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Databricks Machine Learning Associate exam dumps: questions 501 to 600 of 655

Page 6 of the free Databricks Machine Learning Associate question bank for the Databricks Certified Machine Learning Associate exam. Questions 501 to 600 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated January 2025

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Free Databricks Machine Learning Associate practice questions

Questions 501 to 505 of 655

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Databricks Machine Learning Associate Question 501

Select 3

You are working with a Spark DataFrame in Databricks that contains numerical data representing sales performance metrics. You want to compute summary statistics such as mean, standard deviation, minimum, and maximum for all numerical columns in the DataFrame. Which of the following methods could you use to achieve this?

A
Use the .summary() method on the Spark DataFrame.
B
Use the describe() method on the Spark DataFrame.
C
Use dbutils to generate a data profile summary.
D
Use the .agg() method with specific aggregation functions like avg() and stddev().
E
Use the collect() method to bring all data to the driver and calculate the statistics manually.

Show answer and explanation

Correct answers: A, C, D

Explanation

To compute summary statistics for numerical columns in a Spark DataFrame, you can use .summary() for a predefined set of statistics, dbutils data profiling for detailed summaries, or .agg() with specific aggregation functions for custom computations. Methods like describe() are limited in scope, and collect() is inefficient and not suitable for large-scale distributed data processing.

A. Correct.
Correct. The .summary() method in Spark DataFrames computes a set of summary statistics, including mean, standard deviation, min, max, and more for numerical columns.
B. Incorrect.
Incorrect. The describe() method only computes a limited set of statistics (count, mean, stddev, min, max) and is not as comprehensive as .summary() or dbutils-based summaries.
C. Correct.
Correct. dbutils can be used to generate a data profile summary, which provides detailed statistics for numerical columns.
D. Correct.
Correct. The .agg() method allows you to compute specific summary statistics by applying aggregation functions like avg() and stddev() on the DataFrame.
E. Incorrect.
Incorrect. Using the collect() method to calculate statistics manually is inefficient and not recommended in distributed environments like Spark.

Databricks Machine Learning Associate Question 502

Select 3

You are working on a Spark DataFrame named sales_data containing a column revenue. You suspect there are outliers in the revenue column. Which of the following approaches could you use in Databricks to remove these outliers effectively?

A
Filter rows where the revenue column values fall outside 3 standard deviations from the mean.
B
Filter rows where the revenue column values fall outside the interquartile range (IQR) by more than 1.5 times the IQR.
C
Use the fillna() method to replace all null values in the revenue column with the median value.
D
Drop rows with outlier values in the revenue column by setting a threshold based on domain knowledge.
E
Apply a clustering algorithm like K-Means to group similar rows and consider outliers as data points in smaller clusters.

Show answer and explanation

Correct answers: A, B, D

Explanation

Outlier removal in Spark DataFrames can be achieved using statistical methods like standard deviation or IQR-based filtering, which are computationally efficient and widely used in machine learning workflows. Alternatively, domain knowledge can be applied to set thresholds for filtering data. Methods like fillna() address missing values and are not relevant for outliers, while clustering algorithms are not directly intended for outlier removal.

A. Correct.
This is a correct approach. Removing outliers based on standard deviation is a common statistical method to handle extreme values.
B. Correct.
This is a correct approach. Filtering outliers based on the IQR is widely used in data preprocessing for skewed data distributions.
C. Incorrect.
This is incorrect because fillna() is used to handle missing values, not outliers.
D. Correct.
This is a correct approach. Setting a threshold based on domain expertise is a practical way to identify and remove outliers specific to a dataset.
E. Incorrect.
This is incorrect because clustering techniques like K-Means are not specifically designed for outlier removal and require additional steps to identify outliers.

Databricks Machine Learning Associate Question 503

Select 3

You are working with a Spark DataFrame containing the column temperature which represents sensor readings. You suspect that the data contains outliers. You need to remove outliers from this column using either the standard deviation method or the Interquartile Range (IQR) method. Which of the following steps would correctly achieve this in PySpark?

A
Calculate the mean and standard deviation of the temperature column, then filter rows where the values are within three standard deviations from the mean.
B
Calculate the minimum and maximum values of the temperature column and filter rows that fall within this range.
C
Calculate the first quartile (Q1) and third quartile (Q3) of the temperature column, compute the IQR as Q3 - Q1, and filter rows where values are within 1.5 times the IQR below Q1 or above Q3.
D
Use the Spark DataFrame dropna() method to remove rows with missing values, assuming outliers are represented as nulls.
E
Use the Spark SQL function percentile_approx to calculate Q1 and Q3 for the temperature column, derive the IQR, and filter rows where values are within the valid range based on the IQR.

Show answer and explanation

Correct answers: A, C, E

Explanation

Outliers can be removed using either the standard deviation method or the IQR method. The standard deviation method identifies values beyond a certain number of standard deviations from the mean, while the IQR method defines outliers as values outside the range determined by Q1 - 1.5 * IQR and Q3 + 1.5 * IQR. Both methods are commonly used in machine learning workflows, and leveraging Spark SQL functions like percentile_approx facilitates efficient computation in distributed environments. Filtering based on minimum/maximum values or using methods like dropna() does not specifically address outliers.

