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下載Databricks Certified Associate Developer for Apache Spark 3.0 Exam考試題庫
NEW QUESTION 26
Which of the following describes characteristics of the Spark driver?
- A. If set in the Spark configuration, Spark scales the Spark driver horizontally to improve parallel processing performance.
- B. The Spark driver processes partitions in an optimized, distributed fashion.
- C. In a non-interactive Spark application, the Spark driver automatically creates the SparkSession object.
- D. The Spark driver's responsibility includes scheduling queries for execution on worker nodes.
- E. The Spark driver requests the transformation of operations into DAG computations from the worker nodes.
Answer: C
Explanation:
Explanation
The Spark driver requests the transformation of operations into DAG computations from the worker nodes.
No, the Spark driver transforms operations into DAG computations itself.
If set in the Spark configuration, Spark scales the Spark driver horizontally to improve parallel processing performance.
No. There is always a single driver per application, but one or more executors.
The Spark driver processes partitions in an optimized, distributed fashion.
No, this is what executors do.
In a non-interactive Spark application, the Spark driver automatically creates the SparkSession object.
Wrong. In a non-interactive Spark application, you need to create the SparkSession object. In an interactive Spark shell, the Spark driver instantiates the object for you.
NEW QUESTION 27
The code block displayed below contains multiple errors. The code block should remove column transactionDate from DataFrame transactionsDf and add a column transactionTimestamp in which dates that are expressed as strings in column transactionDate of DataFrame transactionsDf are converted into unix timestamps. Find the errors.
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+----------------+
2.|transactionId|predError|value|storeId|productId| f| transactionDate|
3.+-------------+---------+-----+-------+---------+----+----------------+
4.| 1| 3| 4| 25| 1|null|2020-04-26 15:35|
5.| 2| 6| 7| 2| 2|null|2020-04-13 22:01|
6.| 3| 3| null| 25| 3|null|2020-04-02 10:53|
7.+-------------+---------+-----+-------+---------+----+----------------+ Code block:
1.transactionsDf = transactionsDf.drop("transactionDate")
2.transactionsDf["transactionTimestamp"] = unix_timestamp("transactionDate", "yyyy-MM-dd")
- A. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment.
- B. Column transactionDate should be dropped after transactionTimestamp has been written. The string indicating the date format should be adjusted. The withColumn operator should be used instead of the existing column assignment. Operator to_unixtime() should be used instead of unix_timestamp().
- C. The string indicating the date format should be adjusted. The withColumnReplaced operator should be used instead of the drop and assign pattern in the code block to replace column transactionDate with the new column transactionTimestamp.
- D. Column transactionDate should be dropped after transactionTimestamp has been written. The withColumn operator should be used instead of the existing column assignment. Column transactionDate should be wrapped in a col() operator.
- E. Column transactionDate should be wrapped in a col() operator.
Answer: A
Explanation:
Explanation
This question requires a lot of thinking to get right. For solving it, you may take advantage of the digital notepad that is provided to you during the test. You have probably seen that the code block includes multiple errors. In the test, you are usually confronted with a code block that only contains a single error. However, since you are practicing here, this challenging multi-error question will make it easier for you to deal with single-error questions in the real exam.
You can clearly see that column transactionDate should be dropped only after transactionTimestamp has been written. This is because to generate column transactionTimestamp, Spark needs to read the values from column transactionDate.
Values in column transactionDate in the original transactionsDf DataFrame look like 2020-04-26 15:35. So, to convert those correctly, you would have to pass yyyy-MM-dd HH:mm. In other words:
The string indicating the date format should be adjusted.
While you might be tempted to change unix_timestamp() to to_unixtime() (in line with the from_unixtime() operator), this function does not exist in Spark. unix_timestamp() is the correct operator to use here.
Also, there is no DataFrame.withColumnReplaced() operator. A similar operator that exists is DataFrame.withColumnRenamed().
Whether you use col() or not is irrelevant with unix_timestamp() - the command is fine with both.
Finally, you cannot assign a column like transactionsDf["columnName"] = ... in Spark. This is Pandas syntax (Pandas is a popular Python package for data analysis), but it is not supported in Spark.
So, you need to use Spark's DataFrame.withColumn() syntax instead.
More info: pyspark.sql.functions.unix_timestamp - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3
NEW QUESTION 28
Which of the following is the idea behind dynamic partition pruning in Spark?
- A. Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution.
- B. Dynamic partition pruning concatenates columns of similar data types to optimize join performance.
- C. Dynamic partition pruning performs wide transformations on disk instead of in memory.
- D. Dynamic partition pruning is intended to skip over the data you do not need in the results of a query.
- E. Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables.
Answer: D
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 29
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