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

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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. 27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?

A) df = au_df.union(nz_df, allowMissingColumns=True)
B) df = au_df.unionByName(nz_df, allowMissingColumns=False)
C) df = au_df.unionAll(nz_df)
D) df = au_df.unionByName(nz_df, allowMissingColumns=True)

2. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.task.maxFailures
B) spark.sql.shuffle.partitions
C) spark.executor.memory
D) spark.executor.cores

3. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
B) spark.read.orc("/file/test_data.orc").selected("col1", "col2")
C) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
D) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")

4. A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

5. 1 of 55. A data scientist wants to ingest a directory full of plain text files so that each record in the output DataFrame contains the entire contents of a single file and the full path of the file the text was read from.
The first attempt does read the text files, but each record contains a single line. This code is shown below:
txt_path = "/datasets/raw_txt/*"
df = spark.read.text(txt_path) # one row per line by default
df = df.withColumn("file_path", input_file_name()) # add full path
Which code change can be implemented in a DataFrame that meets the data scientist's requirements?

A) Add the option wholetext to the text() function.
B) Add the option wholetext=False to the text() function.
C) Add the option lineSep=", " to the text() function.
D) Add the option lineSep to the text() function.

Solutions: