Databricks Certified Associate Developer for Apache Spark Exam Dumps & Practice Test Questions
Question No 1:
The code provided below has an issue. The intention is to return a new DataFrame that contains the mean of the "sqft" column from the storesDF DataFrame, placing this mean in a new column called "sqftMean."
What is the error in the code, and how should it be corrected?
A. The argument to the mean() operation should be a Column object rather than a string column name.
B. The argument to the mean() operation should not be quoted.
C. The mean() operation is not a standalone function – it’s a method of the Column object.
D. The agg() operation is not appropriate here – the withColumn() operation should be used instead.
E. The only way to compute a mean of a column is with the mean() method from a DataFrame.
Answer: B
Explanation:
In PySpark, when applying aggregation functions like mean(), the argument passed should refer directly to a column, not a string representing the column name. In the original code, the function mean("sqft") is incorrectly passed the string "sqft", which causes an error. mean() expects a Column object, but a quoted string is being provided. This is a common mistake when working with aggregation functions.
To correct the issue, mean() should reference the column sqft directly without quotes. Instead of passing a string, you can use col() from the pyspark.sql.functions module, or use the DataFrame's column reference directly. The corrected code would look like this:
In this code, col("sqft") returns the Column object that represents the sqft column, which is the correct input for the mean() function.
Why Other Options are Incorrect:
Option A: This option suggests that mean() should only take a Column object. However, the mean() function can accept either a column name or a column reference, provided it's used correctly. The issue isn't with the type of the argument, but with how the column is referenced.
Option C: This is incorrect because mean() is not a method of a Column object. It is a function from the pyspark.sql.functions module and can be used to compute the mean of a column, not a method attached to a Column.
Option D: The agg() function is indeed the correct method for performing aggregation operations like mean(). The withColumn() method is used to add or modify columns, but in this case, we are computing an aggregate value, not modifying the DataFrame directly. Therefore, agg() is the proper method here.
Option E: This option is false because there are multiple ways to compute the mean of a column. You can use agg(), groupBy(), or other operations. The mean() method is not the only approach available for this calculation.
To summarize, the error lies in how the column name sqft is quoted. Aggregation functions like mean() expect a Column object, and quoting the column name turns it into a string, which is invalid. The correct approach is to use col("sqft") or refer to the column directly without quotes.
Question No 2:
Which of the following methods can be used to get the number of rows in a DataFrame in common DataFrame-based libraries (such as Pandas or PySpark)?
A. DataFrame.numberOfRows()
B. DataFrame.n()
C. DataFrame.sum()
D. DataFrame.count()
E. DataFrame.countDistinct()
Answer: D
Explanation:
In the context of data manipulation, particularly with large datasets in libraries like Pandas or PySpark, it's essential to know how to quickly retrieve the number of rows in a DataFrame. This question tests the knowledge of the appropriate function to use for this task.
Option A: DataFrame.numberOfRows()
This is not a valid method in typical DataFrame libraries. Neither Pandas nor PySpark provide a numberOfRows() function. Therefore, this method cannot be used to count the rows of a DataFrame.Option B: DataFrame.n()
The n() method is not a standard method in common libraries like Pandas or PySpark for retrieving the number of rows. While there are custom implementations where n() might be used, it's not a typical function for counting rows in these libraries, which makes this option incorrect.Option C: DataFrame.sum()
The sum() function is used to calculate the sum of values within a DataFrame, not the number of rows. It typically operates along columns (or rows depending on the axis), and does not count rows or return row-specific information. Hence, this is not the correct option.Option D: DataFrame.count()
This is the correct method. In Pandas or PySpark, count() is commonly used to count the number of non-null values in each column. When applied to the entire DataFrame, count() returns the number of non-null entries for each column. If the DataFrame doesn't contain nulls, it can effectively give the number of rows in the DataFrame. Therefore, this is the method you would use to determine the total number of rows in most DataFrame-based libraries.Option E: DataFrame.countDistinct()
The countDistinct() function is used to count the number of distinct (unique) values in a column, not the total number of rows. This method helps identify the diversity of values in a column but doesn't relate to counting rows. As such, this option is also incorrect.
To summarize, DataFrame.count() is the correct method to retrieve the number of rows when working with DataFrames, particularly in libraries like Pandas and PySpark. While it counts non-null entries, it is commonly used to get the total row count when the DataFrame is free from nulls.
Question No 3:
Which operation returns a GroupedData object when applied to a Spark DataFrame?
