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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q32-Q37):

NEW QUESTION # 32
A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

Answer: C

Explanation:
In Structured Streaming, only a limited subset of operations is supported due to the nature of unbounded data. Operations like sorting (orderBy) and global aggregation (countDistinct) require a full view of the dataset, which is not possible with streaming data unless specific watermarks or windows are defined.
Review of Each Option:
A: select(countDistinct("Name"))
Not allowed - Global aggregation like countDistinct() requires the full dataset and is not supported directly in streaming without watermark and windowing logic.
Reference: Databricks Structured Streaming Guide - Unsupported Operations.
B: groupby("Id").count()
Supported - Streaming aggregations over a key (like groupBy("Id")) are supported. Spark maintains intermediate state for each key.
Reference: Databricks Docs → Aggregations in Structured Streaming (https://docs.databricks.com/structured-streaming/aggregation.html) C . orderBy("timestamp").limit(4)
Not allowed - Sorting and limiting require a full view of the stream (which is infinite), so this is unsupported in streaming DataFrames.
Reference: Spark Structured Streaming - Unsupported Operations (ordering without watermark/window not allowed).
D: filter(col("count") < 30).show()
Not allowed - show() is a blocking operation used for debugging batch DataFrames; it's not allowed on streaming DataFrames.
Reference: Structured Streaming Programming Guide - Output operations like show() are not supported.
Reference Extract from Official Guide:
"Operations like orderBy, limit, show, and countDistinct are not supported in Structured Streaming because they require the full dataset to compute a result. Use groupBy(...).agg(...) instead for incremental aggregations."
- Databricks Structured Streaming Programming Guide


NEW QUESTION # 33
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

Answer: B

Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
.outputMode("append")
.trigger(processingTime='5 seconds')
.start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.


NEW QUESTION # 34
A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

Answer: A

Explanation:
The most efficient and correct approach is to define a schema using StructType and pass it tospark.read.
schema(...).
This avoids schema inference overhead and ensures proper data types are enforced during read.
Example:
frompyspark.sql.typesimportStructType, StructField, StringType, DoubleType schema = StructType([ StructField("order_id", StringType(),True), StructField("amount", DoubleType(),True),
])
df = spark.read.schema(schema).json("path/to/json")
- Source:Databricks Guide - Read JSON with predefined schema


NEW QUESTION # 35
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?

Answer: D

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 # 36
Which command overwrites an existing JSON file when writing a DataFrame?

Answer: D

Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.


NEW QUESTION # 37
......

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