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【General】 Latest Associate-Developer-Apache-Spark-3.5 Exam Dumps & Associate-Developer

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q125-Q130):NEW QUESTION # 125
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
  • A. By configuring the option recoveryLocation during writeStream
  • B. By configuring the option checkpointLocation during readStream
  • C. By configuring the option checkpointLocation during writeStream
  • D. By configuring the option recoveryLocation during the SparkSession initialization
Answer: C
Explanation:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify the checkpointLocation option during the writeStream operation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify the checkpointLocation option before you run a streaming query, as in the following example:
.option("checkpointLocation", "/path/to/checkpoint/dir")
.toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting the checkpointLocation during writeStream, Spark can maintain state information and ensure exactly-once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 126
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:
  • A. filtered_df = users_raw_df.na.drop(thresh=0)
  • B. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
  • C. filtered_df = users_raw_df.na.drop(how='any')
  • D. filtered_df = users_raw_df.na.drop(how='all')
Answer: C
Explanation:
.na.drop(how='any') drops any row that has at least one null value.
This is exactly what's needed when the goal is to retain only fully complete records.
Usage:CopyEdit
filtered_df = users_raw_df.na.drop(how='any')
Explanation of incorrect options:
A: thresh=0 is invalid - thresh must be ≥ 1.
B: how='all' drops only rows where all columns are null (too lenient).
D: spark.na.drop doesn't support mixing how and thresh in that way; it's incorrect syntax.

NEW QUESTION # 127
42 of 55.
A developer needs to write the output of a complex chain of Spark transformations to a Parquet table called events.liveLatest.
Consumers of this table query it frequently with filters on both year and month of the event_ts column (a timestamp).
The current code:
from pyspark.sql import functions as F
final = df.withColumn("event_year", F.year("event_ts"))
.withColumn("event_month", F.month("event_ts"))
.bucketBy(42, ["event_year", "event_month"])
.saveAsTable("events.liveLatest")
However, consumers report poor query performance.
Which change will enable efficient querying by year and month?
  • A. Replace .bucketBy() with .partitionBy("event_year") only
  • B. Add .sortBy() after .bucketBy()
  • C. Change the bucket count (42) to a lower number
  • D. Replace .bucketBy() with .partitionBy("event_year", "event_month")
Answer: D
Explanation:
When queries frequently filter on certain columns, partitioning by those columns ensures partition pruning, allowing Spark to scan only relevant directories instead of the entire dataset.
Correct code:
final.write.partitionBy("event_year", "event_month").parquet("events.liveLatest") This improves read performance dramatically for filters like:
SELECT * FROM events.liveLatest WHERE event_year = 2024 AND event_month = 5; bucketBy() helps in clustering and joins, not in partition pruning for file-based tables.
Why the other options are incorrect:
B: Bucket count changes parallelism, not query pruning.
C: sortBy organizes data within files, not across partitions.
D: Partitioning by only one column limits pruning benefits.
Reference:
Spark SQL DataFrameWriter - partitionBy() for partitioned tables.
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - partitioning vs. bucketing and query optimization.

NEW QUESTION # 128
1 of 55. A data scientist wants to ingest a directory full of plain text files so that each record in the output DataFrame contains the entire contents of a single file and the full path of the file the text was read from.
The first attempt does read the text files, but each record contains a single line. This code is shown below:
txt_path = "/datasets/raw_txt/*"
df = spark.read.text(txt_path) # one row per line by default
df = df.withColumn("file_path", input_file_name()) # add full path
Which code change can be implemented in a DataFrame that meets the data scientist's requirements?
  • A. Add the option wholetext to the text() function.
  • B. Add the option lineSep=", " to the text() function.
  • C. Add the option lineSep to the text() function.
  • D. Add the option wholetext=False to the text() function.
Answer: A
Explanation:
By default, the spark.read.text() method reads a text file one line per record. This means that each line in a text file becomes one row in the resulting DataFrame.
To read each file as a single record, Apache Spark provides the option wholetext, which, when set to True, causes Spark to treat the entire file contents as one single string per row.
Correct usage:
df = spark.read.option("wholetext", True).text(txt_path)
This way, each record in the DataFrame will contain the full content of one file instead of one line per record.
To also include the file path, the function input_file_name() can be used to create an additional column that stores the complete path of the file being read:
from pyspark.sql.functions import input_file_name
df = spark.read.option("wholetext", True).text(txt_path)
.withColumn("file_path", input_file_name())
This approach satisfies both requirements from the question:
Each record holds the entire contents of a file.
Each record also contains the file path from which the text was read.
Why the other options are incorrect:
B or D (lineSep) - The lineSep option only defines the delimiter between lines. It does not combine the entire file content into a single record.
C (wholetext=False) - This is the default behavior, which still reads one record per line rather than per file.
Reference (Databricks Apache Spark 3.5 - Python / Study Guide):
PySpark API Reference: DataFrameReader.text - describes the wholetext option.
PySpark Functions: input_file_name() - adds a column with the source file path.
Databricks Certified Associate Developer for Apache Spark Exam Guide (June 2025): Section "Using Spark DataFrame APIs" - covers reading files and handling DataFrames.

NEW QUESTION # 129
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?
  • A. The driver node stores the final result after computations are completed by worker nodes
  • 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 only provides the user interface for monitoring the application
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 # 130
......
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