A. DataFrame.GroupBy()
B. DataFrame.cubed()
C. DataFrame.group()
D. DataFrame.groupBy()
E. DataFrame.grouping_id()
Answer: D
Explanation:
In Apache Spark, when you want to group a DataFrame based on one or more columns, you use the groupBy() method, which returns a GroupedData object. This object allows you to perform various aggregate operations on the grouped data. Let's break down each option and why only one is correct:
Option A: DataFrame.GroupBy()
This method does not exist in Spark's DataFrame API. The correct method for grouping a DataFrame is groupBy(), not GroupBy(). Method names in Spark are case-sensitive, so this option is invalid.Option B: DataFrame.cubed()
The cubed() method is not used for grouping in Spark. Instead, it's a method that allows you to compute multidimensional aggregations on the DataFrame. It is generally used for complex aggregation scenarios (like cube or roll-up), but it doesn't return a GroupedData object. Thus, this option is also incorrect.Option C: DataFrame.group()
There is no group() method in the Spark DataFrame API. Grouping is performed using groupBy(). Therefore, this option is not valid.Option D: DataFrame.groupBy()
This is the correct method for grouping a DataFrame in Spark. When you call groupBy() on a DataFrame, it returns a GroupedData object, which is the result of the grouping operation. This object then allows you to apply various aggregate functions like count(), sum(), avg(), etc., to the grouped data. Therefore, this is the correct answer.Option E: DataFrame.grouping_id()
The grouping_id() method is used to return the grouping ID for a particular GROUP BY query, but it does not return a GroupedData object. It is mainly used in conjunction with aggregate functions to identify the level of grouping but does not perform the grouping itself. Therefore, this option is incorrect.
In conclusion, DataFrame.groupBy() is the correct method that returns a GroupedData object in Spark, allowing users to perform aggregation operations on the grouped data.
Question No 4:
Which of the following code blocks returns a collection of summary statistics for all columns in the DataFrame named storesDF?
A. storesDF.summary("mean")
B. storesDF.describe(all = True)
C. storesDF.describe("all")
D. storesDF.summary("all")
E. storesDF.describe()
Answer: D
Explanation:
In Python, when working with a DataFrame, generating summary statistics is a common task. The two primary functions used for this are summary() and describe(), though their usage can vary depending on the library (e.g., pandas, PySpark).
Here's a breakdown of each option:
A. storesDF.summary("mean")
This code is incorrect. The summary() function does not accept "mean" as a parameter. In the pandas library, summary() is used to generate a collection of statistics, including count, mean, std deviation, etc. However, it does not specifically support arguments like "mean" to focus only on that statistic.
B. storesDF.describe(all = True)
This is incorrect because the describe() function does not take all = True as a valid argument. In pandas, describe() is used to generate summary statistics for numeric columns by default. To include all columns (numeric and non-numeric), you would need to use the include="all" parameter, not all = True.
C. storesDF.describe("all")
This option is close but incorrect. The describe() function accepts the include argument, not a string like "all". To generate summary statistics for all columns, the correct syntax would be storesDF.describe(include="all").
D. storesDF.summary("all")
This is the correct answer. The summary() method in certain libraries like PySpark can take an argument like "all", which returns a full set of summary statistics for all columns in the DataFrame. This syntax is valid in PySpark, though it might not apply to pandas directly.
E. storesDF.describe()
This is the most common approach to get summary statistics, but it does not cover all types of columns. By default, describe() provides statistics for numeric columns only. If you wanted to include non-numeric columns, you'd need to explicitly specify include="all", as mentioned above.
To conclude, D (storesDF.summary("all")) is the correct method for generating a collection of summary statistics for all columns in frameworks like PySpark.
Question No 5:
Which of the following code blocks fails to return a DataFrame that is reverse sorted alphabetically based on the "division" column?
A. storesDF.orderBy("division", ascending=False)
B. storesDF.orderBy(["division"], ascending=[0])
C. storesDF.orderBy(col("division").asc())
D. storesDF.sort("division", ascending=False)
E. storesDF.sort(desc("division"))
Answer: C
Explanation:
The task here is to identify the code block that fails to reverse sort a DataFrame based on the "division" column. Let’s analyze each option:
A. storesDF.orderBy("division", ascending=False)
This code is correct. The orderBy() function is used to sort the DataFrame by the "division" column. By specifying ascending=False, it sorts in descending (reverse alphabetical) order.
B. storesDF.orderBy(["division"], ascending=[0])
This code is also correct. Here, ascending=[0] is equivalent to ascending=[False] in Spark, meaning the sort will be in descending order. The use of a list for both the column name and the ascending argument is a valid syntax.
C. storesDF.orderBy(col("division").asc())
This code is incorrect. The col("division").asc() function explicitly sorts the "division" column in ascending order, which is the opposite of what we need. To reverse sort the column, we would need to use desc() instead of asc(). Thus, this option fails to meet the requirement.
