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100% Pass Quiz 2025 Databricks Associate-Developer-Apache-Spark-3.5: Databricks Certified Associate Developer for Apache Spark 3.5 - Python–Efficient Test Lab Questions

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q45-Q50):

NEW QUESTION # 45
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A. saveAsTable with mode Overwrite
B. save with mode Ignore
C. save with mode ErrorIfExists
D. saveAsTable with mode ErrorIfExists

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A

NEW QUESTION # 46
Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A. 0
B. Same number as the cluster executors
C. 1
D. 2

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The.coalesce(numPartitions)function is used to reduce the number of partitions in a DataFrame. It does not increase the number of partitions. If the specified number of partitions is greater than the current number, it will not have any effect.
From the official Spark documentation:
"coalesce() results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim one or more of the current partitions." However, if you try to increase partitions using coalesce (e.g., from 10 to 20), the number of partitions remains unchanged.
Hence,df.coalesce(20)will still return a DataFrame with 10 partitions.
Reference: Apache Spark 3.5 Programming Guide # RDD and DataFrame Operations # coalesce()

NEW QUESTION # 47
A developer runs:

What is the result?
Options:

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

Answer: C

Explanation:
ThepartitionBy()method in Spark organizes output into subdirectories based on unique combinations of the specified columns:
e.g.
/path/to/output/color=red/fruit=apple/part-0000.parquet
/path/to/output/color=green/fruit=banana/part-0001.parquet
This improves query performance via partition pruning.
It does not consolidate into a single file.
Null values are allowed in partitions.
It does not "append" unless.mode("append")is used.
Reference:Spark Write with Partitioning

NEW QUESTION # 48
An engineer wants to join two DataFramesdf1anddf2on the respectiveemployee_idandemp_idcolumns:
df1:employee_id INT,name STRING
df2:emp_id INT,department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?

A. The code fails to execute because the column names employee_id and emp_id do not match automatically
B. The code fails to execute because PySpark does not support joining DataFrames with a different structure
C. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2
D. The code fails to execute because it must use on='employee_id' to specify the join column explicitly

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In PySpark, when performing a join between two DataFrames, the columns do not have to share the same name. You can explicitly provide a join condition by comparing specific columns from each DataFrame.
This syntax is correct and fully supported:
df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
This will perform an inner join betweendf1anddf2using theemployee_idfromdf1andemp_idfromdf2.
Reference: Databricks Spark 3.5 Documentation # DataFrame API # join()

NEW QUESTION # 49
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(how='all')
B. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
C. filtered_df = users_raw_df.na.drop(how='any')
D. filtered_df = users_raw_df.na.drop(thresh=0)

Answer: C

Explanation:
na.drop(how='any')drops any row that has at least one null value.
This is exactly what's needed when the goal is to retain only fully complete records.
Usage:CopyEdit
filtered_df = users_raw_df.na.drop(how='any')
Explanation of incorrect options:
A: thresh=0 is invalid - thresh must be # 1.
B: how='all' drops only rows where all columns are null (too lenient).
D: spark.na.drop doesn't support mixing how and thresh in that way; it's incorrect syntax.
Reference:PySpark DataFrameNaFunctions.drop()

NEW QUESTION # 50
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