EXAM ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 QUIZ, ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 VALID TORRENT

Exam Associate-Developer-Apache-Spark-3.5 Quiz, Associate-Developer-Apache-Spark-3.5 Valid Torrent

Exam Associate-Developer-Apache-Spark-3.5 Quiz, Associate-Developer-Apache-Spark-3.5 Valid Torrent

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q81-Q86):

NEW QUESTION # 81
What is the benefit of using Pandas on Spark for data transformations?
Options:

  • A. It is available only with Python, thereby reducing the learning curve.
  • B. It computes results immediately using eager execution, making it simple to use.
  • C. It runs on a single node only, utilizing the memory with memory-bound DataFrames and hence cost- efficient.
  • D. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.

Answer: D

Explanation:
Pandas API on Spark (formerly Koalas) offers:
Familiar Pandas-like syntax
Distributed execution using Spark under the hood
Scalability for large datasets across the cluster
It provides the power of Spark while retaining the productivity of Pandas.
Reference:Pandas API on Spark Guide


NEW QUESTION # 82
A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
If each of the 10 nodes runs 4 executors, and each executor is assigned 4 CPU cores:
Executors per node = 4
Cores per executor = 4
Total executors = 4 * 10 = 40
Total cores = 40 executors * 4 cores = 160 cores
However, Spark uses 1 core for overhead on each node when managing multiple executors. Thus, the practical allocation is:
Total usable executors = 4 executors/node × 10 nodes = 40
Total cores = 4 cores × 40 executors = 160
Answer is A - but the question asks specifically about "CPU cores used by the application," assuming all
allocated cores are usable (as Spark typically runs executors without internal core reservation unless explicitly configured).
However, if you are considering 4 executors/node × 4 cores = 16 cores per node, across 10 nodes, that's 160.
Final Answer: A


NEW QUESTION # 83
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 stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
  • 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 splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.

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 # 84
A DataFramedfhas columnsname,age, andsalary. The developer needs to sort the DataFrame byagein ascending order andsalaryin descending order.
Which code snippet meets the requirement of the developer?

  • A. df.sort("age", "salary", ascending=[True, True]).show()
  • B. df.orderBy("age", "salary", ascending=[True, False]).show()
  • C. df.sort("age", "salary", ascending=[False, True]).show()
  • D. df.orderBy(col("age").asc(), col("salary").asc()).show()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To sort a PySpark DataFrame by multiple columns with mixed sort directions, the correct usage is:
python
CopyEdit
df.orderBy("age","salary", ascending=[True,False])
agewill be sorted in ascending order
salarywill be sorted in descending order
TheorderBy()andsort()methods in PySpark accept a list of booleans to specify the sort direction for each column.
Documentation Reference:PySpark API - DataFrame.orderBy


NEW QUESTION # 85
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").orderBy(df["Name"].asc())
  • B. df.select("Name").distinct().orderBy(df["Name"].desc())
  • C. df.select("Name").distinct().orderBy(df["Name"])
  • D. df.select("Name").distinct()

Answer: C

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 # 86
......

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