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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q70-Q75):

NEW QUESTION # 70
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A. By configuring the optioncheckpointLocationduringwriteStream
B. By configuring the optionrecoveryLocationduring the SparkSession initialization
C. By configuring the optioncheckpointLocationduringreadStream
D. By configuring the optionrecoveryLocationduringwriteStream

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 71
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A. Use the repartition() transformation with a lower number of partitions
B. Use the sortBy() transformation to reorganize the data
C. Use the distinct() transformation to combine similar partitions
D. Use the coalesce() transformation with a lower number of partitions

Answer: D

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce

NEW QUESTION # 72
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A. By configuring the option recoveryLocation during writeStream
B. By configuring the option recoveryLocation during the SparkSession initialization
C. By configuring the option checkpointLocation during writeStream
D. By configuring the option checkpointLocation during readStream

Answer: C

Explanation:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify the checkpointLocation option during the writeStream operation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify the checkpointLocation option before you run a streaming query, as in the following example:
.option("checkpointLocation", "/path/to/checkpoint/dir")
.toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting the checkpointLocation during writeStream, Spark can maintain state information and ensure exactly-once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 73
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:

A. dropDuplicates with no arguments (removes based on all columns)
B. groupBy without aggregation (invalid use)
C. dropDuplicates on the exact matching fields
D. dropDuplicates on all columns (wrong criteria)

Answer: C

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.

NEW QUESTION # 74
A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
C. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
D. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation -Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.

NEW QUESTION # 75
......

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