LATEST DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM MATERIALS｜100% PASS｜REAL QUESTIONS

Latest Databricks-Certified-Professional-Data-Engineer Exam Materials｜100% Pass｜Real Questions

Latest Databricks-Certified-Professional-Data-Engineer Exam Materials｜100% Pass｜Real Questions

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Databricks Certified Professional Data Engineer certification exam is suitable for data engineers, data architects, and data scientists who are responsible for building and managing data pipelines and workflows. Databricks-Certified-Professional-Data-Engineer exam is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows using Databricks. Candidates must have a solid understanding of data engineering concepts such as data modeling, data integration, data transformation, and data storage.

Databricks Certified Professional Data Engineer certification exam is a challenging exam that requires candidates to demonstrate their understanding of Databricks and data engineering concepts. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions, and candidates have three hours to complete the exam. Databricks-Certified-Professional-Data-Engineer exam covers various topics, including data modeling, data warehousing, data governance, and working with Databricks clusters. To pass the exam, candidates must achieve a minimum passing score of 70%.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
A table is registered with the following code:

Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_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 began.
  • B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
  • C. 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.
  • D. 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.
  • E. All logic will execute when the table is definedand store the result of joiningtables to the DBFS; this stored data will be returned when the table is queried.

Answer: A

Explanation:
Explanation
This is the correct answer because Delta Lake supports time travel, which allows users to query data as of a specific version or timestamp. The code uses the VERSION AS OF syntax to specify the version of each source table to be used in the join. The result of querying recent_orders will be the same as joining those versions of the source tables at query time. The query will use snapshot isolation, which means it will use a consistent snapshot of the table at the time the query began, regardless of any concurrent updates or deletes.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Query an older snapshot of a table (time travel)" section.


NEW QUESTION # 37
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 the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

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

  • A. 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.
  • B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
  • C. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
  • D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
  • E. Both commands will fail. No new variables, tables, or views will be created.

Answer: D

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 Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.


NEW QUESTION # 38
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of the Job's schedule and configuration?

  • A. They can download the JSON equivalent of the job from the Job's page.
  • B. They can submit the Job once on a Job cluster.
  • C. They can link the Job to notebooks that are a part of a Databricks Repo.
  • D. They can submit the Job once on an all-purpose cluster.
  • E. They can download the XML description of the Job from the Job's page

Answer: D


NEW QUESTION # 39
The data engineering team is using a bunch of SQL queries to review data quality and monitor the ETL job every day, which of the following approaches can be used to set up a schedule and auto-mate this process?

  • A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
  • B. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
  • C. They can schedule the query to run every 12 hours from the Jobs UI.
  • D. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
  • E. They can schedule the query to run every 1 day from the Jobs UI

Answer: D

Explanation:
Explanation
Explanation
Individual queries can be refreshed on a schedule basis,
To set the schedule:
1. Click the query info tab.
Graphical user interface, text, application, email Description automatically generated

* Click the link to the right of Refresh Schedule to open a picker with schedule intervals.
Graphical user interface, application Description automatically generated

* Set the schedule.
The picker scrolls and allows you to choose:
* An interval: 1-30 minutes, 1-12 hours, 1 or 30 days, 1 or 2 weeks
* A time. The time selector displays in the picker only when the interval is greater than 1 day and the day selection is greater than 1 week. When you schedule a specific time, Databricks SQL takes input in your computer's timezone and converts it to UTC. If you want a query to run at a certain time in UTC, you must adjust the picker by your local offset. For example, if you want a query to execute at 00:00 UTC each day, but your current timezone is PDT (UTC-7), you should select 17:00 in the picker:
Graphical user interface Description automatically generated

* Click OK.
Your query will run automatically.
If you experience a scheduled query not executing according to its schedule, you should manually trigger the query to make sure it doesn't fail. However, you should be aware of the following:
* If you schedule an interval-for example, "every 15 minutes"-the interval is calculated from the last successful execution. If you manually execute a query, the scheduled query will not be executed until the interval has passed.
* If you schedule a time, Databricks SQL waits for the results to be "outdated". For example, if you have a query set to refresh every Thursday and you manually execute it on Wednesday, by Thursday the results will still be considered "valid", so the query wouldn't be scheduled for a new execution. Thus, for example, when setting a weekly schedule, check the last query execution time and expect the scheduled query to be executed on the selected day after that execution is a week old. Make sure not to manually execute the query during this time.
If a query execution fails, Databricks SQL retries with a back-off algorithm. The more failures the further away the next retry will be (and it might be beyond the refresh interval).
Refer documentation for additional info,
https://docs.microsoft.com/en-us/azure/databricks/sql/user/queries/schedule-query


NEW QUESTION # 40
A data analyst has provided a data engineering team with the following Spark SQL query:
1.SELECT district,
2.avg(sales)
3.FROM store_sales_20220101
4.GROUP BY district;
The data analyst would like the data engineering team to run this query every day. The date at the end of the
table name (20220101) should automatically be replaced with the current date each time the query is run.
Which of the following approaches could be used by the data engineering team to efficiently auto-mate this
process?

  • A. They could manually replace the date within the table name with the current day's date
  • B. They could wrap the query using PySpark and use Python's string variable system to automatically
    update the table name
  • C. They could request that the data analyst rewrites the query to be run less frequently
  • D. They could replace the string-formatted date in the table with a timestamp-formatted date
  • E. They could pass the table into PySpark and develop a robustly tested module on the existing query

Answer: B


NEW QUESTION # 41
......

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