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The Databricks Certified Associate Developer for Apache Spark 3.0 certification exam is a professional-level exam that is designed to test the skills and knowledge of software developers who work with Apache Spark. This certification is offered by Databricks, which is a leading provider of cloud-based data analytics and machine learning platforms. The certification is an excellent way for software developers to demonstrate their proficiency in using Apache Spark to build data-intensive applications and analytics systems.

The Databricks Associate-Developer-Apache-Spark exam covers a range of topics related to Apache Spark, including data frames, SQL, streaming, machine learning, and graph processing. To prepare for the exam, candidates are recommended to have a solid understanding of Spark fundamentals, including programming in Scala or Python, Spark architecture, and Spark data structures. Additionally, candidates should have hands-on experience working with Spark to be able to apply their knowledge to real-world scenarios.

Databricks Associate-Developer-Apache-Spark is a certification exam offered by Databricks, a cloud-based platform for data engineering, data science, and machine learning. The certification is designed for developers who work with Apache Spark and have a solid understanding of the fundamentals of Spark. The exam tests the candidate’s ability to implement data processing and data analysis using Spark.

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
Which of the following code blocks returns a DataFrame where columns predError and productId are removed from DataFrame transactionsDf?
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.+————-+———+—–+——-+———+—-+

A. transactionsDf.drop(“predError”, “productId”, “associateId”)
B. transactionsDf.withColumnRemoved(“predError”, “productId”)
C. transactionsDf.drop(col(“predError”, “productId”))
D. transactionsDf.drop([“predError”, “productId”, “associateId”])
E. transactionsDf.dropColumns(“predError”, “productId”, “associateId”)

Answer: E

Explanation:
Explanation
The key here is to understand that columns that are passed to DataFrame.drop() are ignored if they do not exist in the DataFrame. So, passing column name associateId to transactionsDf.drop() does not have any effect.
Passing a list to transactionsDf.drop() is not valid. The documentation (link below) shows the call structure as DataFrame.drop(*cols). The * means that all arguments that are passed to DataFrame.drop() are read as columns. However, since a list of columns, for example [“predError”,
“productId”, “associateId”] is not a column, Spark will run into an error.
More info: pyspark.sql.DataFrame.drop – PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 169
Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
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.+————-+———+—–+——-+———+—-+

A. transactionsDf.drop(value, predError)
B. transactionsDf.drop(“predError”, “value”)
C. transactionsDf.drop([“predError”, “value”])
D. transactionsDf.drop(col(“value”), col(“predError”))
E. transactionsDf.drop([col(“predError”), col(“value”)])

Answer: B

Explanation:
Explanation
Output of correct code block:
+————-+——-+———+—-+
|transactionId|storeId|productId| f|
+————-+——-+———+—-+
| 1| 25| 1|null|
| 2| 2| 2|null|
| 3| 25| 3|null|
+————-+——-+———+—-+
To solve this question, you should be fmailiar with the drop() API. The order of column names does not matter
– in this question the order differs in some answers just to confuse you. Also, drop() does not take a list. The *cols operator in the documentation means that all arguments passed to drop() are interpreted as column names.
More info: pyspark.sql.DataFrame.drop – PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 170
Which of the following is a problem with using accumulators?

A. Accumulator values can only be read by the driver, but not by executors.
B. Only numeric values can be used in accumulators.
C. Only unnamed accumulators can be inspected in the Spark UI.
D. Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
E. Accumulators do not obey lazy evaluation.

Answer: A

Explanation:
Explanation
Accumulator values can only be read by the driver, but not by executors.
Correct. So, for example, you cannot use an accumulator variable for coordinating workloads between executors. The typical, canonical, use case of an accumulator value is to report data, for example for debugging purposes, back to the driver. For example, if you wanted to count values that match a specific condition in a UDF for debugging purposes, an accumulator provides a good way to do that.
Only numeric values can be used in accumulators.
No. While pySpark’s Accumulator only supports numeric values (think int and float), you can define accumulators for custom types via the AccumulatorParam interface (documentation linked below).
Accumulators do not obey lazy evaluation.
Incorrect – accumulators do obey lazy evaluation. This has implications in practice: When an accumulator is encapsulated in a transformation, that accumulator will not be modified until a subsequent action is run.
Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
Wrong. A concern with accumulators is in fact that under certain conditions they can run for each task more than once. For example, if a hardware failure occurs during a task after an accumulator variable has been increased but before a task has finished and Spark launches the task on a different worker in response to the failure, already executed accumulator variable increases will be repeated.
Only unnamed accumulators can be inspected in the Spark UI.
No. Currently, in PySpark, no accumulators can be inspected in the Spark UI. In the Scala interface of Spark, only named accumulators can be inspected in the Spark UI.
More info: Aggregating Results with Spark Accumulators | Sparkour, RDD Programming Guide – Spark 3.1.2 Documentation, pyspark.Accumulator – PySpark 3.1.2 documentation, and pyspark.AccumulatorParam – PySpark 3.1.2 documentation

NEW QUESTION # 171
Which of the following statements about garbage collection in Spark is incorrect?

