Exam Certified Associate Developer for Apache Spark topic 1 question 85 discussion

D is the correct answer because the code creates a Dataset, not a DataFrame, and needs .toDF() to convert it. B is incorrect because the type is inferred automatically by Spark and you do not need to specify IntegerType explicitly.

In Scala with Spark, createDataset() creates a Dataset rather than a DataFrame. If we want a DataFrame instead of a Dataset, we need to call toDF().

It should be D. Scala has a createDataset function which returns a dataset - where then toDF has to be called. Doc: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/sql/Dataset.html

Correct answer is D: spark.createDataset() creates a Dataset, not a DataFrame. To convert a Dataset to a DataFrame, you use toDF().

Since this is a scala question, the correct syntax would be : spark.createDataset(years).toDF("year") but that isn't one of the options

Official Databricks tests (where answer is A) Question 44 Which of the following code blocks creates a single-column DataFrame from Scala Listyears which is made up of integers? A. spark.createDataset(years).toDF B. spark.createDataFrame(years, IntegerType) C. spark.createDataset(years) D. spark.DataFrame(years, IntegerType) E. spark.createDataFrame(years)

C. There is no operation createDataset – the createDataFrame operation should be used instead. The correct method to create a DataFrame in Spark using Scala is createDataFrame, not createDataset. The correct syntax would be: scala Copy code val df = spark.createDataFrame(years.map(Tuple1.apply)).toDF("columnName") This assumes that years is a List of integers, and the resulting DataFrame will have a single column named "columnName".

C is the answer