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Brain Dump Databricks-Certified-Professional-Data-Engineer Free - Practice Databricks-Certified-Professional-Data-Engineer Exam Pdf

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

NEW QUESTION # 36
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 wrap the query using PySpark and use Python's string variable system to automatically
    update the table name
  • B. They could manually replace the date within the table name with the current day's date
  • C. They could pass the table into PySpark and develop a robustly tested module on the existing query
  • D. They could replace the string-formatted date in the table with a timestamp-formatted date
  • E. They could request that the data analyst rewrites the query to be run less frequently

Answer: A


NEW QUESTION # 37
You are working on a marketing team request to identify customers with the same information between two tables CUSTOMERS_2021 and CUSTOMERS_2020 each table contains 25 columns with the same schema, You are looking to identify rows that match between two tables across all columns, which of the following can be used to perform in SQL

  • A. 1.SELECT * FROM CUSTOMERS_2021
    2. UNION ALL
    3.SELECT * FROM CUSTOMERS_2020
  • B. 1.SELECT * FROM CUSTOMERS_2021
    2. UNION
    3.SELECT * FROM CUSTOMERS_2020
  • C. 1.SELECT * FROM CUSTOMERS_2021 C1
    2.INNER JOIN CUSTOMERS_2020 C2
    3.ON C1.CUSTOMER_ID = C2.CUSTOMER_ID
  • D. 1.SELECT * FROM CUSTOMERS_2021
    2. INTERSECT
    3.SELECT * FROM CUSTOMERS_2020
  • E. 1.SELECT * FROM CUSTOMERS_2021
    2.EXCEPT
    3.SELECT * FROM CUSTOMERS_2020

Answer: D

Explanation:
Explanation
Answer is,
1.SELECT * FROM CUSTOMERS_2021
2. INTERSECT
3.SELECT * FROM CUSTOMERS_2020
To compare all the rows between both the tables across all the columns using intersect will help us achieve that, an inner join is only going to check if the same column value exists across both the tables on a single column.
INTERSECT [ALL | DISTINCT]
*Returns the set of rows which are in both subqueries.
If ALL is specified a row that appears multiple times in the subquery1 as well as in subquery will be returned multiple times.
If DISTINCT is specified the result does not contain duplicate rows. This is the default.


NEW QUESTION # 38
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. 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.
  • B. The connection to the external table will succeed; the string "redacted" will be printed.
  • C. The connection to the external table will fail; the string "redacted" will be printed.
  • D. 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.
  • E. The connection to the external table will succeed; the string value of password will be printed in plain text.

Answer: B

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under
"Secrets" section.


NEW QUESTION # 39
Review the following error traceback:
Which statement describes the error being raised?

  • A. The code executed was PvSoark but was executed in a Scala notebook.
  • B. There is a type error because a column object cannot be multiplied.
  • C. There is a type error because a DataFrame object cannot be multiplied.
  • D. There is a syntax error because the heartrate column is not correctly identified as a column.
  • E. There is no column in the table named heartrateheartrateheartrate

Answer: D

Explanation:
The error being raised is an AnalysisException, which is a type of exception that occurs when Spark SQL cannot analyze or execute a query due to some logical or semantic error1. In this case, the error message indicates that the query cannot resolve the column name 'heartrateheartrateheartrate' given the input columns
'heartrate' and 'age'. This means that there is no column in the table named 'heartrateheartrateheartrate', and the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the query. To fix this error, the query should use a valid column name that exists in the table, such as
'heartrate'. References: AnalysisException


NEW QUESTION # 40
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

  • A. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
  • B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
  • C. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
  • D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Answer: C

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context. In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.
Reference: Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).


NEW QUESTION # 41
......

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