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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 2: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 3: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 4: Structured Streaming | 10% | - Streaming Applications
|
| Topic 5: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 6: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 7: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?
- A. By configuring the option recoveryLocation during writeStream.
- B. By configuring the option recoveryLocation during SparkSession initialization.
- C. By configuring the option checkpointLocation during readStream.
- D. By configuring the option checkpointLocation during writeStream.
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A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?
- A. query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start() - B. query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start() - C. query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start() - D. query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()
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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.withColumn()
- D. DataFrame.filter()
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A developer runs:
What is the result?
Options:
- A. It stores all data in a single Parquet file.
- B. It creates separate directories for each unique combination of color and fruit.
- C. It throws an error if there are null values in either partition column.
- D. It appends new partitions to an existing Parquet file.
Explanation: Only visible for Exam4Labs members. You can sign-up / login (it's free).
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. Convert the Pandas UDF from a Series → Series UDF to a Series → Scalar UDF
- C. Convert the Pandas UDF to a PySpark UDF
- D. Run the in_spanish_inner() function in a mapInPandas() function call
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