Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Exam Preparation

Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Questions & Answers

• Question 1:

  A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.

  If task A fails during a scheduled run, which statement describes the results of this run?

  A. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
  B. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
  C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
  D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
  E. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
  D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

• Question 2:

  What statement is true regarding the retention of job run history?

  A. It is retained until you export or delete job run logs
  B. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
  C. t is retained for 60 days, during which you can export notebook run results to HTML
  D. It is retained for 60 days, after which logs are archived
  E. It is retained for 90 days or until the run-id is re-used through custom run configuration
  C. t is retained for 60 days, during which you can export notebook run results to HTML

• Question 3:

  To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.

  The data engineering team has been made aware of new requirements from a customer- facing application, which is the only downstream workload they manage entirely. As a result, an aggregate tableused by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.

  Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

  A. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
  B. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
  C. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
  D. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
  E. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer- facing application.
  B. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.

• Question 4:

  A junior data engineer on your team has implemented the following code block.

  

  The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table. Theevent_idfield serves as a unique key for this table.

  When this query is executed, what will happen with new records that have the sameevent_idas an existing record?

  A. They are merged.
  B. They are ignored.
  C. They are updated.
  D. They are inserted.
  E. They are deleted.
  B. They are ignored.

• Question 5:

  A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:

  SELECT COUNT (*) FROM table Which of the following describes how results are generated each time the dashboard is updated?

  A. The total count of rows is calculated by scanning all data files
  B. The total count of rows will be returned from cached results unless REFRESH is run
  C. The total count of records is calculated from the Delta transaction logs
  D. The total count of records is calculated from the parquet file metadata
  E. The total count of records is calculated from the Hive metastore
  C. The total count of records is calculated from the Delta transaction logs

• Question 6:

  The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-todate, and quarter-to-date. This table is namedstore_saies_summaryand the schema is as follows:

  

  The tabledaily_store_salescontains all the information needed to update store_sales_summary. The schema for this table is:

  store_id INT, sales_date DATE, total_sales FLOAT

  Ifdaily_store_salesis implemented as a Type 1 table and thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summarytable?

  A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
  B. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
  C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
  D. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
  E. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.
  E. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

• Question 7:

  A Delta table of weather records is partitioned by date and has the below schema:

  date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT

  To find all the records from within the Arctic Circle, you execute a query with the below filter:

  latitude > 66.3

  Which statement describes how the Delta engine identifies which files to load?

  A. All records are cached to an operational database and then the filter is applied
  B. The Parquet file footers are scanned for min and max statistics for the latitude column
  C. All records are cached to attached storage and then the filter is applied
  D. The Delta log is scanned for min and max statistics for the latitude column
  E. The Hive metastore is scanned for min and max statistics for the latitude column
  D. The Delta log is scanned for min and max statistics for the latitude column

• Question 8:

  The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.

  After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

  

  Which statement describes what will happen when the above code is executed?

  A. The connection to the external table will fail; the string "redacted" will be printed.
  B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
  C. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
  D. The connection to the external table will succeed; the string value of password will be printed in plain text.
  E. The connection to the external table will succeed; the string "redacted" will be printed.
  E. The connection to the external table will succeed; the string "redacted" will be printed.

• Question 9:

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

• Question 10:

  The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:

  item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING

  The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.

  A junior data engineer suggests converting this data to Delta Lake will improve query performance.

  Which response to the junior data engineer s suggestion is correct?

  A. Delta Lake statistics are not optimized for free text fields with high cardinality.
  B. Text data cannot be stored with Delta Lake.
  C. ZORDER ON review will need to be run to see performance gains.
  D. The Delta log creates a term matrix for free text fields to support selective filtering.
  E. Delta Lake statistics are only collected on the first 4 columns in a table.
  A. Delta Lake statistics are not optimized for free text fields with high cardinality.