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Databricks Certified Associate Developer for Apache Spark 3.0 Exam 認定 Associate-Developer-Apache-Spark 試験問題 (Q167-Q172):

質問 # 167
Which of the following describes slots?

• A. Slots are dynamically created and destroyed in accordance with an executor's workload.
• B. A slot is always limited to a single core.
  Slots are the communication interface for executors and are used for receiving commands and sending results to the driver.
• C. A Java Virtual Machine (JVM) working as an executor can be considered as a pool of slots for task execution.
• D. To optimize I/O performance, Spark stores data on disk in multiple slots.

正解：C 解説：
Explanation
Slots are the communication interface for executors and are used for receiving commands and sending results to the driver.
Wrong, executors communicate with the driver directly.
Slots are dynamically created and destroyed in accordance with an executor's workload.
No, Spark does not actively create and destroy slots in accordance with the workload. Per executor, slots are made available in accordance with how many cores per executor (property spark.executor.cores) and how many CPUs per task (property spark.task.cpus) the Spark configuration calls for.
A slot is always limited to a single core.
No, a slot can span multiple cores. If a task would require multiple cores, it would have to be executed through a slot that spans multiple cores.
In Spark documentation, "core" is often used interchangeably with "thread", although "thread" is the more accurate word. A single physical core may be able to make multiple threads available. So, it is better to say that a slot can span multiple threads.
To optimize I/O performance, Spark stores data on disk in multiple slots.
No - Spark stores data on disk in multiple partitions, not slots.
More info: Spark Architecture | Distributed Systems Architecture
質問 # 168
The code block displayed below contains an error. The code block should count the number of rows that have a predError of either 3 or 6. Find the error.
Code block:
transactionsDf.filter(col('predError').in([3, 6])).count()

• A. The number of rows cannot be determined with the count() operator.
• B. Instead of filter, the select method should be used.
• C. Instead of a list, the values need to be passed as single arguments to the in operator.
• D. The method used on column predError is incorrect.
• E. Numbers 3 and 6 need to be passed as string variables.

正解：D 解説：
Explanation
Correct code block:
transactionsDf.filter(col('predError').isin([3, 6])).count()
The isin method is the correct one to use here - the in method does not exist for the Column object.
More info: pyspark.sql.Column.isin - PySpark 3.1.2 documentation
質問 # 169
Which of the following code blocks returns a copy of DataFrame transactionsDf where the column storeId has been converted to string type?

• A. transactionsDf.withColumn("storeId", col("storeId", "string"))
• B. transactionsDf.withColumn("storeId", col("storeId").cast("string"))
• C. transactionsDf.withColumn("storeId", convert("storeId", "string"))
• D. transactionsDf.withColumn("storeId", convert("storeId").as("string"))
• E. transactionsDf.withColumn("storeId", col("storeId").convert("string"))

正解：B 解説：
Explanation
This question asks for your knowledge about the cast syntax. cast is a method of the Column class. It is worth noting that one could also convert a column type using the Column.astype() method, which is just an alias for cast.
Find more info in the documentation linked below.
More info: pyspark.sql.Column.cast - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
質問 # 170
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("predError", "value")
• B. transactionsDf.drop(["predError", "value"])
• C. transactionsDf.drop([col("predError"), col("value")])
• D. transactionsDf.drop(value, predError)
• E. transactionsDf.drop(col("value"), col("predError"))

正解：A 解説：
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
質問 # 171*
Which of the following is the idea behind dynamic partition pruning in Spark?

• A. Dynamic partition pruning is intended to skip over the data you do not need in the results of a query.
• B. Dynamic partition pruning performs wide transformations on disk instead of in memory.
• C. Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution.
• D. Dynamic partition pruning concatenates columns of similar data types to optimize join performance.
• E. Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables.

正解：A 解説：
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.
質問 # 172
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