View Full Databricks Certified Associate Developer for Apache Spark Exam Dumps  and Practice Test Dumps

 

Question 141.

Which DataFrame method can persist data using a specific storage level?

1. persist()
   2. explain()
   3. repartition()
   4. describe()

Correct Answer: 1. persist()

Explanation:

persist() allows a DataFrame to be stored using a chosen storage level, such as memory, disk, or a combination depending on the environment and configuration. This is useful when the same DataFrame will be reused across multiple actions. explain() displays execution plans, repartition() changes data distribution, and describe() returns summary statistics. persist() provides more control than cache() when a developer wants to explicitly select how intermediate results should be retained.

Question 142.

Which DataFrame method removes previously cached or persisted data from storage?

1. clearCache()
   2. unpersist()
   3. drop()
   4. release()

Correct Answer: 2. unpersist()

Explanation:

unpersist() removes cached or persisted blocks associated with a DataFrame, allowing executor memory or disk resources to be reclaimed. It is useful when an intermediate dataset is no longer needed. clearCache() applies at a broader catalog or session level rather than being the standard DataFrame method, while drop() removes columns. release() is not the standard DataFrame API for removing persisted data. Properly unpersisting large datasets can help control resource consumption.

Question 143.

Which method displays the query execution plan for a DataFrame?

1. describe()
   2. printSchema()
   3. explain()
   4. show()

Correct Answer: 3. explain()

Explanation:

explain() displays the logical and physical plans Spark uses to execute a DataFrame query. This information can reveal scans, projections, filters, joins, exchanges, and other execution operators. describe() returns summary statistics, printSchema() displays schema information, and show() displays rows. explain() is especially useful when troubleshooting performance or determining whether Spark is using an expected join strategy or optimization.

Question 144.

Which type of Spark operation triggers execution of previously defined lazy transformations?

1. Projection
   2. Expression
   3. Schema definition
   4. Action

Correct Answer: 4. Action

Explanation:

An action triggers Spark to evaluate the lineage of lazy transformations required to produce a result. Examples include count(), collect(), take(), and certain write operations. Transformations such as select(), filter(), and withColumn() generally build a logical plan without immediately processing the entire dataset. This lazy model allows Spark to optimize the combined operation plan before executing it across the cluster.

Question 145.

Which of the following is a Spark action?

1. count()
   2. select()
   3. filter()
   4. withColumn()

Correct Answer: 1. count()

Explanation:

count() is an action because it triggers Spark computation and returns the number of rows in the dataset. select(), filter(), and withColumn() are transformations that construct new logical DataFrames and are evaluated lazily. An action causes Spark to execute the required stages and tasks needed to produce a result. Recognizing actions is important because they determine when actual cluster work begins.

Question 146.

Which of the following is a transformation rather than an action?

1. collect()
   2. select()
   3. count()
   4. show()

Correct Answer: 2. select()

Explanation:

select() is a transformation because it creates a new logical DataFrame representing selected columns or expressions without immediately returning a final result. collect(), count(), and show() are actions that trigger computation. Spark’s lazy transformation model enables the optimizer to analyze a sequence of operations together before executing them. This can reduce unnecessary processing and produce a more efficient physical plan.

Question 147.

Which Spark component coordinates the execution of an application and communicates with executors?

1. Worker process only
   2. Storage level
   3. Driver
   4. DataFrameWriter

Correct Answer: 3. Driver

Explanation:

The driver coordinates a Spark application, creates the execution plan, schedules work, and communicates with executors. Executors run tasks and store data for the application. Storage levels describe persistence behavior, while DataFrameWriter provides APIs for writing datasets. The driver therefore acts as the central control process for application execution, though the actual distributed computation is performed by executors.

Question 148.

Which Spark component executes tasks and stores partitions for an application?

1. SparkSession only
   2. Catalyst optimizer
   3. Driver only
   4. Executor

Correct Answer: 4. Executor

Explanation:

Executors are processes responsible for running tasks and storing data partitions for a Spark application. The driver coordinates execution and schedules work, while executors perform the distributed computation. SparkSession is the main entry point for working with structured APIs, and Catalyst optimizes query plans. Executors therefore provide the computational resources where transformations and actions are actually carried out across the cluster.

Question 149.

What is a Spark task?

1. A unit of work executed on one partition
   2. A complete Spark application
   3. A permanent database table
   4. A cluster manager configuration

Correct Answer: 1. A unit of work executed on one partition

Explanation:

A task is a fundamental unit of execution in Spark and typically processes one partition of data within a stage. Multiple tasks can run in parallel across executors, depending on available resources. A complete Spark application consists of many jobs, stages, and tasks. Database tables and cluster manager settings are separate concepts. Understanding tasks and partitions is essential for reasoning about Spark parallelism and performance.

Question 150.

What generally determines the number of tasks in a Spark stage?

1. Number of DataFrame columns
   2. Number of partitions
   3. Number of SQL expressions
   4. Number of cached datasets

Correct Answer: 2. Number of partitions

Explanation:

A Spark stage generally creates one task for each partition that must be processed. Therefore, partition count strongly influences the amount of available task-level parallelism. DataFrame column count and the number of SQL expressions do not directly determine task count. Cached datasets can affect execution performance but are not the primary factor. Appropriate partitioning helps Spark distribute work efficiently across available executor resources.

Question 151.

What is a Spark stage primarily defined by?

