DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM STUDY SOLUTIONS, VALID ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 TEST BOOK

Databricks Associate-Developer-Apache-Spark-3.5 Exam Study Solutions, Valid Associate-Developer-Apache-Spark-3.5 Test Book

Databricks Associate-Developer-Apache-Spark-3.5 Exam Study Solutions, Valid Associate-Developer-Apache-Spark-3.5 Test Book

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q50-Q55):

NEW QUESTION # 50
In the code block below,aggDFcontains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

  • A. aggregate
  • B. replace
  • C. complete
  • D. append

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct output mode for streaming aggregations that need to output the full updated results at each trigger is"complete".
From the official documentation:
"complete: The entire updated result table will be output to the sink every time there is a trigger." This is ideal for aggregations, such as counts or averages grouped by a key, where the result table changes incrementally over time.
append: only outputs newly added rows
replace and aggregate: invalid values for output mode
Reference: Spark Structured Streaming Programming Guide # Output Modes


NEW QUESTION # 51
A data engineer is working on the DataFrame:

(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?

  • A. df.select("Name").distinct().orderBy(df["Name"].desc())
  • B. df.select("Name").distinct()
  • C. df.select("Name").distinct().orderBy(df["Name"])
  • D. df.select("Name").orderBy(df["Name"].asc())

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To extract unique values from a column and sort them alphabetically:
distinct()is required to remove duplicate values.
orderBy()is needed to sort the results alphabetically (ascending by default).
Correct code:
df.select("Name").distinct().orderBy(df["Name"])
This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting.
Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.


NEW QUESTION # 52
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

  • A. users.write.saveAsTable("default_table").option("path", "/some/path")
  • B. users.write.saveAsTable("default_table", path="/some/path")
  • C. users.write(path="/some/path").saveAsTable("default_table")
  • D. users.write.option("path", "/some/path").saveAsTable("default_table")

Answer: D

Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table


NEW QUESTION # 53
A data analyst wants to add a column date derived from a timestamp column.
Options:

  • A. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
  • B. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
  • C. dates_df.withColumn("date", f.to_date("timestamp")).show()
  • D. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()

Answer: C

Explanation:
f.to_date() converts a timestamp or string to a DateType.
Ideal for extracting the date component (year-month-day) from a full timestamp.
Example:
frompyspark.sql.functionsimportto_date
dates_df.withColumn("date", to_date("timestamp"))
Reference:Spark SQL Date Functions


NEW QUESTION # 54
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

  • A. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
  • B. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
  • C. Uses trigger() - default micro-batch trigger without interval.
  • D. Uses trigger(continuous='5 seconds') - continuous processing mode.

Answer: B

Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
outputMode("append")
trigger(processingTime='5 seconds')
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers


NEW QUESTION # 55
......

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