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The Databricks Associate-Developer-Apache-Spark Certification Exam is an online exam that can be taken from anywhere in the world. The exam is proctored and requires candidates to have a stable internet connection and a webcam. The exam consists of multiple-choice questions and coding exercises that test a candidate's knowledge and skills in Spark application development. The exam is timed, and candidates have a limited amount of time to complete each section.
NEW QUESTION # 17
Which of the following describes the role of the cluster manager?
Answer: A
Explanation:
Explanation
The cluster manager allocates resources to Spark applications and maintains the executor processes in client mode.
Correct. In cluster mode, the cluster manager is located on a node other than the client machine. From there it starts and ends executor processes on the cluster nodes as required by the Spark application running on the Spark driver.
The cluster manager allocates resources to Spark applications and maintains the executor processes in remote mode.
Wrong, there is no "remote" execution mode in Spark. Available execution modes are local, client, and cluster.
The cluster manager allocates resources to the DataFrame manager
Wrong, there is no "DataFrame manager" in Spark.
The cluster manager schedules tasks on the cluster in client mode.
No, in client mode, the Spark driver schedules tasks on the cluster - not the cluster manager.
The cluster manager schedules tasks on the cluster in local mode.
Wrong: In local mode, there is no "cluster". The Spark application is running on a single machine, not on a cluster of machines.
NEW QUESTION # 18
Which of the following options describes the responsibility of the executors in Spark?
Answer: E
Explanation:
Explanation
More info: Running Spark: an overview of Spark's runtime architecture - Manning (https://bit.ly/2RPmJn9)
NEW QUESTION # 19
Which of the following describes properties of a shuffle?
Answer: A
Explanation:
Explanation
In a shuffle, Spark writes data to disk.
Correct! Spark's architecture dictates that intermediate results during a shuffle are written to disk.
A shuffle is one of many actions in Spark.
Incorrect. A shuffle is a transformation, but not an action.
Shuffles involve only single partitions.
No, shuffles involve multiple partitions. During a shuffle, Spark generates output partitions from multiple input partitions.
Operations involving shuffles are never evaluated lazily.
Wrong. A shuffle is a costly operation and Spark will evaluate it as lazily as other transformations. This is, until a subsequent action triggers its evaluation.
Shuffles belong to a class known as "full transformations".
Not quite. Shuffles belong to a class known as "wide transformations". "Full transformation" is not a relevant term in Spark.
More info: Spark - The Definitive Guide, Chapter 2 and Spark: disk I/O on stage boundaries explanation - Stack Overflow
NEW QUESTION # 20
Which of the following is the idea behind dynamic partition pruning in Spark?
Answer: C
Explanation:
Explanation
Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution.
No - this is what adaptive query execution does, but not dynamic partition pruning.
Dynamic partition pruning concatenates columns of similar data types to optimize join performance.
Wrong, this answer does not make sense, especially related to dynamic partition pruning.
Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables.
It is true that dynamic partition pruning works in joins using broadcast variables. This actually happens in both the logical optimization and the physical planning stage. However, data types do not play a role for the reoptimization.
Dynamic partition pruning performs wide transformations on disk instead of in memory.
This answer does not make sense. Dynamic partition pruning is meant to accelerate Spark - performing any transformation involving disk instead of memory resources would decelerate Spark and certainly achieve the opposite effect of what dynamic partition pruning is intended for.
NEW QUESTION # 21
In which order should the code blocks shown below be run in order to create a DataFrame that shows the mean of column predError of DataFrame transactionsDf per column storeId and productId, where productId should be either 2 or 3 and the returned DataFrame should be sorted in ascending order by column storeId, leaving out any nulls in that column?
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.+-------------+---------+-----+-------+---------+----+
1. .mean("predError")
2. .groupBy("storeId")
3. .orderBy("storeId")
4. transactionsDf.filter(transactionsDf.storeId.isNotNull())
5. .pivot("productId", [2, 3])
Answer: C
Explanation:
Explanation
Correct code block:
transactionsDf.filter(transactionsDf.storeId.isNotNull()).groupBy("storeId").pivot("productId", [2,
3]).mean("predError").orderBy("storeId")
Output of correct code block:
+-------+----+----+
|storeId| 2| 3|
+-------+----+----+
| 2| 6.0|null|
| 3|null|null|
| 25| 3.0| 3.0|
+-------+----+----+
This question is quite convoluted and requires you to think hard about the correct order of operations.
The pivot method also makes an appearance - a method that you may not know all that much about (yet).
At the first position in all answers is code block 4, so the question is essentially just about the ordering of the remaining 4 code blocks.
The question states that the returned DataFrame should be sorted by column storeId. So, it should make sense to have code block 3 which includes the orderBy operator at the very end of the code block. This leaves you with only two answer options.
Now, it is useful to know more about the context of pivot in PySpark. A common pattern is groupBy, pivot, and then another aggregating function, like mean. In the documentation linked below you can see that pivot is a method of pyspark.sql.GroupedData - meaning that before pivoting, you have to use groupBy. The only answer option matching this requirement is the one in which code block 2 (which includes groupBy) is stated before code block 5 (which includes pivot).
More info: pyspark.sql.GroupedData.pivot - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION # 22
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