1. A single DataFrame column
   2. A storage level setting
   3. A group of tasks separated by shuffle boundaries
   4. A user-defined SQL alias

Correct Answer: 3. A group of tasks separated by shuffle boundaries

Explanation:

A Spark stage represents a group of tasks that can be executed without crossing a shuffle boundary. Operations that require data redistribution, such as certain joins or aggregations, can divide a job into additional stages. Column selection, aliases, and storage levels do not by themselves define stages. Understanding stage boundaries helps explain why shuffle-heavy workloads can require more execution steps and incur additional network and disk overhead.

Question 152.

Which event commonly creates a new stage in Spark execution?

1. Renaming a column
   2. Selecting an existing column
   3. Adding a literal column
   4. A shuffle

Correct Answer: 4. A shuffle

Explanation:

A shuffle redistributes data across partitions and commonly introduces a new stage boundary in Spark. Operations such as groupBy(), repartition(), and some joins may require shuffles. Renaming columns, selecting existing columns, and adding literals are generally narrow transformations that do not require broad redistribution. Because shuffles involve network transfer and potentially disk I/O, they are important performance considerations.

Question 153.

Which type of dependency allows each output partition to depend on only a small number of input partitions?

1. Narrow dependency
   2. Wide dependency
   3. Broadcast dependency only
   4. Cartesian dependency only

Correct Answer: 1. Narrow dependency

Explanation:

A narrow dependency exists when each output partition depends on a limited number of input partitions, often just one. Transformations such as map-like projections and many filters are examples of narrow operations. Wide dependencies require data from multiple input partitions and often lead to shuffles. Narrow dependencies are generally less expensive because they allow pipelined execution without redistributing large amounts of data across the cluster.

Question 154.

Which type of dependency commonly requires data to be redistributed across partitions?

1. Narrow dependency
   2. Wide dependency
   3. Local variable dependency
   4. Schema dependency

Correct Answer: 2. Wide dependency

Explanation:

A wide dependency occurs when output partitions depend on records from multiple input partitions. This commonly requires a shuffle so related data can be redistributed across the cluster. Aggregations, repartitioning, and many joins can create wide dependencies. Narrow dependencies generally avoid this broad redistribution. Wide dependencies are important because they often create stage boundaries and can significantly influence job performance.

Question 155.

Which operation is likely to create a wide dependency?

1. select()
   2. withColumnRenamed()
   3. groupBy()
   4. lit()

Correct Answer: 3. groupBy()

Explanation:

groupBy() often creates a wide dependency because rows sharing the same grouping key may originate from many different partitions. Spark must redistribute those rows so that values belonging to the same group can be processed together. select(), withColumnRenamed(), and literal-column expressions generally do not require this redistribution. Wide transformations like groupBy() are therefore common sources of shuffle activity.

Question 156.

Which operation is most likely to remain a narrow transformation?

1. repartition()
   2. groupBy()
   3. distinct()
   4. filter()

Correct Answer: 4. filter()

Explanation:

filter() typically operates independently on each input partition and does not require rows to move between partitions. This makes it a narrow transformation. repartition(), groupBy(), and distinct() generally require redistribution and are associated with shuffles. Narrow transformations are usually cheaper because Spark can process them locally within partitions and pipeline them with other compatible operations.

Question 157.

What is the primary purpose of Spark’s Catalyst optimizer?

1. Optimize structured query plans
   2. Store cached DataFrames
   3. Manage external databases
   4. Create executor processes

Correct Answer: 1. Optimize structured query plans

Explanation:

Catalyst is Spark SQL’s query optimizer. It analyzes logical plans and applies optimization rules before producing an optimized physical execution strategy. It can simplify expressions, push filters, prune columns, and perform other transformations that improve query execution. Catalyst does not manage external databases, create executor processes, or act as the storage mechanism for cached data. Its role is centered on optimizing structured queries.

Question 158.

Which optimization allows Spark to avoid reading unnecessary columns from a columnar data source?

1. Predicate pushdown
   2. Column pruning
   3. Checkpointing
   4. Repartitioning

Correct Answer: 2. Column pruning

Explanation:

Column pruning allows Spark to read only the columns required by a query instead of scanning every column in the underlying dataset. This can significantly reduce I/O when using columnar formats such as Parquet. Predicate pushdown focuses on reducing rows by moving filters toward the data source. Checkpointing truncates lineage, while repartitioning changes data distribution. Column pruning is therefore specifically concerned with avoiding unnecessary column reads.

Question 159.

Which optimization attempts to apply filtering as close to the data source as possible?

1. Column aliasing
   2. Broadcast caching
   3. Predicate pushdown
   4. Schema printing

Correct Answer: 3. Predicate pushdown

Explanation:

Predicate pushdown moves supported filter conditions toward the data source so that fewer rows need to be read and processed by Spark. This can substantially improve performance, especially with formats and storage systems that support filtering during scanning. Column pruning reduces unnecessary columns, but predicate pushdown specifically targets rows. Aliasing and schema display do not reduce the amount of source data read.

Question 160.

Which file format is columnar and commonly optimized for analytical workloads in Spark?

1. Plain text
   2. XML
   3. Raw binary
   4. Parquet

Correct Answer: 4. Parquet

Explanation:

Parquet is a columnar storage format widely used with Spark for analytical workloads. Because values are organized by column, Spark can take advantage of features such as column pruning, predicate pushdown, compression, and efficient encoding. Plain text and raw binary formats do not provide the same structured columnar advantages. Parquet is therefore a common choice for large-scale analytics and data-lake processing.