Databricks Associate-Developer-Apache-Spark-3.5

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A) streaming_df.filter (col("count") < 30).show()
B) streaming_df.orderBy("timestamp").limit(4)
C) streaming_df.groupby("Id") .count ()
D) streaming_df. select (countDistinct ("Name") )

2. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) Support for SQL queries on structured data
B) Ability to process small datasets efficiently
C) In-memory computation and parallel processing capabilities
D) Built-in machine learning libraries

3. 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')
C) filtered_df = users_raw_df.na.drop(how='any')
D) filtered_df = users_raw_df.na.drop(how='all', thresh=None)

4. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) COMPLETE
B) REPLACE
C) APPEND
D) AGGREGATE

5. 14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A) It stores all data in a single Parquet file.
B) It throws an error if there are null values in either partition column.
C) It appends new partitions to an existing Parquet file.
D) It creates separate directories for each unique combination of color and taste.

Solutions: