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Databricks Associate-Developer-Apache-Spark-3.5 Exam Practice Test To Gain Brilliante Result

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q80-Q85):

NEW QUESTION # 80
46 of 55.
A data engineer is implementing a streaming pipeline with watermarking to handle late-arriving records.
The engineer has written the following code:
inputStream
.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
What happens to data that arrives after the watermark threshold?

A. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
C. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
D. The watermark ensures that late data arriving within 10 minutes of the latest event time will be processed and included in the windowed aggregation.

Answer: C

Explanation:
Watermarking in Structured Streaming defines how late a record can arrive based on event time before Spark discards it.
Behavior:
.withWatermark("event_time", "10 minutes")
This means Spark will keep state for 10 minutes beyond the maximum event time seen so far.
Any data arriving later than 10 minutes after the current watermark is ignored - it will not be included in the aggregation or output.
Why the other options are incorrect:
B: Late data beyond the watermark threshold is not included.
C: Late data is not moved to a new window; it's simply dropped.
D: True for late data within the watermark threshold, not after it.
Reference:
Spark Structured Streaming Guide - withWatermark() behavior and late data handling.
Databricks Exam Guide (June 2025): Section "Structured Streaming" - watermarking and state cleanup behavior.

NEW QUESTION # 81
A developer is trying to join two tables,sales.purchases_fctandsales.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 thepurchases_fcttable that do not exist in thecustomer_dimtable 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('customer_id') == F.col('custid'), 'left')
D. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.

NEW QUESTION # 82
A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A. 0
B. 1
C. 2
D. 3

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
If each of the 10 nodes runs 4 executors, and each executor is assigned 4 CPU cores:
Executors per node = 4
Cores per executor = 4
Total executors = 4 * 10 = 40
Total cores = 40 executors * 4 cores = 160 cores
However, Spark uses 1 core for overhead on each node when managing multiple executors. Thus, the practical allocation is:
Total usable executors = 4 executors/node × 10 nodes = 40
Total cores = 4 cores × 40 executors = 160
Answer is A - but the question asks specifically about "CPU cores used by the application," assuming all
allocated cores are usable (as Spark typically runs executors without internal core reservation unless explicitly configured).
However, if you are considering 4 executors/node × 4 cores = 16 cores per node, across 10 nodes, that's 160.
Final Answer: A

NEW QUESTION # 83
Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A. groupBy without aggregation (invalid use)
B. dropDuplicates with no arguments (removes based on all columns)
C. dropDuplicates on all columns (wrong criteria)
D. dropDuplicates on the exact matching fields

Answer: D

Explanation:
dedup_df = iot_bronze_df.dropDuplicates(["event_ts","sensor_id","metric_value"]) dropDuplicates accepts a list of columns to use for deduplication.
This ensures only unique records based on the specified keys are retained.
Reference:DataFrame.dropDuplicates() API

NEW QUESTION # 84
44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A. query = df.writeStream
.outputMode("append")
.trigger(once=True)
.start()
B. query = df.writeStream
.outputMode("append")
.trigger(continuous="5 seconds")
.start()
C. query = df.writeStream
.outputMode("append")
.trigger(processingTime="5 seconds")
.start()
D. query = df.writeStream
.outputMode("append")
.start()

Answer: C

Explanation:
To process data in fixed micro-batch intervals, use the .trigger(processingTime="interval") option in Structured Streaming.
Correct usage:
query = df.writeStream
.outputMode("append")
.trigger(processingTime="5 seconds")
.start()
This instructs Spark to process available data every 5 seconds.
Why the other options are incorrect:
B: continuous triggers are for continuous processing mode (different execution model).
C: once=True runs the stream a single time (batch mode).
D: Default trigger runs as fast as possible, not fixed intervals.
Reference:
PySpark Structured Streaming Guide - Trigger types: processingTime, once, continuous.
Databricks Exam Guide (June 2025): Section "Structured Streaming" - controlling streaming triggers and batch intervals.

NEW QUESTION # 85
......

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