Free Associate-Developer-Apache-Spark Questions for Databricks Associate-Developer-Apache-Spark Exam

Question #1

Which of the following describes a difference between Spark's cluster and client execution modes?

A. In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client mode.

B. In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.

C. In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.

D. In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.

E. In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #2

The code block displayed below contains an error. The code block should use Python method find_most_freq_letter to find the letter present most in column itemName of DataFrame itemsDf and return it in a new column most_frequent_letter. Find the error.
Code block:
1. find_most_freq_letter_udf = udf(find_most_freq_letter)
2. itemsDf.withColumn("most_frequent_letter", find_most_freq_letter("itemName"))

A. UDFs do not exist in PySpark.

B. Spark is not adding a column.

C. The "itemName" expression should be wrapped in col().

D. Spark is not using the UDF method correctly.

E. The UDF method is not registered correctly, since the return type is missing.

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #3

Which of the following code blocks returns only rows from DataFrame transactionsDf in which values in column productId are unique?

A. transactionsDf.unique("productId")

B. transactionsDf.distinct("productId")

C. transactionsDf.dropDuplicates(subset="productId")

D. transactionsDf.drop_duplicates(subset="productId")

E. transactionsDf.dropDuplicates(subset=["productId"])

Discussion 0

Correct Answer: E Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #4

Which of the following code blocks returns approximately 1000 rows, some of them potentially being duplicates, from the 2000-row DataFrame transactionsDf that only has unique rows?

A. transactionsDf.sample(True, 0.5, force=True)

B. transactionsDf.take(1000)

C. transactionsDf.sample(False, 0.5)

D. transactionsDf.take(1000).distinct()

E. transactionsDf.sample(True, 0.5)

Discussion 0

Correct Answer: E Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #5

Which of the following describes tasks?

A. A task is a command sent from the driver to the executors in response to a transformation.

B. A task is a collection of rows.

C. Tasks transform jobs into DAGs.

D. A task is a collection of slots.

E. Tasks get assigned to the executors by the driver.

Discussion 0

Correct Answer: E Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #6

Which of the following code blocks returns a one-column DataFrame for which every row contains an array of all integer numbers from 0 up to and including the number given in column predError of DataFrame transactionsDf, and null if predError is null?
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+-------------+---------+-----+-------+---------+----+

A. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = [range(target)]
6. return result
7.
8.count_to_target_udf = udf(count_to_target, ArrayType[IntegerType])
9.
10.transactionsDf.select(count_to_target_udf(col('predError')))

B. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
9.
10.transactionsDf.select(count_to_target_udf('predError'))
(Correct)

C. 1.def count_to_target(target):
2. result = list(range(target))
3. return result
4.
5.count_to_target_udf = udf(count_to_target, ArrayType(IntegerType()))
6.
7.df = transactionsDf.select(count_to_target_udf('predError'))

D. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.transactionsDf.select(count_to_target(col('predError')))

E. 1.def count_to_target(target):
2. if target is None:
3. return
4.
5. result = list(range(target))
6. return result
7.
8.count_to_target_udf = udf(count_to_target)
9.
10.transactionsDf.select(count_to_target_udf('predError'))

Discussion 0

Correct Answer: B Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #7

Which of the following describes a shuffle?

A. A shuffle is a process that is executed during a broadcast hash join.

B. A shuffle is a process that allocates partitions to executors.

C. A shuffle is a process that compares data across partitions.

D. A shuffle is a Spark operation that results from DataFrame.coalesce().

E. A shuffle is a process that compares data across executors.

Discussion 0

Correct Answer: C Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #8

The code block displayed below contains an error. The code block should trigger Spark to cache DataFrame transactionsDf in executor memory where available, writing to disk where insufficient executor memory is available, in a fault-tolerant way. Find the error.
Code block:
transactionsDf.persist(StorageLevel.MEMORY_AND_DISK)

A. The code block uses the wrong operator for caching.

B. The DataFrameWriter needs to be invoked.

C. Data caching capabilities can be accessed through the spark object, but not through the DataFrame API.

D. Caching is not supported in Spark, data are always recomputed.

E. The storage level is inappropriate for fault-tolerant storage.

Discussion 0

Correct Answer: E Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #9

Which of the following statements about broadcast variables is correct?

A. Broadcast variables are local to the worker node and not shared across the cluster.

B. Broadcast variables are serialized with every single task.

C. Broadcast variables are immutable.

D. Broadcast variables are commonly used for tables that do not fit into memory.

E. Broadcast variables are occasionally dynamically updated on a per-task basis.

Discussion 0

Correct Answer: C Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #10

Which of the following code blocks creates a new 6-column DataFrame by appending the rows of the
6-column DataFrame yesterdayTransactionsDf to the rows of the 6-column DataFrame todayTransactionsDf, ignoring that both DataFrames have different column names?

A. todayTransactionsDf.unionByName(yesterdayTransactionsDf)

B. union(todayTransactionsDf, yesterdayTransactionsDf)

C. todayTransactionsDf.unionByName(yesterdayTransactionsDf, allowMissingColumns=True)

D. todayTransactionsDf.union(yesterdayTransactionsDf)

E. todayTransactionsDf.concat(yesterdayTransactionsDf)

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #11

Which of the following describes characteristics of the Spark driver?

A. The Spark driver processes partitions in an optimized, distributed fashion.

B. The Spark driver's responsibility includes scheduling queries for execution on worker nodes.

C. The Spark driver requests the transformation of operations into DAG computations from the worker nodes.

D. In a non-interactive Spark application, the Spark driver automatically creates the SparkSession object.

E. If set in the Spark configuration, Spark scales the Spark driver horizontally to improve parallel processing performance.

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Question #12

The code block shown below should write DataFrame transactionsDf as a parquet file to path storeDir, using brotli compression and replacing any previously existing file. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.format("parquet").__2__(__3__).option(__4__, "brotli").__5__(storeDir)

A. 1. store
2. with
3. "replacement"
4. "compression"
5. path

B. 1. save
2. mode
3. "replace"
4. "compression"
5. path

C. 1. write
2. mode
3. "overwrite"
4. "compression"
5. save
(Correct)

D. 1. save
2. mode
3. "ignore"
4. "compression"
5. path

E. 1. write
2. mode
3. "overwrite"
4. compression
5. parquet

Discussion 0

Correct Answer: B Vote an answer

Explanation: Only visible for ExamDiscuss members. You can sign-up / login (it's free).

Free Associate-Developer-Apache-Spark Questions for Databricks Certified Associate Developer for Apache Spark 3.0 Associate-Developer-Apache-Spark Exam as PDF & Practice Test Engine

Download Free Databricks Associate-Developer-Apache-Spark Demo