Question 1

What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

Options :

A : Leave-one-out encoding

B : Target encoding

C : One-hot encoding

D : Categorical embeddings

E : String indexing

Answer: C

Question 2

A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

Options :

A : The data will be limited to a single executor preventing the model from being loaded multiple times

B : The model will be limited to a single executor preventing the data from being distributed

C : The model only needs to be loaded once per executor rather than once per batch during the inference process

D : The data will be distributed across multiple executors during the inference process

Answer: C

Question 3

Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?

Options :

A : F1

B : R-squared

C : MAE

D : MSE

Answer: A

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?

Options :

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

Question 5

The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.

Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?

Options :

A : Logistic regression

B : Spark ML cannot distribute linear regression training

C : Iterative optimization

D : Least-squares method

E : Singular value decomposition

Answer: C

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