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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q41-Q46):
NEW QUESTION # 41
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)
B)
C)
D)
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C
NEW QUESTION # 42
Given the schema:
event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:
Answer: A
Explanation:
dedup_df = iot_bronze_df.dropDuplicates(["event_ts","sensor_id","metric_value"]) dropDuplicates accepts a list of columns to use for deduplication.
This ensures only unique records based on the specified keys are retained.
Reference:DataFrame.dropDuplicates() API
NEW QUESTION # 43
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?
Answer: A
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 # 44
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?
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 # 45
A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To filter rows based on a condition and display them in Spark, usefilter(...).show():
employees_df.filter(employees_df.tenure >= 5).show()
Option A is correct and shows the results.
Option B filters but doesn't display them.
Option C uses Python's built-infilter, not Spark.
Option D collects the results to the driver, which is unnecessary if.show()is sufficient.
Final Answer: A
NEW QUESTION # 46
......
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