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

1. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
B) Uses trigger() - default micro-batch trigger without interval.
C) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
D) Uses trigger(continuous='5 seconds') - continuous processing mode.

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

3. 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 = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
C) fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
D) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))

4. A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
B) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
C) df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
D) df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")

5. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A) A stage contains multiple tasks, and each task contains multiple jobs.
B) A stage contains multiple jobs, and each job contains multiple tasks.
C) A job contains multiple stages, and each stage contains multiple tasks.
D) A job contains multiple tasks, and each task contains multiple stages.

Solutions: