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

1. Which of the following statements about RDDs is incorrect?

A) The high-level DataFrame API is built on top of the low-level RDD API.
B) RDD stands for Resilient Distributed Dataset.
C) RDDs are immutable.
D) RDDs are great for precisely instructing Spark on how to do a query.
E) An RDD consists of a single partition.

2. The code block displayed below contains an error. When the code block below has executed, it should have divided DataFrame transactionsDf into 14 parts, based on columns storeId and transactionDate (in this order). Find the error.
Code block:
transactionsDf.coalesce(14, ("storeId", "transactionDate"))

A) Operator coalesce needs to be replaced by repartition, the parentheses around the column names need to be removed, and .select() needs to be appended to the code block.
B) Operator coalesce needs to be replaced by repartition.
C) Operator coalesce needs to be replaced by repartition and the parentheses around the column names need to be replaced by square brackets.
D) Operator coalesce needs to be replaced by repartition, the parentheses around the column names need to be removed, and .count() needs to be appended to the code block.
(Correct)
E) The parentheses around the column names need to be removed and .select() needs to be appended to the code block.

3. Which of the following code blocks returns the number of unique values in column storeId of DataFrame transactionsDf?

A) transactionsDf.select(count("storeId")).dropDuplicates()
B) transactionsDf.select(distinct("storeId")).count()
C) transactionsDf.select("storeId").dropDuplicates().count()
D) transactionsDf.distinct().select("storeId").count()
E) transactionsDf.dropDuplicates().agg(count("storeId"))

4. Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

A) transactionsDf.cache()
B) transactionsDf.clear_persist()
C) transactionsDf.persist()
D) from pyspark import StorageLevel
transactionsDf.persist(StorageLevel.MEMORY_ONLY)
E) transactionsDf.storage_level('MEMORY_ONLY')
F) from pyspark import StorageLevel
transactionsDf.cache(StorageLevel.MEMORY_ONLY)

5. Which of the following code blocks reads in the JSON file stored at filePath, enforcing the schema expressed in JSON format in variable json_schema, shown in the code block below?
Code block:
1.json_schema = """
2.{"type": "struct",
3. "fields": [
4. {
5. "name": "itemId",
6. "type": "integer",
7. "nullable": true,
8. "metadata": {}
9. },
10. {
11. "name": "supplier",
12. "type": "string",
13. "nullable": true,
14. "metadata": {}
15. }
16. ]
17.}
18."""

A) spark.read.schema(json_schema).json(filePath)
1.schema = StructType.fromJson(json.loads(json_schema))
2.spark.read.json(filePath, schema=schema)
B) spark.read.json(filePath, schema=spark.read.json(json_schema))
C) spark.read.json(filePath, schema=json_schema)
D) spark.read.json(filePath, schema=schema_of_json(json_schema))

問題與答案：

問題 #1 答案： E

問題 #2 答案： D

問題 #3 答案： C

問題 #4 答案： D

問題 #5 答案： A