Question 1

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 2

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 3

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 4

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 5

A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.

Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

Options :

A : Change the number of compute nodes to be half or less than half of the number of evaluations.

B : Change the number of compute nodes and the number of evaluations to be much larger but equal.

C : Change the iterative optimization algorithm used to facilitate the tuning process.

D : Change the number of compute nodes to be double or more than double the number of evaluations.

Answer: C

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