QUIZ DATABRICKS - ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 - DATABRICKS CERTIFIED ASSOCIATE DEVELOPER FOR APACHE SPARK 3.5 - PYTHON–HIGH-QUALITY EXAM SAMPLE ONLINE

Quiz Databricks - Associate-Developer-Apache-Spark-3.5 - Databricks Certified Associate Developer for Apache Spark 3.5 - Python–High-quality Exam Sample Online

Quiz Databricks - Associate-Developer-Apache-Spark-3.5 - Databricks Certified Associate Developer for Apache Spark 3.5 - Python–High-quality Exam Sample Online

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q55-Q60):

NEW QUESTION # 55
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:

  • A. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
  • B. Uses trigger(continuous='5 seconds') - continuous processing mode.
  • C. Uses trigger() - default micro-batch trigger without interval.
  • D. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

Answer: D

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.
Reference:Spark Structured Streaming - Triggers


NEW QUESTION # 56
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?

  • A. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
  • B. Use spark.read.json() with the inferSchema option set to true
  • C. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
  • D. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.

Answer: D

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 # 57
What is the behavior for functiondate_sub(start, days)if a negative value is passed into thedaysparameter?

  • A. The number of days specified will be added to the start date
  • B. An error message of an invalid parameter will be returned
  • C. The number of days specified will be removed from the start date
  • D. The same start date will be returned

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The functiondate_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.
Reference: Spark SQL Functions # date_sub()


NEW QUESTION # 58
A data engineer writes the following code to join two DataFramesdf1anddf2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

  • A. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
  • B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
  • C. Broadcast join, as df2 is smaller than the default broadcast threshold
  • D. Shuffle join because no broadcast hints were provided

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default broadcast join threshold in Spark is:
spark.sql.autoBroadcastJoinThreshold = 10MB
Sincedf2is only 8 MB (less than 10 MB), Spark will automatically apply a broadcast join without requiring explicit hints.
From the Spark documentation:
"If one side of the join is smaller than the broadcast threshold, Spark will automatically broadcast it to all executors." A is incorrect because Spark does support auto broadcast even with static plans.
B is correct: Spark will automatically broadcast df2.
C and D are incorrect because Spark's default logic handles this optimization.
Final Answer: B


NEW QUESTION # 59
A data scientist is working on a project that requires processing large amounts of structured data, performing SQL queries, and applying machine learning algorithms. The data scientist is considering using Apache Spark for this task.
Which combination of Apache Spark modules should the data scientist use in this scenario?
Options:

  • A. Spark DataFrames, Structured Streaming, and GraphX
  • B. Spark DataFrames, Spark SQL, and MLlib
  • C. Spark SQL, Pandas API on Spark, and Structured Streaming
  • D. Spark Streaming, GraphX, and Pandas API on Spark

Answer: B

Explanation:
Comprehensive Explanation:
To cover structured data processing, SQL querying, and machine learning in Apache Spark, the correct combination of components is:
Spark DataFrames: for structured data processing
Spark SQL: to execute SQL queries over structured data
MLlib: Spark's scalable machine learning library
This trio is designed for exactly this type of use case.
Why other options are incorrect:
A: GraphX is for graph processing - not needed here.
B: Pandas API on Spark is useful, but MLlib is essential for ML, which this option omits.
C: Spark Streaming is legacy; GraphX is irrelevant here.
Reference:Apache Spark Modules Overview


NEW QUESTION # 60
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