A. Correct.
Correct. This is the standard deviation method, where values beyond three standard deviations from the mean are considered outliers. This method is commonly used for normally distributed data.
B. Incorrect.
Incorrect. Filtering based on the minimum and maximum values of the column does not address outliers and would simply retain all valid data points.
C. Correct.
Correct. This is the IQR method, which identifies outliers as values outside the range [Q1 - 1.5 * IQR, Q3 + 1.5 * IQR]. This method is robust for skewed data.
D. Incorrect.
Incorrect. While dropna() removes rows with null values, it does not specifically address outliers, as it assumes missing values are equivalent to outliers.
E. Correct.
Correct. Using Spark SQL functions like percentile_approx allows computation of Q1 and Q3 efficiently for large datasets, enabling the application of the IQR method to filter outliers.

Databricks Machine Learning Associate Question 504

Select 2

You are working with a Spark DataFrame containing a column named 'sales'. You suspect that the column contains outliers and want to remove them. Which of the following approaches can be used to filter out outliers based on standard deviation or interquartile range (IQR) in PySpark?

A
Calculate the mean and standard deviation of the 'sales' column, then filter rows where values fall outside 3 standard deviations from the mean.
B
Compute the first (Q1) and third quartiles (Q3) of the 'sales' column, calculate the IQR as Q3 - Q1, and filter rows where values fall outside the range [Q1 - 1.5IQR, Q3 + 1.5IQR].
C
Use the describe() method on the DataFrame to directly identify and remove outliers based on standard deviation.
D
Write a custom UDF to flag and remove outliers using the IQR method.
E
Use Spark’s built-in 'removeOutliers()' function to automatically filter outliers based on either standard deviation or IQR.

Show answer and explanation

Correct answers: A, B

Explanation

Removing outliers in PySpark requires explicit calculations using either the standard deviation or IQR method. Both approaches involve computing summary statistics (e.g., mean, standard deviation, quantiles) and filtering rows based on a threshold. The describe() method and UDFs are not efficient or sufficient for this task, and no built-in function like 'removeOutliers()' exists in Spark.

A. Correct.
This is correct. Calculating the mean and standard deviation, then filtering rows using a threshold (e.g., 3 standard deviations from the mean) is a valid and common approach to removing outliers in PySpark.
B. Correct.
This is correct. The IQR method is a standard approach for identifying outliers by calculating Q1 and Q3, deriving the IQR, and filtering rows outside the specified range. This can be implemented using PySpark functions like approxQuantile().
C. Incorrect.
This is incorrect. The describe() method provides summary statistics (e.g., mean, standard deviation, min, max) but does not directly support outlier removal.
D. Incorrect.
This is incorrect. While UDFs can be used for row-level operations in PySpark, they are inefficient for this task given that PySpark provides built-in functions to compute summary statistics like mean, standard deviation, and quantiles.
E. Incorrect.
This is incorrect. Spark does not provide a built-in 'removeOutliers()' function. Outlier removal must be explicitly implemented using standard deviation or IQR calculations.

Databricks Machine Learning Associate Question 505

Select 2

You have a Spark DataFrame containing a column named 'price' with numerical values. You suspect there are outliers in the 'price' column and want to remove them using either the standard deviation or the Interquartile Range (IQR) method. Which of the following code snippets correctly removes outliers from the DataFrame?

A
df = df.filter((col('price') > mean_price - 3 * stddev_price) & (col('price') < mean_price + 3 * stddev_price))
B
df = df.filter((col('price') > q1 - 1.5 * iqr) & (col('price') < q3 + 1.5 * iqr))
C
df = df.filter((col('price') > mean_price - stddev_price) & (col('price') < mean_price + stddev_price))
D
df = df.filter((col('price') > q1 - 3 * iqr) & (col('price') < q3 + 3 * iqr))
E
df = df = df.filter((col('price') > q3 - 1.5 * iqr) & (col('price') < q1 + 1.5 * iqr))

Show answer and explanation

Correct answers: A, B

Explanation

Outliers can be removed using standard deviation or the IQR method. The standard deviation method filters values within three standard deviations from the mean, while the IQR method filters values outside 1.5 times the IQR below Q1 or above Q3. Both approaches are valid and commonly used for outlier detection.

A. Correct.
Correct: This is the standard deviation method, which filters values within three standard deviations from the mean. It is a valid approach to remove outliers.
B. Correct.
Correct: This is the IQR-based method, which removes values outside 1.5 times the IQR below Q1 or above Q3. It is commonly used for outlier detection.
C. Incorrect.
Incorrect: While this uses the standard deviation, it only filters within one standard deviation from the mean, which may remove too many valid data points and is not typically used for outlier removal.
D. Incorrect.
Incorrect: While this uses the IQR, multiplying it by 3 instead of 1.5 is not a standard approach and may allow too many outliers to remain in the data.
E. Incorrect.
Incorrect: This reverses Q1 and Q3 in the IQR calculation, which is incorrect and would not properly identify outliers.

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Databricks Machine Learning Associate practice questions 501 to 600 of 655

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