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最新的 Databricks Certification Associate-Developer-Apache-Spark 免費考試真題 (Q143-Q148):

問題 #143
Which of the following describes properties of a shuffle?

• A. A shuffle is one of many actions in Spark.
• B. Shuffles involve only single partitions.
• C. Shuffles belong to a class known as "full transformations".
• D. Operations involving shuffles are never evaluated lazily.
• E. In a shuffle, Spark writes data to disk.

答案：E 解題說明：
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
問題 #144
Which of the following code blocks performs an inner join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively, excluding columns value and storeId from DataFrame transactionsDf and column attributes from DataFrame itemsDf?

• A. 1.transactionsDf <br />2. .drop(col('value'), col('storeId')) <br />3. .join(itemsDf.drop(col('attributes')), col('productId')==col('itemId'))
• B. transactionsDf.drop('value', 'storeId').join(itemsDf.select('attributes'), transactionsDf.productId==itemsDf.itemId)
• C. 1.transactionsDf.createOrReplaceTempView('transactionsDf')
  2.itemsDf.createOrReplaceTempView('itemsDf')
  3.
  4.statement = """
  5.SELECT * FROM transactionsDf
  6.INNER JOIN itemsDf
  7.ON transactionsDf.productId==itemsDf.itemId
  8."""
  9.spark.sql(statement).drop("value", "storeId", "attributes")
• D. 1.transactionsDf.createOrReplaceTempView('transactionsDf')
  2.itemsDf.createOrReplaceTempView('itemsDf')
  3.
  4.spark.sql("SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId").drop("attributes")
• E. transactionsDf.drop("value", "storeId").join(itemsDf.drop("attributes"),
  "transactionsDf.productId==itemsDf.itemId")

答案：C 解題說明：
Explanation
This question offers you a wide variety of answers for a seemingly simple question. However, this variety reflects the variety of ways that one can express a join in PySpark. You need to understand some SQL syntax to get to the correct answer here.
transactionsDf.createOrReplaceTempView('transactionsDf')
itemsDf.createOrReplaceTempView('itemsDf')
statement = """
SELECT * FROM transactionsDf
INNER JOIN itemsDf
ON transactionsDf.productId==itemsDf.itemId
"""
spark.sql(statement).drop("value", "storeId", "attributes")
Correct - this answer uses SQL correctly to perform the inner join and afterwards drops the unwanted columns. This is totally fine. If you are unfamiliar with the triple-quote """ in Python: This allows you to express strings as multiple lines.
transactionsDf <br />drop(col('value'), col('storeId')) <br />join(itemsDf.drop(col('attributes')), col('productId')==col('itemId'))
No, this answer option is a trap, since DataFrame.drop() does not accept a list of Column objects. You could use transactionsDf.drop('value', 'storeId') instead.
transactionsDf.drop("value", "storeId").join(itemsDf.drop("attributes"),
"transactionsDf.productId==itemsDf.itemId")
Incorrect - Spark does not evaluate "transactionsDf.productId==itemsDf.itemId" as a valid join expression.
This would work if it would not be a string.
transactionsDf.drop('value', 'storeId').join(itemsDf.select('attributes'), transactionsDf.productId==itemsDf.itemId) Wrong, this statement incorrectly uses itemsDf.select instead of itemsDf.drop.
transactionsDf.createOrReplaceTempView('transactionsDf')
itemsDf.createOrReplaceTempView('itemsDf')
spark.sql("SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId").drop("attributes") No, here the SQL expression syntax is incorrect. Simply specifying -columnName does not drop a column.
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
問題 #145
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. To optimize I/O performance, Spark stores data on disk in multiple slots.
• D. A Java Virtual Machine (JVM) working as an executor can be considered as a pool of slots for task execution.

答案：D 解題說明：
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
問題 #146
The code block shown below should return a DataFrame with columns transactionsId, predError, value, and f from DataFrame transactionsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.1(2)

• A. 1. select
  2. "transactionId, predError, value, f"
• B. 1. select
  2. ["transactionId", "predError", "value", "f"]
• C. 1. filter
  2. "transactionId", "predError", "value", "f"
• D. 1. where
  2. col("transactionId"), col("predError"), col("value"), col("f")
• E. 1. select
  2. col(["transactionId", "predError", "value", "f"])

答案：B 解題說明：
Explanation
Correct code block:
transactionsDf.select(["transactionId", "predError", "value", "f"])
The DataFrame.select returns specific columns from the DataFrame and accepts a list as its only argument.
Thus, this is the correct choice here. The option using col(["transactionId", "predError",
"value", "f"]) is invalid, since inside col(), one can only pass a single column name, not a list. Likewise, all columns being specified in a single string like "transactionId, predError, value, f" is not valid syntax.
filter and where filter rows based on conditions, they do not control which columns to return.
Static notebook | Dynamic notebook: See test 2
問題 #147
The code block displayed below contains an error. The code block is intended to return all columns of DataFrame transactionsDf except for columns predError, productId, and value. Find the error.
Excerpt of DataFrame transactionsDf:
transactionsDf.select(~col("predError"), ~col("productId"), ~col("value"))

• A. The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value wrapped in the col operator so they should be expressed like drop(col(predError), col(productId), col(value)).
• B. The select operator should be replaced by the drop operator.
• C. The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value as strings.
  (Correct)
• D. The select operator should be replaced with the deselect operator.
• E. The column names in the select operator should not be strings and wrapped in the col operator, so they should be expressed like select(~col(predError), ~col(productId), ~col(value)).

答案：C 解題說明：
Explanation
Correct code block:
transactionsDf.drop("predError", "productId", "value")
Static notebook | Dynamic notebook: See test 1
問題 #148
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