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最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題:

1. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) The code throws an error due to a schema mismatch.
B) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
C) [Row(name='bambi'), Row(name='alladin', age=20)]
D) [Row(name='alladin', age=20)]

2. A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A)

B)

C)

D)

A) result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
B) result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price"))
C) result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))
D) result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))

3. A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A) Use spark.read.json() with the inferSchema option set to true
B) Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
C) Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
D) Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

4. Which UDF implementation calculates the length of strings in a Spark DataFrame?

A) df.withColumn("length", udf(lambda s: len(s), StringType()))
B) df.select(length(col("stringColumn")).alias("length"))
C) df.withColumn("length", spark.udf("len", StringType()))
D) spark.udf.register("stringLength", lambda s: len(s))

5. A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*','_metadata.file_path')
Which change will ensure one record per file?
Options:

A) Add the option lineSep=", " to the text() function
B) Add the option wholetext=False to the text() function
C) Add the option lineSep='\n' to the text() function
D) Add the option wholetext=True to the text() function

問題與答案：