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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q75-Q80):

NEW QUESTION # 75
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

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

Answer: C

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 # 76
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 an Iterator[Series] # Iterator[Series] UDF
B. Run thein_spanish_inner()function in amapInPandas()function call
C. Convert the Pandas UDF from a Series # Series UDF to a Series # Scalar UDF
D. Convert the Pandas UDF to a PySpark UDF

Answer: A

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 # 77
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A)

B)

C)

D)

A. result_df = prices_df
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
B. result_df = prices_df
.agg(F.count("spot_price").alias("spot_price"))
.filter(F.col("spot_price") > F.lit("min_price"))
C. result_df = prices_df
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
D. result_df = prices_df
.agg(F.min("spot_price"), F.max("spot_price"))

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isBbecause it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.
* F.count_if(condition) counts the number of rows that meet the specified boolean condition.
* In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-
43773)."
Why other options are incorrect or outdated:
* Auses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
* Cperforms a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
* Dincorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore,Bis the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.

NEW QUESTION # 78
Given the following code snippet inmy_spark_app.py:

What is the role of the driver node?

A. The driver node stores the final result after computations are completed by worker nodes
B. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes
C. The driver node only provides the user interface for monitoring the application
D. The driver node holds the DataFrame data and performs all computations locally

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In the Spark architecture, the driver node is responsible for orchestrating the execution of a Spark application.
It converts user-defined transformations and actions into a logical plan, optimizes it into a physical plan, and then splits the plan into tasks that are distributed to the executor nodes.
As per Databricks and Spark documentation:
"The driver node is responsible for maintaining information about the Spark application, responding to a user's program or input, and analyzing, distributing, and scheduling work across the executors." This means:
Option A is correct because the driver schedules and coordinates the job execution.
Option B is incorrect because the driver does more than just UI monitoring.
Option C is incorrect since data and computations are distributed across executor nodes.
Option D is incorrect; results are returned to the driver but not stored long-term by it.
Reference: Databricks Certified Developer Spark 3.5 Documentation # Spark Architecture # Driver vs Executors.

NEW QUESTION # 79
A developer runs:

What is the result?
Options:

A. It stores all data in a single Parquet file.
B. It appends new partitions to an existing Parquet file.
C. It throws an error if there are null values in either partition column.
D. It creates separate directories for each unique combination of color and fruit.

Answer: D

Explanation:
ThepartitionBy()method in Spark organizes output into subdirectories based on unique combinations of the specified columns:
e.g.
/path/to/output/color=red/fruit=apple/part-0000.parquet
/path/to/output/color=green/fruit=banana/part-0001.parquet
This improves query performance via partition pruning.
It does not consolidate into a single file.
Null values are allowed in partitions.
It does not "append" unless.mode("append")is used.
Reference:Spark Write with Partitioning

NEW QUESTION # 80
......

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