DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 LATEST EXAM GUIDE - POSITIVE ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 FEEDBACK

Databricks Associate-Developer-Apache-Spark-3.5 Latest Exam Guide - Positive Associate-Developer-Apache-Spark-3.5 Feedback

Databricks Associate-Developer-Apache-Spark-3.5 Latest Exam Guide - Positive Associate-Developer-Apache-Spark-3.5 Feedback

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q15-Q20):

NEW QUESTION # 15
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

  • A. customerDF.withColumn("username", substring_index(col("email"), "@", 1))
    .withColumn("domain", substring_index(col("email"), "@", -1))
  • B. customerDF.select(
    col("email").substr(0, 5).alias("username"),
    col("email").substr(-5).alias("domain")
    )
  • C. customerDF.select(
    regexp_replace(col("email"), "@", "").alias("username"),
    regexp_replace(col("email"), "@", "").alias("domain")
    )
  • D. customerDF.withColumn("username", split(col("email"), "@").getItem(0))
    .withColumn("domain", split(col("email"), "@").getItem(1))

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Option B is the correct and idiomatic approach in PySpark to split a string column (like email) based on a delimiter such as "@".
The split(col("email"), "@") function returns an array with two elements: username and domain.
getItem(0) retrieves the first part (username).
getItem(1) retrieves the second part (domain).
withColumn() is used to create new columns from the extracted values.
Example from official Databricks Spark documentation on splitting columns:
from pyspark.sql.functions import split, col
df.withColumn("username", split(col("email"), "@").getItem(0))
withColumn("domain", split(col("email"), "@").getItem(1))
##Why other options are incorrect:
A uses fixed substring indices (substr(0, 5)), which won't correctly extract usernames and domains of varying lengths.
C uses substring_index, which is available but less idiomatic for splitting emails and is slightly less readable.
D removes "@" from the email entirely, losing the separation between username and domain, and ends up duplicating values in both fields.
Therefore, Option B is the most accurate and reliable solution according to Apache Spark 3.5 best practices.


NEW QUESTION # 16
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 it must use on='employee_id' to specify the join column explicitly
  • C. The code fails to execute because PySpark does not support joining DataFrames with a different structure
  • D. The code works as expected because the join condition explicitly matches employee_id from df1 with emp_id from df2

Answer: D

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 # 17
A Spark DataFramedfis cached using theMEMORY_AND_DISKstorage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

  • A. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
  • B. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
  • C. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
  • D. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When using theMEMORY_AND_DISKstorage level, Spark attempts to cache as much of the DataFrame in memory as possible. If the DataFrame does not fit entirely in memory, Spark will store the remaining partitions on disk. This allows processing to continue, albeit with a performance overhead due to disk I/O.
As per the Spark documentation:
"MEMORY_AND_DISK: It stores partitions that do not fit in memory on disk and keeps the rest in memory.
This can be useful when working with datasets that are larger than the available memory."
- Perficient Blogs: Spark - StorageLevel
This behavior ensures that Spark can handle datasets larger than the available memory by spilling excess data to disk, thus preventing job failures due to memory constraints.


NEW QUESTION # 18
How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

  • A. Set the spark.executor.memory property to a large value.
  • B. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
  • C. Configure the application to run in cluster mode instead of local mode.
  • D. Increase the number of local threads based on the number of CPU cores.

Answer: D

Explanation:
When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.
Using local[*] ensures Spark uses all available CPU cores for parallelism.
Example:
spark-submit --masterlocal[*]
Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.
Reference:Spark Master URLs


NEW QUESTION # 19
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.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first ("timestamp"))
  • B. df = df.dropDuplicates(["transaction_amount"])
  • C. df = df.filter(F.col("transaction_id").isNotNull())
  • D. df = df.dropDuplicates()

Answer: D

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 # 20
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