D. storesDF.sort("division", ascending=False)
This is another correct code block. The sort() function is similar to orderBy() and sorts the DataFrame by the "division" column in descending order (reverse alphabetical). It uses ascending=False to indicate descending order.
E. storesDF.sort(desc("division"))
This is a valid approach for reverse sorting. The desc() function sorts the "division" column in descending order. This is another way to ensure the DataFrame is sorted reverse alphabetically.
Options A, B, D, and E all correctly sort the DataFrame in reverse alphabetical order. However, option C is the only one that sorts the "division" column in ascending order, which fails to meet the requirement for reverse sorting. Thus, the correct answer is C.
Question No 6:
Which of the following code blocks correctly returns a 15 percent sample of rows from the DataFrame storesDF without replacement?
A. storesDF.sample(fraction = 0.10)
B. storesDF.sampleBy(fraction = 0.15)
C. storesDF.sample(True, fraction = 0.10)
D. storesDF.sample()
E. storesDF.sample(fraction = 0.15)
Answer: E
Explanation:
In Pandas, the sample() method is used to select a random subset of rows from a DataFrame. This method accepts several parameters that help control how the sampling is done, including the fraction parameter, which defines the proportion of data to sample. Additionally, the method by default performs sampling without replacement unless specified otherwise.
Let's go through each option to explain why E is the correct choice:
Option A: storesDF.sample(fraction = 0.10)
This option specifies a fraction of 0.10, which would return a random sample of 10% of the rows, not 15%. The fraction parameter is correctly used here, but it doesn’t match the desired 15% sample.
Option B: storesDF.sampleBy(fraction = 0.15)
This option is incorrect because sampleBy() is not a valid method in Pandas. It’s a function used in PySpark for stratified sampling, not for simple random sampling in Pandas. Therefore, this option is not relevant to the question.
Option C: storesDF.sample(True, fraction = 0.10)
This is an incorrect syntax. The True here seems to be an attempt to indicate that the sampling should be done with replacement, but the correct argument for sampling with replacement is replace=True, not just True. Additionally, the fraction is set to 0.10, which is not what we need.
Option D: storesDF.sample()
This option is incomplete. The sample() function is used here without specifying the fraction argument or whether sampling is to be done with replacement or without it. By default, sample() would return a single row from the DataFrame (100% of one row), which is not the correct behavior for this question.
Option E: storesDF.sample(fraction = 0.15)
This is the correct option. The sample() method is used with the fraction=0.15 argument, which correctly samples 15% of the rows from the DataFrame. Since the replace parameter is not specified, it defaults to False, meaning the sampling will be done without replacement. This matches the requirements of the question perfectly.
In conclusion, E is the correct answer, as it correctly uses the sample() method to return a 15% random sample of the rows from the DataFrame without replacement.
Question No 7:
Which of the following methods will return all the rows from a DataFrame called storesDF in a PySpark environment?
A. storesDF.head()
B. storesDF.collect()
C. storesDF.count()
D. storesDF.take()
E. storesDF.show()
Answer: B
Explanation:
In a PySpark environment, DataFrames are distributed across multiple nodes in a cluster, and to interact with the data, it’s important to understand how to retrieve all the rows from a DataFrame. Let's break down each option and clarify why B is the correct choice.
Option A: storesDF.head()
The head() method in PySpark returns only the first row (or a limited number of rows if specified). It is typically used when you want a quick look at the first element of the DataFrame, but it does not retrieve all rows. If you want to get the entire DataFrame, head() is not the right choice.
Why it's incorrect: head() does not return all the rows, only the first row.
Option B: storesDF.collect()
The collect() method is the correct choice for retrieving all the rows from a PySpark DataFrame. It gathers all the rows from all the distributed partitions and brings them to the local machine as a list of Row objects. While this method is effective in retrieving all data, it should be used carefully, especially with large datasets, because it can cause memory issues by trying to load everything into the driver machine's memory.
Why it's correct: collect() gathers all rows from the distributed environment and returns them as a list, allowing you to process all the data locally.
Option C: storesDF.count()
The count() method in PySpark returns the number of rows in the DataFrame, not the data itself. It’s useful for getting the size of the DataFrame but does not provide access to the data.
Why it's incorrect: count() only returns the number of rows, not the actual data.
Option D: storesDF.take()
The take(n) method retrieves the first n rows of the DataFrame. While you can specify how many rows to return, it still does not retrieve all rows unless you specify a number equal to the total number of rows in the DataFrame, which is not practical. It's more commonly used to preview a subset of the DataFrame.
