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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q29-Q34):

NEW QUESTION # 29
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A. A Cartesian join
B. A sort-merge join
C. A broadcast nested loop join
D. A shuffled hash join

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B

NEW QUESTION # 30
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 cluster mode
B. Increase the driver memory on the client machine
C. Add more executor instances to the cluster
D. Switch the deployment mode to local mode

Answer: A

Explanation:
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

NEW QUESTION # 31
Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp")," ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A. When the groupBy transformation is applied
B. When the count action is applied
C. When the show action is applied
D. When the filter transformation is applied

Answer: B

Explanation:
Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.
The first action in the code is:reduced_df.count()
So Spark starts processing data at this line.
Reference:Apache Spark Programming Guide - Lazy Evaluation

NEW QUESTION # 32
A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A. DataFrame.select()
B. DataFrame.groupBy().agg()
C. DataFrame.filter()
D. DataFrame.withColumn()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Operations that trigger data movement across partitions (like groupBy, join, repartition) result in a shuffle and a new stage.
From Spark documentation:
"groupBy and aggregation cause data to be shuffled across partitions to combine rows with the same key." Option A (groupBy + agg) # causes shuffle.
Options B, C, and D (filter, withColumn, select) # transformations that do not require shuffling; they are narrow dependencies.
Final Answer: A

NEW QUESTION # 33
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 from a Series # Series UDF to a Series # Scalar UDF
B. Run thein_spanish_inner()function in amapInPandas()function call
C. Convert the Pandas UDF from a Series # Series UDF to an Iterator[Series] # Iterator[Series] UDF
D. Convert the Pandas UDF to a PySpark UDF

Answer: C

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 # 34
......

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New Associate-Developer-Apache-Spark-3.5 Braindumps Files | Latest Associate-Developer-Apache-Spark-3.5 Download Fee: Databricks Certified Associate Developer for Apache Spark 3.5 - Python 100% Pass

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q29-Q34):

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