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Databricks Associate-Developer-Apache-Spark-3.5 Exam Course - Associate-Developer-Apache-Spark-3.5 Reliable Exam Simulator

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q58-Q63):

NEW QUESTION # 58
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.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
C. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)
D. customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)

Answer: B

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 # 59
A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A. users.write.saveAsTable("default_table", path="/some/path")
B. users.write(path="/some/path").saveAsTable("default_table")
C. users.write.option("path", "/some/path").saveAsTable("default_table")
D. users.write.saveAsTable("default_table").option("path", "/some/path")

Answer: C

Explanation:
To persist a table and specify the save path, use:
users.write.option("path","/some/path").saveAsTable("default_table")
The .option("path", ...) must be applied before calling saveAsTable.
Option A uses invalid syntax (write(path=...)).
Option B applies.option()after.saveAsTable()-which is too late.
Option D uses incorrect syntax (no path parameter in saveAsTable).
Reference:Spark SQL - Save as Table

NEW QUESTION # 60
A developer is trying to join two tables,sales.purchases_fctandsales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in thepurchases_fcttable that do not exist in thecustomer_dimtable are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
B. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
C. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
D. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.

NEW QUESTION # 61
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 will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
B. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
C. 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.
D. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.

Answer: A

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 # 62
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A. result_df = prices_df
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
B. result_df = prices_df
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
C. result_df = prices_df
.agg(F.min("spot_price"), F.max("spot_price"))
D. result_df = prices_df
.agg(F.count("spot_price").alias("spot_price"))
.filter(F.col("spot_price") > F.lit("min_price"))

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isBbecause it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.
* F.count_if(condition) counts the number of rows that meet the specified boolean condition.
* In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-
43773)."
Why other options are incorrect or outdated:
* Auses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
* Cperforms a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
* Dincorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore,Bis the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.

NEW QUESTION # 63
......

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