FREE PDF 2025 DATABRICKS ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 NEWEST TEST SIMULATOR FEE

Free PDF 2025 Databricks Associate-Developer-Apache-Spark-3.5 Newest Test Simulator Fee

Free PDF 2025 Databricks Associate-Developer-Apache-Spark-3.5 Newest Test Simulator Fee

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q67-Q72):

NEW QUESTION # 67
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 optioncheckpointLocationduringwriteStream
  • C. By configuring the optioncheckpointLocationduringreadStream
  • D. By configuring the optionrecoveryLocationduring the SparkSession initialization

Answer: B

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 # 68
A data engineer is working on the DataFrame:

(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?

  • A. df.select("Name").distinct().orderBy(df["Name"].desc())
  • B. df.select("Name").distinct()
  • C. df.select("Name").orderBy(df["Name"].asc())
  • D. df.select("Name").distinct().orderBy(df["Name"])

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To extract unique values from a column and sort them alphabetically:
distinct()is required to remove duplicate values.
orderBy()is needed to sort the results alphabetically (ascending by default).
Correct code:
df.select("Name").distinct().orderBy(df["Name"])
This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting.
Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.


NEW QUESTION # 69
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

  • A. Use the repartition() transformation with a lower number of partitions
  • B. Use the distinct() transformation to combine similar partitions
  • C. Use the sortBy() transformation to reorganize the data
  • D. Use the coalesce() transformation with a lower number of partitions

Answer: D

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce


NEW QUESTION # 70
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

  • A. The conversion will automatically distribute the data across worker nodes
  • B. The operation will load all data into the driver's memory, potentially causing memory overflow
  • C. Data will be lost during conversion
  • D. The operation will fail if the Pandas DataFrame exceeds 1000 rows

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D


NEW QUESTION # 71
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
frompyspark.sql.functionsimportbroadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

  • A. It filters the id values before performing the join.
  • B. It ensures that the join happens only when the id values are identical.
  • C. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
  • D. It increases the partition size for df1 and df2.

Answer: C

Explanation:
broadcast(df1) tells Spark to send the small DataFrame (df1) to all worker nodes.
This eliminates the need for shuffling df1 during the join.
Broadcast joins are optimized for scenarios with one large and one small table.
Reference:Spark SQL Performance Tuning Guide - Broadcast Joins


NEW QUESTION # 72
......

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