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最新的 Databricks Certification Associate-Developer-Apache-Spark 免費考試真題:

1. In which order should the code blocks shown below be run in order to assign articlesDf a DataFrame that lists all items in column attributes ordered by the number of times these items occur, from most to least often?
Sample of DataFrame articlesDf:
1.+------+-----------------------------+-------------------+
2.|itemId|attributes |supplier |
3.+------+-----------------------------+-------------------+
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
7.+------+-----------------------------+-------------------+

A) 1. articlesDf = articlesDf.groupby("col")
2. articlesDf = articlesDf.select(explode(col("attributes")))
3. articlesDf = articlesDf.orderBy("count").select("col")
4. articlesDf = articlesDf.sort("count",ascending=False).select("col")
5. articlesDf = articlesDf.groupby("col").count()
B) 5, 2
C) 2, 5, 4
D) 2, 3, 4
E) 2, 5, 3
F) 4, 5

2. Which of the following code blocks returns approximately 1000 rows, some of them potentially being duplicates, from the 2000-row DataFrame transactionsDf that only has unique rows?

A) transactionsDf.sample(True, 0.5)
B) transactionsDf.take(1000).distinct()
C) transactionsDf.sample(True, 0.5, force=True)
D) transactionsDf.sample(False, 0.5)
E) transactionsDf.take(1000)

3. Which of the following code blocks creates a new one-column, two-row DataFrame dfDates with column date of type timestamp?

A) 1.dfDates = spark.createDataFrame(["23/01/2022 11:28:12","24/01/2022 10:58:34"], ["date"])
2.dfDates = dfDates.withColumnRenamed("date", to_datetime("date", "yyyy-MM-dd HH:mm:ss"))
B) 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])
2.dfDates = dfDates.withColumnRenamed("date", to_timestamp("date", "yyyy-MM-dd HH:mm:ss"))
C) 1.dfDates = spark.createDataFrame(["23/01/2022 11:28:12","24/01/2022 10:58:34"], ["date"])
2.dfDates = dfDates.withColumn("date", to_timestamp("dd/MM/yyyy HH:mm:ss", "date"))
D) 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])
2.dfDates = dfDates.withColumn("date", to_timestamp("date", "dd/MM/yyyy HH:mm:ss"))
E) 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])

4. Which of the following describes Spark's Adaptive Query Execution?

A) Adaptive Query Execution features are dynamically switching join strategies and dynamically optimizing skew joins.
B) Adaptive Query Execution reoptimizes queries at execution points.
C) Adaptive Query Execution applies to all kinds of queries.
D) Adaptive Query Execution is enabled in Spark by default.
E) Adaptive Query Execution features include dynamically coalescing shuffle partitions, dynamically injecting scan filters, and dynamically optimizing skew joins.

5. Which of the following is a characteristic of the cluster manager?

A) Each cluster manager works on a single partition of data.
B) In client mode, the cluster manager runs on the edge node.
C) The cluster manager receives input from the driver through the SparkContext.
D) The cluster manager does not exist in standalone mode.
E) The cluster manager transforms jobs into DAGs.

問題與答案：