Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Databricks Certified Associate Developer for Apache Spark 3.5-Python Questions and Answers

Comprehensive and Detailed Explanation From Exact Extract:

To extract unique values from a column and sort them alphabetically:

distinct()is required to remove duplicate values.

orderBy()is needed to sort the results alphabetically (ascending by default).

Correct code:

df.select("Name").distinct().orderBy(df["Name"])

This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting. Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.

The correct way to overwrite an existing file using the DataFrameWriter is:

df.write.mode("overwrite").json("path/to/file")

Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.

Comprehensive and Detailed Explanation:

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

Comprehensive and Detailed Explanation From Exact Extract:

Apache Arrow is used under the hood to optimize conversion between Pandas and PySpark DataFrames. The correct configuration setting is:

spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")

From the official documentation:

“This configuration must be enabled to allow for vectorized execution and efficient conversion between Pandas and PySpark using Arrow.”

Option B is correct.

Options A, C, and D are invalid config keys and not recognized by Spark.

Final Answer: B

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

Comprehensive and Detailed Explanation From Exact Extract:

Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.

Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.

This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.

Comprehensive and Detailed Explanation From Exact Extract:

The methoddropDuplicatesWithinWatermark()in Structured Streaming drops duplicate records based on a specified column and watermark window. The watermark defines the threshold for how late data is considered valid.

From the Spark documentation:

"dropDuplicatesWithinWatermark removes duplicates that occur within the event-time watermark window."

In this case, Spark will retain the first occurrence and drop subsequent records within the 30-minute watermark window.

Final Answer: B

Comprehensive and Detailed Explanation From Exact Extract:

In Spark’s client mode, the driver runs on the local machine that submitted the job. If that machine is resource-constrained (e.g., low memory), performance degrades.

From the Spark documentation:

"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability."

Option A is incorrect — executors do not help the driver directly.

Option B might help short-term but does not scale.

Option C is correct — switching to cluster mode moves the driver to the cluster.

Option D (local mode) is for development/testing, not production.

Final Answer: C

Which operation is supported with streaming_df?

A. streaming_df.select(countDistinct("Name"))

B. streaming_df.groupby("Id").count()

C. streaming_df.orderBy("timestamp").limit(4)

D. streaming_df.filter(col("count") < 30).show()

Answer: B

Comprehensive and Detailed Explanation:

In Structured Streaming, only a limited subset of operations is supported due to the nature of unbounded data. Operations like sorting (orderBy) and global aggregation (countDistinct) require a full view of the dataset, which is not possible with streaming data unless specific watermarks or windows are defined.

Review of Each Option:

A. select(countDistinct("Name"))

Not allowed — Global aggregation like countDistinct() requires the full dataset and is not supported directly in streaming without watermark and windowing logic.

Comprehensive and Detailed Explanation:

Broadcast joins work by sending the smaller DataFrame to all executors, eliminating the shuffle of the larger DataFrame.

From Spark documentation:

“Broadcast joins are efficient when one DataFrame is small enough to fit in memory. Spark avoids shuffling the larger table.”

DataFrame B (1 GB) fits within the default threshold and should be broadcasted.

It eliminates the need to shuffle the large DataFrame A.

Final Answer: B

Comprehensive and Detailed Explanation From Exact Extract:

The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such asreduce()oraggregate().

From the documentation:

“To perform global aggregations on distributed data, actions likereduce()are commonly used to collect summaries such as min/max/avg.”

Accumulators (Option B) do not support max operations directly and are not intended for such analytics.

Broadcast (Option C) is used to send data to workers, not collect from them.

Spark UI (Option D) is a monitoring tool — not an analytics collection interface.

Final Answer: A

Comprehensive and Detailed Explanation:

To read from a specific Kafka topic using Structured Streaming, the correct syntax is:

python

CopyEdit

option("subscribe","feed")

This is explicitly defined in the Spark documentation:

"subscribe – The Kafka topic to subscribe to. Only one topic can be specified for this option."

(Source:Apache Spark Structured Streaming + Kafka Integration Guide)

B."subscribe.topic" is invalid.

C."kafka.topic" is not a recognized option.

D."topic" is not valid for Kafka source in Spark.

Comprehensive Explanation:

To cover structured data processing, SQL querying, and machine learning in Apache Spark, the correct combination of components is:

Spark DataFrames: for structured data processing

Spark SQL: to execute SQL queries over structured data

MLlib: Spark's scalable machine learning library

This trio is designed for exactly this type of use case.

Why other options are incorrect:

A: GraphX is for graph processing — not needed here.

B: Pandas API on Spark is useful, but MLlib is essential for ML, which this option omits.

C: Spark Streaming is legacy; GraphX is irrelevant here.

Comprehensive and Detailed Explanation:

The methodsaveAsTable()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 usesave(), which doesn't create a managed table with metadata in the metastore.

Final Answer: A

Comprehensive and Detailed Explanation From Exact Extract:

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

In the first row,"hello"cannot be cast to Integer for theagefield → Spark setsage=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

Comprehensive and Detailed Explanation From Exact Extract:

The functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.

Example:

SELECT date_sub('2024-05-01', -5)

-- Returns: 2024-05-06

So, a negative value effectively adds the absolute number of days to the date.

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

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.

Comprehensive and Detailed Explanation From Exact Extract:

The Spark UI is the standard and most effective way to inspect executor logs, task time, input size, and shuffles.

From the Databricks documentation:

“You can monitor job execution via the Spark Web UI. It includes detailed logs and metrics, including task-level execution time, shuffle reads/writes, and executor memory usage.”

(Source: Databricks Spark Monitoring Guide)

Option A is incorrect: logs are not guaranteed to be in/tmp, especially in cloud environments.

B.—verbosehelps during job submission but doesn't give detailed executor logs.

D.spark-sqlis a CLI tool for running queries, not for inspecting logs.

Hence, the correct method is using the Spark UI → Stages tab → Executor logs.

Comprehensive and Detailed Explanation From Exact Extract:

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.

Comprehensive and Detailed Explanation From Exact Extract:

Adaptive Query Execution (AQE) is a powerful optimization framework introduced in Apache Spark 3.0 and enabled by default since Spark 3.2. It dynamically adjusts query execution plans based on runtime statistics, leading to significant performance improvements. The key benefits of AQE include:

Dynamic Join Strategy Selection: AQE can switch join strategies at runtime. For instance, it can convert a sort-merge join to a broadcast hash join if it detects that one side of the join is small enough to be broadcasted, thus optimizing the join operation .

Handling Skewed Data: AQE detects skewed partitions during join operations and splits them into smaller partitions. This approach balances the workload across tasks, preventing scenarios where certain tasks take significantly longer due to data skew .

Coalescing Post-Shuffle Partitions: AQE dynamically coalesces small shuffle partitions into larger ones based on the actual data size, reducing the overhead of managing numerous small tasks and improving overall query performance .

These runtime optimizations allow Spark to adapt to the actual data characteristics during query execution, leading to more efficient resource utilization and faster query processing times.