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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

2. Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A) spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
B) spark.conf.set("spark.pandas.arrow.enabled", "true")
C) spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
D) spark.conf.set("spark.sql.execution.arrow.enabled", "true")

3. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
B) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
C) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
D) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")

4. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write.option("path", "/some/path").saveAsTable("default_table")
B) users.write.saveAsTable("default_table").option("path", "/some/path")
C) users.write.saveAsTable("default_table", path="/some/path")
D) users.write(path="/some/path").saveAsTable("default_table")

5. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.mode("overwrite").json("path/to/file")
B) df.write.json("path/to/file", overwrite=True)
C) df.write.overwrite.json("path/to/file")
D) df.write.format("json").save("path/to/file", mode="overwrite")

Solutions: