Databricks Associate-Developer-Apache-Spark 試験概要:

Databricks Associate-Developer-Apache-Spark 試験シラバストピック:

Databricks Certified Associate Developer for Apache Spark 3.0 認定 Associate-Developer-Apache-Spark 試験問題：

1. Which of the following code blocks returns a single row from DataFrame transactionsDf?
Full 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.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+-------------+---------+-----+-------+---------+----+

A) transactionsDf.where(col("value").isNull()).select("productId", "storeId").distinct()
B) transactionsDf.filter(col("storeId")==25).select("predError","storeId").distinct()
C) transactionsDf.select("productId", "storeId").where("storeId == 2 OR storeId != 25")
D) transactionsDf.where(col("storeId").between(3,25))
E) transactionsDf.filter((col("storeId")!=25) | (col("productId")==2))

2. The code block displayed below contains an error. The code block should return all rows of DataFrame transactionsDf, but including only columns storeId and predError. Find the error.
Code block:
spark.collect(transactionsDf.select("storeId", "predError"))

A) Instead of collect, collectAsRows needs to be called.
B) Columns storeId and predError need to be represented as a Python list, so they need to be wrapped in brackets ([]).
C) The collect method is not a method of the SparkSession object.
D) Instead of select, DataFrame transactionsDf needs to be filtered using the filter operator.
E) The take method should be used instead of the collect method.

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

A) transactionsDf.distinct("storeId")
B) transactionsDf.filter("storeId").distinct()
C) transactionsDf.select(col("storeId").distinct())
D) transactionsDf.select("storeId").distinct()
(Correct)
E) transactionsDf["storeId"].distinct()

4. Which of the following is one of the big performance advantages that Spark has over Hadoop?

A) Spark achieves great performance by storing data in the DAG format, whereas Hadoop can only use parquet files.
B) Spark achieves performance gains for developers by extending Hadoop's DataFrames with a user-friendly API.
C) Spark achieves higher resiliency for queries since, different from Hadoop, it can be deployed on Kubernetes.
D) Spark achieves great performance by storing data in the HDFS format, whereas Hadoop can only use parquet files.
E) Spark achieves great performance by storing data and performing computation in memory, whereas large jobs in Hadoop require a large amount of relatively slow disk I/O operations.

5. Which of the following code blocks reads in the two-partition parquet file stored at filePath, making sure all columns are included exactly once even though each partition has a different schema?
Schema of first partition:
1.root
2. |-- transactionId: integer (nullable = true)
3. |-- predError: integer (nullable = true)
4. |-- value: integer (nullable = true)
5. |-- storeId: integer (nullable = true)
6. |-- productId: integer (nullable = true)
7. |-- f: integer (nullable = true)
Schema of second partition:
1.root
2. |-- transactionId: integer (nullable = true)
3. |-- predError: integer (nullable = true)
4. |-- value: integer (nullable = true)
5. |-- storeId: integer (nullable = true)
6. |-- rollId: integer (nullable = true)
7. |-- f: integer (nullable = true)
8. |-- tax_id: integer (nullable = false)

A) spark.read.parquet(filePath, mergeSchema='y')
B) spark.read.option("mergeSchema", "true").parquet(filePath)
C) 1.nx = 0
2.for file in dbutils.fs.ls(filePath):
3. if not file.name.endswith(".parquet"):
4. continue
5. df_temp = spark.read.parquet(file.path)
6. if nx == 0:
7. df = df_temp
8. else:
9. df = df.union(df_temp)
10. nx = nx+1
11.df
D) spark.read.parquet(filePath)
E) 1.nx = 0
2.for file in dbutils.fs.ls(filePath):
3. if not file.name.endswith(".parquet"):
4. continue
5. df_temp = spark.read.parquet(file.path)
6. if nx == 0:
7. df = df_temp
8. else:
9. df = df.join(df_temp, how="outer")
10. nx = nx+1
11.df

質問と回答：