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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q50-Q55):NEW QUESTION # 50
A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?
  • A. df = spark.read.parquet("/path/events/data/*")
  • B. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
  • C. df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
  • D. df = spark.read.parquet("/path/events/data/")
Answer: B
Explanation:
To read all files recursively within a nested directory structure, Spark requires the recursiveFileLookup option to be explicitly enabled. According to Databricks official documentation, when dealing with deeply nested Parquet files in a directory tree (as shown in this example), you should set:
df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/") This ensures that Spark searches through all subdirectories under /path/events/data/ and reads any Parquet files it finds, regardless of the folder depth.
Option A is incorrect because while it includes an option, inferSchema is irrelevant here and does not enable recursive file reading.
Option C is incorrect because wildcards may not reliably match deep nested structures beyond one directory level.
Option D is incorrect because it will only read files directly within /path/events/data/ and not subdirectories like /2023/01/01.
Databricks documentation reference:
"To read files recursively from nested folders, set the recursiveFileLookup option to true. This is useful when data is organized in hierarchical folder structures" - Databricks documentation on Parquet files ingestion and options.

NEW QUESTION # 51
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region-> region id containing the smallest 3 region_idvalues.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping ofregion -> region_idfor the smallest
3region_idvalues.
Which code fragment meets the requirements?
  • A. regions = dict(
    regions_df
    .select('region_id', 'region')
    .limit(3)
    .collect()
    )
  • B. regions = dict(
    regions_df
    .select('region', 'region_id')
    .sort(desc('region_id'))
    .take(3)
    )
  • C. regions = dict(
    regions_df
    .select('region', 'region_id')
    .sort('region_id')
    .take(3)
    )
  • D. regions = dict(
    regions_df
    .select('region_id', 'region')
    .sort('region_id')
    .take(3)
    )
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The question requires creating a dictionary where keys areregionvalues and values are the correspondingregion_idintegers. Furthermore, it asks to retrieve only the smallest 3region_idvalues.
Key observations:
select('region', 'region_id')puts the column order as expected bydict()- where the first column becomes the key and the second the value.
sort('region_id')ensures sorting in ascending order so the smallest IDs are first.
take(3)retrieves exactly 3 rows.
Wrapping the result indict(...)correctly builds the required Python dictionary:{ 'AFRICA': 0, 'AMERICA': 1,
'ASIA': 2 }.
Incorrect options:
Option B flips the order toregion_idfirst, resulting in a dictionary with integer keys - not what's asked.
Option C uses.limit(3)without sorting, which leads to non-deterministic rows based on partition layout.
Option D sorts in descending order, giving the largest rather than smallestregion_ids.
Hence, Option A meets all the requirements precisely.

NEW QUESTION # 52
The following code fragment results in an error:

Which code fragment should be used instead?
  • A.
  • B.
  • C.
  • D.
Answer: D

NEW QUESTION # 53
A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing agroupByoperation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

D)

  • A. Use theapplyInPandasAPI:
    df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
  • B. Use a regular Spark UDF:
    from pyspark.sql.functions import mean
    df.groupBy("user_id").agg(mean("value")).show()
  • C. Use a Pandas UDF:
    @pandas_udf("double")
    def mean_func(value: pd.Series) -> float:
    return value.mean()
    df.groupby("user_id").agg(mean_func(df["value"])).show()
  • D. Use themapInPandasAPI:
    df.mapInPandas(mean_func, schema="user_id long, value double").show()
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct approach to perform a parallelizedgroupByoperation across Spark worker nodes using Pandas API is viaapplyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.
As per the Databricks documentation:
"applyInPandas()allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution." Option A is correct and achieves this parallel execution.
Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.
Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.
Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.
Therefore, to run agroupBywith parallel Pandas logic on Spark workers, Option A usingapplyInPandasis the only correct answer.
Reference: Apache Spark 3.5 Documentation # Pandas API on Spark # Grouped Map Pandas UDFs (applyInPandas)

NEW QUESTION # 54
What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?
  • A. An error message of an invalid parameter will be returned
  • B. The same start date will be returned
  • C. The number of days specified will be added to the start date
  • D. The number of days specified will be removed from the start date
Answer: C
Explanation:
The function date_sub(start, days) subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.
Example:
SELECT date_sub('2024-05-01', -5)
-- Returns: 2024-05-06
So, a negative value effectively adds the absolute number of days to the date.

NEW QUESTION # 55
......
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