A. In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
B. Garbage collection information can be accessed in the Spark UI’s stage detail view.
C. Optimizing garbage collection performance in Spark may limit caching ability.
D. Serialized caching is a strategy to increase the performance of garbage collection.
E. Manually persisting RDDs in Spark prevents them from being garbage collected.

Answer: E

Explanation:
Explanation
Manually persisting RDDs in Spark prevents them from being garbage collected.
This statement is incorrect, and thus the correct answer to the question. Spark’s garbage collector will remove even persisted objects, albeit in an “LRU” fashion. LRU stands for least recently used.
So, during a garbage collection run, the objects that were used the longest time ago will be garbage collected first.
See the linked StackOverflow post below for more information.
Serialized caching is a strategy to increase the performance of garbage collection.
This statement is correct. The more Java objects Spark needs to collect during garbage collection, the longer it takes. Storing a collection of many Java objects, such as a DataFrame with a complex schema, through serialization as a single byte array thus increases performance. This means that garbage collection takes less time on a serialized DataFrame than an unserialized DataFrame.
Optimizing garbage collection performance in Spark may limit caching ability.
This statement is correct. A full garbage collection run slows down a Spark application. When taking about
“tuning” garbage collection, we mean reducing the amount or duration of these slowdowns.
A full garbage collection run is triggered when the Old generation of the Java heap space is almost full. (If you are unfamiliar with this concept, check out the link to the Garbage Collection Tuning docs below.) Thus, one measure to avoid triggering a garbage collection run is to prevent the Old generation share of the heap space to be almost full.
To achieve this, one may decrease its size. Objects with sizes greater than the Old generation space will then be discarded instead of cached (stored) in the space and helping it to be “almost full”.
This will decrease the number of full garbage collection runs, increasing overall performance.
Inevitably, however, objects will need to be recomputed when they are needed. So, this mechanism only works when a Spark application needs to reuse cached data as little as possible.
Garbage collection information can be accessed in the Spark UI’s stage detail view.
This statement is correct. The task table in the Spark UI’s stage detail view has a “GC Time” column, indicating the garbage collection time needed per task.
In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
This statement is correct. The G1 garbage collector, also known as garbage first garbage collector, is an alternative to the default Parallel garbage collector.
While the default Parallel garbage collector divides the heap into a few static regions, the G1 garbage collector divides the heap into many small regions that are created dynamically. The G1 garbage collector has certain advantages over the Parallel garbage collector which improve performance particularly for Spark workloads that require high throughput and low latency.
The G1 garbage collector is not enabled by default, and you need to explicitly pass an argument to Spark to enable it. For more information about the two garbage collectors, check out the Databricks article linked below.

NEW QUESTION # 172
Which of the following statements about broadcast variables is correct?

A. Broadcast variables are occasionally dynamically updated on a per-task basis.
B. Broadcast variables are immutable.
C. Broadcast variables are serialized with every single task.
D. Broadcast variables are local to the worker node and not shared across the cluster.
E. Broadcast variables are commonly used for tables that do not fit into memory.

Answer: B

Explanation:
Explanation
Broadcast variables are local to the worker node and not shared across the cluster.
This is wrong because broadcast variables are meant to be shared across the cluster. As such, they are never just local to the worker node, but available to all worker nodes.
Broadcast variables are commonly used for tables that do not fit into memory.
This is wrong because broadcast variables can only be broadcast because they are small and do fit into memory.
Broadcast variables are serialized with every single task.
This is wrong because they are cached on every machine in the cluster, precisely avoiding to have to be serialized with every single task.
Broadcast variables are occasionally dynamically updated on a per-task basis.
This is wrong because broadcast variables are immutable – they are never updated.
More info: Spark – The Definitive Guide, Chapter 14

NEW QUESTION # 173
……

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Pass Guaranteed Quiz 2023 Associate-Developer-Apache-Spark: Efficient Reliable Databricks Certified Associate Developer for Apache Spark 3.0 Exam Study Notes

Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q168-Q173):

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