smaller target file size based on the amount of data in each partition
Which statement regarding stream-static joins and static Delta tables is correct?
- A . Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
- B . Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job’s initialization.
- C . The checkpoint directory will be used to track state information for the unique keys present in the join.
- D . Stream-static joins cannot use static Delta tables because of consistency issues.
- E . The checkpoint directory will be used to track updates to the static Delta table.
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A . to_interval("event_time", "5 minutes").alias("time")
- B . window("event_time", "5 minutes").alias("time")
- C . "event_time"
- D . window("event_time", "10 minutes").alias("time")
- E . lag("event_time", "10 minutes").alias("time")
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