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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 2: Structured Streaming | 10% | - Streaming Applications
|
| Topic 3: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 4: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 5: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 6: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 7: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?
- A. It accepts watermarks in seconds and the code results in an error
- B. It removes duplicates that arrive within the 30-minute window specified by the watermark
- C. It removes all duplicates regardless of when they arrive
- D. It is not able to handle deduplication in this scenario
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?
- A. spark.conf.set("spark.pandas.arrow.enabled", "true")
- B. spark.conf.set("spark.sql.execution.arrow.enabled", "true")
- C. spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
- D. spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
Correct Answer: C 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?
- A. customerDF = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1)) - B. customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
- C. customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
- D. customerDF = customerDF.select("email").alias("username", "domain")
Correct Answer: A 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
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 the exact matching fields
- D. dropDuplicates on all columns (wrong criteria)
Correct Answer: C 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?
- A. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159 - B. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159 - C. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159 - D. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
Correct Answer: A 🗳️
Explanation: Only visible for VCE4Dumps members. You can sign-up / login (it's free).
