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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q39-Q44):

NEW QUESTION # 39
A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

  • A. query = streaming_df.writeStream
    .format("console")
    .option("checkpoint", "/path/to/checkpoint")
    .outputMode("append")
    .start()
  • B. query = streaming_df.writeStream
    .format("console")
    .outputMode("append")
    .start()
  • C. query = streaming_df.writeStream
    .format("console")
    .outputMode("complete")
    .start()
  • D. query = streaming_df.writeStream
    .format("console")
    .outputMode("append")
    .option("checkpointLocation", "/path/to/checkpoint")
    .start()

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable fault tolerance and ensure that Spark can resume from the last committed offset after failure, you must configure a checkpoint location using the correct option key:"checkpointLocation".
From the official Spark Structured Streaming guide:
"To make a streaming query fault-tolerant and recoverable, a checkpoint directory must be specified using.
option("checkpointLocation", "/path/to/dir")."
Explanation of options:
Option A uses an invalid option name:"checkpoint"(should be"checkpointLocation") Option B is correct: it setscheckpointLocationproperly Option C lacks checkpointing and won't resume after failure Option D also lacks checkpointing configuration Reference: Apache Spark 3.5 Documentation # Structured Streaming # Fault Tolerance Semantics


NEW QUESTION # 40
Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

  • A. 0
  • B. Same number as the cluster executors
  • C. 1
  • D. 2

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The.coalesce(numPartitions)function is used to reduce the number of partitions in a DataFrame. It does not increase the number of partitions. If the specified number of partitions is greater than the current number, it will not have any effect.
From the official Spark documentation:
"coalesce() results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim one or more of the current partitions." However, if you try to increase partitions using coalesce (e.g., from 10 to 20), the number of partitions remains unchanged.
Hence,df.coalesce(20)will still return a DataFrame with 10 partitions.
Reference: Apache Spark 3.5 Programming Guide # RDD and DataFrame Operations # coalesce()


NEW QUESTION # 41
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)

B)

C)

D)

  • A. exploded_df = exploded_df.select("record_datetime", "record_exploded")
  • B. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
  • C. exploded_df = exploded_df.select(
    "record_datetime",
    "record_exploded.sensor_id",
    "record_exploded.status",
    "record_exploded.health"
    )
    exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
  • D. exploded_df = exploded_df.select(
    "record_datetime",
    "record_exploded.sensor_id",
    "record_exploded.status",
    "record_exploded.health"
    )
    exploded_df = sensor_df.withColumn("record_exploded", explode("record"))

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C


NEW QUESTION # 42
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

  • A. The driver node only provides the user interface for monitoring the application
  • B. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
  • C. The driver node holds the DataFrame data and performs all computations locally
  • D. The driver node stores the final result after computations are completed by worker nodes

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In the Spark architecture, the driver node is responsible for orchestrating the execution of a Spark application.
It converts user-defined transformations and actions into a logical plan, optimizes it into a physical plan, and then splits the plan into tasks that are distributed to the executor nodes.
As per Databricks and Spark documentation:
"The driver node is responsible for maintaining information about the Spark application, responding to a user's program or input, and analyzing, distributing, and scheduling work across the executors." This means:
Option A is correct because the driver schedules and coordinates the job execution.
Option B is incorrect because the driver does more than just UI monitoring.
Option C is incorrect since data and computations are distributed across executor nodes.
Option D is incorrect; results are returned to the driver but not stored long-term by it.
Reference: Databricks Certified Developer Spark 3.5 Documentation # Spark Architecture # Driver vs Executors.


NEW QUESTION # 43
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

  • A.
  • B.
  • C.
  • D.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isDbecause it uses proper time-based window aggregation along with watermarking, which is the required pattern in Spark Structured Streaming for time-based aggregations over event-time data.
From the Spark 3.5 documentation on structured streaming:
"You can define sliding windows on event-time columns, and usegroupByalong withwindow()to compute aggregates over those windows. To deal with late data, you usewithWatermark()to specify how late data is allowed to arrive." (Source:Structured Streaming Programming Guide) In optionD, the use of:
python
CopyEdit
groupBy("sensor_id", window("timestamp","5 minutes"))
agg(avg("temperature").alias("avg_temp"))
ensures that for eachsensor_id, the average temperature is calculated over 5-minute event-time windows. To complete the logic, it is assumed thatwithWatermark("timestamp", "5 minutes")is used earlier in the pipeline to handle late events.
Explanation of why other options are incorrect:
Option AusesWindow.partitionBywhich applies to static DataFrames or batch queries and is not suitable for streaming aggregations.
Option Bdoes not apply a time window, thus does not compute the rolling average over 5 minutes.
Option Cincorrectly applieswithWatermark()after an aggregation and does not include any time window, thus missing the time-based grouping required.
Therefore,Option Dis the only one that meets all requirements for computing a time-windowed streaming aggregation.


NEW QUESTION # 44
......

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