Why it's incorrect: take() only returns the first n rows, not all rows.
Option E: storesDF.show()
The show() method displays a sample of the DataFrame’s rows in the console. By default, it shows 20 rows, though you can specify a different number. However, it only displays a subset of the data rather than returning all rows.
Why it's incorrect: show() only prints a sample of the rows, typically the first 20, rather than returning all rows.
In conclusion, collect() is the best method for retrieving all rows from a PySpark DataFrame, but be cautious when working with large datasets to avoid memory overloads.
Question No 8:
Which of the following code blocks correctly applies the function assessPerformance() to each row of a DataFrame called storesDF?
A. [assessPerformance(row) for row in storesDF.take(3)]
B. [assessPerformance() for row in storesDF]
C. storesDF.collect().apply(lambda: assessPerformance)
D. [assessPerformance(row) for row in storesDF.collect()]
E. [assessPerformance(row) for row in storesDF]
Answer: D
Explanation:
To apply the assessPerformance() function to each row of a DataFrame such as storesDF, we need to correctly iterate over the rows and pass each row as an argument to the function. Here's a detailed analysis of each option:
Option A: [assessPerformance(row) for row in storesDF.take(3)]
This option iterates over only the first three rows of the DataFrame by using take(3). The method take(3) returns a subset (a list) of the first three rows. While this is valid for applying the function to just the first three rows, it is not the correct approach if the task is to apply the function to every row in the DataFrame. Therefore, this is not the correct answer.
Option B: [assessPerformance() for row in storesDF]
In this code, the function assessPerformance() is being called without any arguments. However, assessPerformance() requires a row from storesDF as input, and since no argument is provided, this will lead to an error. The function call is syntactically incorrect because it does not supply the necessary row data. As a result, this is not a valid solution.
Option C: storesDF.collect().apply(lambda: assessPerformance)
Here, collect() is used to gather all the rows in the DataFrame into a list. However, after that, the code tries to call apply() on the list. The apply() function is typically used on DataFrame columns or Series objects, not on standard Python lists. Since collect() returns a list (or array), applying apply() to it is invalid. This results in an error, making this approach incorrect.
Option D: [assessPerformance(row) for row in storesDF.collect()]
This is the correct approach. The collect() method is used to gather all rows of the DataFrame into a list. Then, the list comprehension iterates over each row, applying the assessPerformance() function to every row in the DataFrame. This method correctly applies the function to each row, making it the proper solution.
Option E: [assessPerformance(row) for row in storesDF]
This option attempts to iterate directly over the storesDF DataFrame object. However, in frameworks like Spark or Pandas, DataFrame objects are not directly iterable like lists. You need to collect the rows first (using collect() in Spark or converting to a list in Pandas) before iterating over them. Since this code does not first collect the rows, it will result in an error, making this option invalid.
Option D is the only one that correctly applies the assessPerformance() function to each row of the entire DataFrame. It leverages collect() to retrieve the rows as a list and then uses list comprehension to apply the function to each row efficiently.
Question No 9:
How does the groupBy() method function in Apache Spark?
A) It collects data based on a particular column.
B) It processes the data in parallel across nodes.
C) It partitions data into different segments.
D) It arranges data in a sorted order.
Answer: A
Explanation:
The groupBy() method in Apache Spark is used to group data based on one or more columns. When applied, it segregates the dataset into groups based on the column values, making it easier to perform aggregations or other computations on each group separately. This is particularly useful when you want to apply transformations like sum, count, or average on data grouped by specific column values. While it might seem similar to sorting, groupBy() doesn't just reorder data; it effectively clusters data into categories that can be used for aggregate functions. It is an essential operation when dealing with large datasets, particularly when combining it with functions like agg() for aggregating data.
Question No 10:
What is the difference between the DataFrame API and the RDD API in Spark?
A) The DataFrame API is less efficient compared to RDDs.
B) RDDs are more abstract than DataFrames.
C) The DataFrame API includes optimizations like Catalyst and Tungsten.
D) The DataFrame API does not support SQL queries.
Answer: C
Explanation:
The main difference between the DataFrame API and the RDD (Resilient Distributed Dataset) API lies in the level of abstraction and optimizations. DataFrames provide a higher-level abstraction, offering an interface that is similar to working with tables in a relational database, and they support SQL-like queries. They also benefit from optimizations such as the Catalyst optimizer and Tungsten execution engine, which help improve query execution efficiency and memory management. These optimizations make DataFrames much more performant for handling large datasets compared to RDDs, which are a lower-level API. RDDs offer more control over the data but require more manual handling of computations and transformations. RDDs are considered more "primitive," while DataFrames are designed for higher-level data processing tasks.
