New Year Sale ! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

Databricks Exam Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Topic 2 Question 58 Discussion

Actual exam question for Databricks's Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 exam
Question #: 58
Topic #: 2
[All Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Questions]

The code block shown below should show information about the data type that column storeId of DataFrame transactionsDf contains. Choose the answer that correctly fills the blanks in the code

block to accomplish this.

Code block:

transactionsDf.__1__(__2__).__3__

Show Suggested Answer Hide Answer
Suggested Answer: B

Correct code block:

transactionsDf.select('storeId').printSchema()

The difficulty of this Question: is that it is hard to solve with the stepwise first-to-last-gap approach that has worked well for similar questions, since the answer options are so different from

one

another. Instead, you might want to eliminate answers by looking for patterns of frequently wrong answers.

A first pattern that you may recognize by now is that column names are not expressed in quotes. For this reason, the answer that includes storeId should be eliminated.

By now, you may have understood that the DataFrame.limit() is useful for returning a specified amount of rows. It has nothing to do with specific columns. For this reason, the answer that resolves to

limit('storeId') can be eliminated.

Given that we are interested in information about the data type, you should Question: whether the answer that resolves to limit(1).columns provides you with this information. While

DataFrame.columns is a valid call, it will only report back column names, but not column types. So, you can eliminate this option.

The two remaining options either use the printSchema() or print_schema() command. You may remember that DataFrame.printSchema() is the only valid command of the two. The select('storeId')

part just returns the storeId column of transactionsDf - this works here, since we are only interested in that column's type anyways.

More info: pyspark.sql.DataFrame.printSchema --- PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 3, Question: 57 (Databricks import instructions)


Contribute your Thoughts:

Rosann
5 months ago
Haha, this question is a real brain-teaser! I'm gonna go with E too. 'dtypes' is just what the doctor ordered to diagnose that pesky data type. Plus, 'select' is the way to go when you need to zero in on a specific column. Nailed it!
upvoted 0 times
...
Catarina
5 months ago
This reminds me of that time I accidentally deleted my entire database. Good times, good times. Anyway, I'd say E is the way to go. 'dtypes' is the key to finding the data type, and 'select' is the right method. Gotta love those Spark DataFrame methods!
upvoted 0 times
Rozella
3 months ago
Yeah, E looks good. 'dtypes' is the method to use for checking data types.
upvoted 0 times
...
Ronna
3 months ago
I think E is the right answer too. 'dtypes' is what we need to check the data type.
upvoted 0 times
...
Fletcher
3 months ago
I agree, E seems like the correct choice. 'dtypes' is definitely the key to finding the data type.
upvoted 0 times
...
Ammie
3 months ago
I'm going with E too. 'dtypes' and 'select' seem like the right choices.
upvoted 0 times
...
Anjelica
4 months ago
Yeah, I agree. 'dtypes' is definitely the key to finding the data type.
upvoted 0 times
...
Gennie
4 months ago
I think E is the correct answer. 'dtypes' is used to find the data type and 'select' is the method.
upvoted 0 times
...
...
Kallie
5 months ago
I believe A is incorrect because 'print_schema()' should be 'printSchema()'. So, C is the right answer.
upvoted 0 times
...
Leila
5 months ago
I'm not sure, but I think A could also be a possibility.
upvoted 0 times
...
Twana
5 months ago
I agree with Arlene, C seems like the correct option.
upvoted 0 times
...
Arlene
5 months ago
I think the answer is C.
upvoted 0 times
...
Carry
5 months ago
Ooh, tricky one! I'm leaning towards D. 'printSchema()' is the way to go, and 'limit' might be useful for quick previews, but 'storeId' is the column we need to check. Hmm, let's see what the others think!
upvoted 0 times
...
Latia
5 months ago
I'm going with C. 'printSchema()' is the correct way to print the schema of the DataFrame, and 'select' is the right method to use. Nice and straightforward!
upvoted 0 times
Phyliss
5 months ago
C is the right answer. 'printSchema()' is the method to print the schema and 'select' is used to select the column 'storeId'.
upvoted 0 times
...
Octavio
5 months ago
Great choice! 'printSchema()' is the method to display the schema and 'select' is used to select the column 'storeId'.
upvoted 0 times
...
Tijuana
5 months ago
Yes, C is the correct answer. 'printSchema()' is used to display the schema of the DataFrame and 'select' is used to select the column 'storeId'.
upvoted 0 times
...
Teddy
5 months ago
I'm going with C. 'printSchema()' is the correct way to print the schema of the DataFrame, and 'select' is the right method to use. Nice and straightforward!
upvoted 0 times
...
...
Marge
6 months ago
Hmm, I think it's E. The 'select' method allows you to choose specific columns, 'storeId' is the column we want to check, and 'dtypes' will give us the data type. Easy peasy!
upvoted 0 times
Mabel
5 months ago
'storeId' is the column we want to check, and 'dtypes' will give us the data type. Easy peasy!
upvoted 0 times
...
Valentin
6 months ago
I think it's E. The 'select' method allows you to choose specific columns.
upvoted 0 times
...
...

Save Cancel