EXAM ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 VOUCHER - ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 EXAM SAMPLE

Exam Associate-Developer-Apache-Spark-3.5 Voucher - Associate-Developer-Apache-Spark-3.5 Exam Sample

Exam Associate-Developer-Apache-Spark-3.5 Voucher - Associate-Developer-Apache-Spark-3.5 Exam Sample

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q48-Q53):

NEW QUESTION # 48
A data analyst wants to add a column date derived from a timestamp column.
Options:

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

Answer: D

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 # 49
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(path="/some/path").saveAsTable("default_table")
  • B. users.write.saveAsTable("default_table").option("path", "/some/path")
  • C. users.write.option("path", "/some/path").saveAsTable("default_table")
  • D. users.write.saveAsTable("default_table", path="/some/path")

Answer: C

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 # 50
A data engineer writes the following code to join two DataFramesdf1anddf2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

  • A. Broadcast join, as df2 is smaller than the default broadcast threshold
  • B. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
  • C. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
  • D. Shuffle join because no broadcast hints were provided

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default broadcast join threshold in Spark is:
spark.sql.autoBroadcastJoinThreshold = 10MB
Sincedf2is only 8 MB (less than 10 MB), Spark will automatically apply a broadcast join without requiring explicit hints.
From the Spark documentation:
"If one side of the join is smaller than the broadcast threshold, Spark will automatically broadcast it to all executors." A is incorrect because Spark does support auto broadcast even with static plans.
B is correct: Spark will automatically broadcast df2.
C and D are incorrect because Spark's default logic handles this optimization.
Final Answer: B


NEW QUESTION # 51
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

  • A. Broadcast a variable to share the maximum time among workers
  • B. Use an RDD action like reduce() to compute the maximum time
  • C. Use an accumulator to record the maximum time on the driver
  • D. Configure the Spark UI to automatically collect maximum times

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such asreduce()oraggregate().
From the documentation:
"To perform global aggregations on distributed data, actions likereduce()are commonly used to collect summaries such as min/max/avg." Accumulators (Option B) do not support max operations directly and are not intended for such analytics.
Broadcast (Option C) is used to send data to workers, not collect from them.
Spark UI (Option D) is a monitoring tool - not an analytics collection interface.
Final Answer: A


NEW QUESTION # 52
Which command overwrites an existing JSON file when writing a DataFrame?

  • A. df.write.mode("overwrite").json("path/to/file")
  • B. df.write.format("json").save("path/to/file", mode="overwrite")
  • C. df.write.overwrite.json("path/to/file")
  • D. df.write.json("path/to/file", overwrite=True)

Answer: A

Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
Reference:PySpark DataFrameWriter API


NEW QUESTION # 53
......

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