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

1. 25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A) employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")
B) employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
C) employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)
D) employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")

2. Which UDF implementation calculates the length of strings in a Spark DataFrame?

A) df.withColumn("length", udf(lambda s: len(s), StringType()))
B) df.withColumn("length", spark.udf("len", StringType()))
C) spark.udf.register("stringLength", lambda s: len(s))
D) df.select(length(col("stringColumn")).alias("length"))

3. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A) Shuffle join because no broadcast hints were provided.
B) Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
C) Broadcast join, as df2 is smaller than the default broadcast threshold.
D) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.

4. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

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

5. 17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?

A) Collecting persistent table statistics and storing them in the metastore for future use
B) Optimizing the layout of Delta files on disk
C) Dynamically switching join strategies
D) Improving the performance of single-stage Spark jobs

Solutions: