RECOMMENDED DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM QUESTIONS TO PASS IN FIRST TRY

Recommended Databricks-Certified-Professional-Data-Engineer Exam Questions To Pass In First Try

Recommended Databricks-Certified-Professional-Data-Engineer Exam Questions To Pass In First Try

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>> Latest Databricks-Certified-Professional-Data-Engineer Questions <<

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
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. Theuser_idfield 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 namedaccount_historywhich maintains a full record of all data in the same schema as the source. The next table in the system is namedaccount_currentand is implemented as a Type 1 table representing the most recent value for each uniqueuser_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 describedaccount_currenttable 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. 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.
  • 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. 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.
  • E. 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.

Answer: C

Explanation:
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.


NEW QUESTION # 64
A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

  • A. The tables should be refreshed in the writing cluster before the next query is run
  • B. The tables should be stored in a cloud-based external system
  • C. The tables should be updated before the next query is run
  • D. The tables should be altered to include metadata to not cache
  • E. The tables should be converted to the Delta format

Answer: E


NEW QUESTION # 65
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in thegeo_lookuptable.
Before executing the code, runningSHOWTABLESon the current database indicates the database contains only two tables:geo_lookupandsales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

  • A. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
  • B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
  • C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
  • D. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af:
    if this entity exists, Cmd 2 will succeed.
  • E. Both commands will fail. No new variables, tables, or views will be created.

Answer: B

Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under
"Language Interoperability" section; Databricks Documentation, under "Mix languages" section.


NEW QUESTION # 66
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 records is calculated from the Delta transaction logs
  • 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 Hive metastore
  • D. The total count of rows is calculated by scanning all data files
  • E. The total count of records is calculated from the parquet file metadata

Answer: A

Explanation:
https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%
20number,a%20given%20Delta%20table%20version.


NEW QUESTION # 67
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

  • A. Stage's detail screen and Executor's files
  • B. Stage's detail screen and Query's detail screen
  • C. Driver's and Executor's log files
  • D. Executor's detail screen and Executor's log files

Answer: B

Explanation:
In Apache Spark's UI, indicators of data spilling to disk during the execution of wide transformations can be found in the Stage's detail screen and the Query's detail screen. These screens provide detailed metrics about each stage of a Spark job, including information about memory usage and spill data. If a task is spilling data to disk, it indicates that the data being processed exceeds the available memory, causing Spark to spill data to disk to free up memory. This is an important performance metric as excessive spill can significantly slow down the processing.
References:
* Apache Spark Monitoring and Instrumentation: Spark Monitoring Guide
* Spark UI Explained: Spark UI Documentation


NEW QUESTION # 68
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