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Databricks Databricks-Machine-Learning-Associate Exam Sample Questions

Question # 1

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository. Which of the following explanations justifies this suggestion?

A. One-hot encoding is not supported by most machine learning libraries.

B. One-hot encoding is dependent on the target variable’s values which differ for each application.

C. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.

D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.

E. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

Answer

E. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

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Question # 2

A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data. Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

A. They can refactor their notebook to process the data in parallel.

B. They can refactor their notebook to use the PySpark DataFrame API.

C. They can refactor their notebook to use the Scala Dataset API.

D. They can refactor their notebook to use Spark SQL.

E. They can refactor their notebook to utilize the pandas API on Spark.

Answer

E. They can refactor their notebook to utilize the pandas API on Spark.

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Question # 3

Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

A. Keras

B. pandas

C. PvTorch

D. Spark ML

E. Scikit-learn

Answer

D. Spark ML

Explanation:

Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.

References:

Spark MLlib documentation (Feature Engineering with Spark ML).

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Question # 4

A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration. Which of the following lines of code can the data scientist run to accomplish the task?

A. spark_df.describe()

B. dbutils.data(spark_df).summarize()

C. This task cannot be accomplished in a single line of code.

D. spark_df.summary()

E. dbutils.data.summarize (spark_df)

Answer

E. dbutils.data.summarize (spark_df)

Explanation:

To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility functiondbutils.data.summarizecan be used. This function provides a comprehensive summary, including visual histograms.

Correct code:

dbutils.data.summarize(spark_df)

Other options likespark_df.describe()andspark_df.summary()provide textual statistical summaries but do not include visual histograms.

References:

Databricks Utilities Documentation

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Question # 5

In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?

A. When the features are of the categorical type

B. When the features are of the boolean type

C. When the features contain a lot of extreme outliers

D. When the features contain no outliers

E. When the features contain no missingno values

Answer

C. When the features contain a lot of extreme outliers

Explanation:

Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.

References:

Data Imputation Techniques (Dealing with Outliers).

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