ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 GUIDE & SAMPLE ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 QUESTIONS PDF

Associate-Developer-Apache-Spark-3.5 Guide & Sample Associate-Developer-Apache-Spark-3.5 Questions Pdf

Associate-Developer-Apache-Spark-3.5 Guide & Sample Associate-Developer-Apache-Spark-3.5 Questions Pdf

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q16-Q21):

NEW QUESTION # 16
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: C

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 # 17
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

  • A. filtered_df = users_raw_df.na.drop(how='all')
  • B. filtered_df = users_raw_df.na.drop(thresh=0)
  • C. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
  • D. filtered_df = users_raw_df.na.drop(how='any')

Answer: D

Explanation:
na.drop(how='any')drops any row that has at least one null value.
This is exactly what's needed when the goal is to retain only fully complete records.
Usage:CopyEdit
filtered_df = users_raw_df.na.drop(how='any')
Explanation of incorrect options:
A: thresh=0 is invalid - thresh must be # 1.
B: how='all' drops only rows where all columns are null (too lenient).
D: spark.na.drop doesn't support mixing how and thresh in that way; it's incorrect syntax.
Reference:PySpark DataFrameNaFunctions.drop()


NEW QUESTION # 18
A data engineer needs to write a DataFramedfto a Parquet file, partitioned by the columncountry, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

  • A. df.write.mode("append").partitionBy("country").parquet("/data/output")
  • B. df.write.partitionBy("country").parquet("/data/output")
  • C. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
  • D. df.write.mode("overwrite").parquet("/data/output")

Answer: C

Explanation:
The.mode("overwrite")ensures that existing files at the path will be replaced.
partitionBy("country")optimizes queries by writing data into partitioned folders.
Correct syntax:
df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- Source:Spark SQL, DataFrames and Datasets Guide


NEW QUESTION # 19
An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline.
The initial code is:

def in_spanish_inner(df: pd.Series) -> pd.Series:
model = get_translation_model(target_lang='es')
return df.apply(model)
in_spanish = sf.pandas_udf(in_spanish_inner, StringType())
How can the MLOps engineer change this code to reduce how many times the language model is loaded?

  • A. Convert the Pandas UDF to a PySpark UDF
  • B. Convert the Pandas UDF from a Series # Series UDF to a Series # Scalar UDF
  • C. Run thein_spanish_inner()function in amapInPandas()function call
  • D. Convert the Pandas UDF from a Series # Series UDF to an Iterator[Series] # Iterator[Series] UDF

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language modelis created on each call, which happens per batch. This is inefficient and results in significant overhead due to repeated model initialization.
To reduce the frequency of model loading, the engineer should convert the UDF to an iterator-based Pandas UDF (Iterator[pd.Series] -> Iterator[pd.Series]). This allows the model to be loaded once per executor and reused across multiple batches, rather than once per call.
From the official Databricks documentation:
"Iterator of Series to Iterator of Series UDFs are useful when the UDF initialization is expensive... For example, loading a ML model once per executor rather than once per row/batch."
- Databricks Official Docs: Pandas UDFs
Correct implementation looks like:
python
CopyEdit
@pandas_udf("string")
def translate_udf(batch_iter: Iterator[pd.Series]) -> Iterator[pd.Series]:
model = get_translation_model(target_lang='es')
for batch in batch_iter:
yield batch.apply(model)
This refactor ensures theget_translation_model()is invoked once per executor process, not per batch, significantly improving pipeline performance.


NEW QUESTION # 20
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 optioncheckpointLocationduringreadStream
  • B. By configuring the optionrecoveryLocationduring the SparkSession initialization
  • C. By configuring the optionrecoveryLocationduringwriteStream
  • D. By configuring the optioncheckpointLocationduringwriteStream

Answer: D

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 # 21
......

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