Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 - Databricks Certified Associate Developer for Apache Spark 3.5 – Python

Partitioning a dataset divides data into separate directories based on partition column values. When queries filter on partitioned columns, Spark can prune irrelevant partitions — meaning it only reads files that match the filter criteria.

Advantage:

Reduces I/O and improves performance by scanning only relevant subsets of data.

Example:

/data/sales/year=2023/month=10/...

/data/sales/year=2024/month=01/...

A query filtering WHERE year = 2024 reads only the relevant partition.

Why the other options are incorrect:

A: Compression is independent of partitioning.

B: Spark does not automatically clean partitions unless managed manually.

C: Partitioning does not cause Spark to load entire data into memory.

Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.

According to the Spark 3.5.5 documentation:

"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener."

This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.

Regarding other options:

B. While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.

C. Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.

D. Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.

Apache Spark was designed for big data and high-velocity workloads. Its core strength lies in its in-memory computation and parallel distributed processing model.

These features allow Spark to:

Process large-scale datasets quickly across many nodes.

Support real-time and near–real-time analytics for tasks like fraud detection and recommendations.

Minimize disk I/O through caching and memory persistence.

Thus, the key advantage in this use case is Spark’s ability to handle large data volumes efficiently using distributed, in-memory computation.

Why the other options are incorrect:

A: Spark is optimized for large, not small, datasets.

C: SQL support is useful but doesn’t solve the scalability issue.

D: MLlib supports machine learning but relies on Spark’s parallel computation for speed.

The approx_percentile function in Spark is a performance-optimized alternative to percentile. It takes an optional accuracy parameter:

approx_percentile(column, percentage, accuracy)

Higher accuracy values → more precise results, but increased memory/computation.

Lower values → faster but less accurate.

From the documentation:

“Increasing the accuracy improves precision but increases memory usage.”

Final Answer: D

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

The method saveAsTable() creates a new table and optionally fails if the table exists.

From Spark documentation:

"The mode 'ErrorIfExists' (default) will throw an error if the table already exists."

Thus:

Option A is correct.

Option B (Overwrite) would overwrite existing data — not acceptable here.

Option C and D use save(), which doesn't create a managed table with metadata in the metastore.

Final Answer: A

Apache Spark follows a lazy evaluation model, meaning transformations (like filter(), select(), map()) are not executed immediately. Instead, they build a logical plan (lineage graph) that represents the sequence of operations to be applied.

Execution only begins when an action (e.g., count(), collect(), save(), show()) is called. At that point, Spark’s engine:

Optimizes the logical plan into a physical plan.

Divides it into stages and tasks.

Executes them across the cluster.

This design helps Spark optimize execution paths and avoid unnecessary computations.

Why the other options are incorrect:

A: Transformations do not execute immediately; they are deferred.

B: Optimization happens during job execution (after an action), not during transformations.

D: Execution starts automatically once an action is triggered, no manual intervention needed.

In Spark, when a CSV row does not match the provided schema, Spark does not raise an error by default. Instead, it returns null for fields that cannot be parsed correctly.

In the first row, "hello" cannot be cast to Integer for the age field → Spark sets age=None

In the second row, "20" is a valid integer → age=20

So the output will be:

[Row(name='bambi', age=None), Row(name='alladin', age=20)]

Final Answer: C

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.

To ensure that data written out to disk is sorted, it is important to consider how Spark writes data when saving to Parquet tables. The methods .sort() or .orderBy() apply a global sort but do not guarantee that the sorting will persist in the final output files unless certain conditions are met (e.g. a single partition via .coalesce(1) — which is not scalable).

Instead, the proper method in distributed Spark processing to ensure rows are sorted within their respective partitions when written out is:

sortWithinPartitions("column_name")

According to Apache Spark documentation:

"sortWithinPartitions() ensures each partition is sorted by the specified columns. This is useful for downstream systems that require sorted files."

This method works efficiently in distributed settings, avoids the performance bottleneck of global sorting (as in .orderBy() or .sort()), and guarantees each output partition has sorted records — which meets the requirement of consistently sorted data.

Thus:

Option A and B do not guarantee the persisted file contents are sorted.

Option C introduces a bottleneck via .coalesce(1) (single partition).

Option D correctly applies sorting within partitions and is scalable.