Associate-Developer-Apache-Spark-3.5 Zertifizierungsprüfung - Associate-Developer-Apache-Spark-3.5 Online Test

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Associate-Developer-Apache-Spark-3.5 Prüfungsfragen mit Lösungen (Q12-Q17):

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

Antwort: D

Begründung:
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.

13. Frage
A Spark application is experiencing performance issues in client mode because the driver is resource- constrained.
How should this issue be resolved?

A. Switch the deployment mode to local mode
B. Increase the driver memory on the client machine
C. Add more executor instances to the cluster
D. Switch the deployment mode to cluster mode

Antwort: D

Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark's client mode, the driver runs on the local machine that submitted the job. If that machine is resource- constrained (e.g., low memory), performance degrades.
From the Spark documentation:
"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability." Option A is incorrect - executors do not help the driver directly.
Option B might help short-term but does not scale.
Option C is correct - switching to cluster mode moves the driver to the cluster.
Option D (local mode) is for development/testing, not production.
Final Answer: C

14. Frage
A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A. df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
B. df = spark.read.parquet("/path/events/data/*")
C. df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
D. df = spark.read.parquet("/path/events/data/")

Antwort: C

Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
To read all files recursively within a nested directory structure, Spark requires therecursiveFileLookupoption to be explicitly enabled. According to Databricks official documentation, when dealing with deeply nested Parquet files in a directory tree (as shown in this example), you should set:
df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/") This ensures that Spark searches through all subdirectories under/path/events/data/and reads any Parquet files it finds, regardless of the folder depth.
Option A is incorrect because while it includes an option,inferSchemais irrelevant here and does not enable recursive file reading.
Option C is incorrect because wildcards may not reliably match deep nested structures beyond one directory level.
Option D is incorrect because it will only read files directly within/path/events/data/and not subdirectories like
/2023/01/01.
Databricks documentation reference:
"To read files recursively from nested folders, set therecursiveFileLookupoption to true. This is useful when data is organized in hierarchical folder structures" - Databricks documentation on Parquet files ingestion and options.

15. Frage
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
B. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
C. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors

Antwort: A

Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.
Reference:Spark 3.5.5 Documentation - Tuning

16. Frage
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)

B)

C)

D)

Options:

A. Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
B. Uses trigger() - default micro-batch trigger without interval.
C. Uses trigger(continuous='5 seconds') - continuous processing mode.
D. Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.

Antwort: D

Begründung:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
outputMode("append")
trigger(processingTime='5 seconds')
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers

17. Frage
......

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Associate-Developer-Apache-Spark-3.5 Zertifizierungsprüfung - Associate-Developer-Apache-Spark-3.5 Online Test

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Associate-Developer-Apache-Spark-3.5 Prüfungsfragen mit Lösungen (Q12-Q17):

Associate-Developer-Apache-Spark-3.5 Databricks Certified Associate Developer for Apache Spark 3.5 - Python Pass4sure Zertifizierung & Databricks Certified Associate Developer for Apache Spark 3.5 - Python zuverlässige Prüfung Übung

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