Databricks Associate-Developer-Apache-Spark-3.5

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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
B) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
C) fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
D) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')

2. 7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

A) 'header' 'true'
B) 'header' 'false'
C) 'sep' '|'
D) 'sep' ','

3. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Stability: isolation of application code and dependencies from each other and the Spark driver
B) Debuggability: the ability to perform interactive debugging directly from the application code
C) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
D) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs

4. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Collecting persistent table statistics and storing them in the metastore for future use
B) Improving the performance of single-stage Spark jobs
C) Dynamically switching join strategies
D) Optimizing the layout of Delta files on disk

5. 33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A) raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
B) raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
C) raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
D) raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))

Solutions: