ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 DUMPS GUIDE | ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 TRAINING KIT

Associate-Developer-Apache-Spark-3.5 Dumps Guide | Associate-Developer-Apache-Spark-3.5 Training Kit

Associate-Developer-Apache-Spark-3.5 Dumps Guide | Associate-Developer-Apache-Spark-3.5 Training Kit

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

NEW QUESTION # 45
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

  • A. DataFrame.filter()
  • B. DataFrame.groupBy().agg()
  • C. DataFrame.select()
  • D. DataFrame.withColumn()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) # causes shuffle.
Options B, C, and D (filter, withColumn, select) # transformations that do not require shuffling; they are narrow dependencies.
Final Answer: A


NEW QUESTION # 46
A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns fortransaction_id,account_number, transaction_amount, andtimestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

  • A. df = df.dropDuplicates()
  • B. df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first ("timestamp"))
  • C. df = df.filter(F.col("transaction_id").isNotNull())
  • D. df = df.dropDuplicates(["transaction_amount"])

Answer: A

Explanation:
dropDuplicates() with no column list removes duplicates based on all columns.
It's the most efficient and semantically correct way to deduplicate records that are completely identical across all fields.
From the PySpark documentation:
dropDuplicates(): Return a new DataFrame with duplicate rows removed, considering all columns if none are specified.
- Source:PySpark DataFrame.dropDuplicates() API


NEW QUESTION # 47
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.option("inferSchema", "true").parquet("/path/events/data/")
  • B. df = spark.read.parquet("/path/events/data/*")
  • C. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
  • D. df = spark.read.parquet("/path/events/data/")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To read all files recursively within a nested directory structure, Spark requires therecursiveFileLookupoption 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,inferSchemais 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 therecursiveFileLookupoption to true. This is useful when data is organized in hierarchical folder structures" - Databricks documentation on Parquet files ingestion and options.


NEW QUESTION # 48
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

  • A. By configuring the optionrecoveryLocationduringwriteStream
  • B. By configuring the optioncheckpointLocationduringreadStream
  • C. By configuring the optioncheckpointLocationduringwriteStream
  • D. By configuring the optionrecoveryLocationduring the SparkSession initialization

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.


NEW QUESTION # 49
The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

  • A. @F.udf(T.DoubleType())
    def simple_udf(t: int) -> int:
    return t * 3.14159
  • B. @F.udf(T.IntegerType())
    def simple_udf(t: int) -> int:
    return t * 3.14159
  • C. @F.udf(T.IntegerType())
    def simple_udf(t: float) -> float:
    return t * 3.14159
  • D. @F.udf(T.DoubleType())
    def simple_udf(t: float) -> float:
    return t * 3.14159

Answer: D

Explanation:
Comprehensive and Detailed Explanation:
The original code has several issues:
It references a variable answer that is undefined.
The function is annotated to return a str, but the logic attempts numeric multiplication.
The UDF return type is declared as T.IntegerType() but the function performs a floating-point operation, which is incompatible.
Option B correctly:
Uses DoubleType to reflect the fact that the multiplication involves a float (3.14159).
Declares the input as float, which aligns with the multiplication.
Returns a float, which matches both the logic and the schema type annotation.
This structure aligns with how PySpark expects User Defined Functions (UDFs) to be declared:
"To define a UDF you must specify a Python function and provide the return type using the relevant Spark SQL type (e.g., DoubleType for float results)." Example from official documentation:
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType
@udf(returnType=DoubleType())
def multiply_by_pi(x: float) -> float:
return x * 3.14159
This makes Option B the syntactically and semantically correct choice.


NEW QUESTION # 50
......

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