Online Access Free Databricks-Machine-Learning-Associate Practice Test

A data scientist has written a feature engineering notebook that utilizes the pandas library. As the size of the data processed by the notebook increases, the notebook's runtime is drastically increasing, but it is processing slowly as the size of the data included in the process increases.
Which of the following tools can the data scientist use to spend the least amount of time refactoring their notebook to scale with big data?

• A.Spark SQL
• B.pandas API on Spark
• C.PySpark DataFrame API
• D.Feature Store

A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the path model_uri for the DataFrame batch_df.
batch_df has the following schema:
customer_id STRING
The machine learning engineer runs the following code block to perform inference on batch_df using the linear regression model at model_uri:

In which situation will the machine learning engineer's code block perform the desired inference?

• A.When the model at model_uri only uses customer_id as a feature
• B.When the Feature Store feature set was logged with the model at model_uri
• C.This code block will not perform the desired inference in any situation.
• D.When all of the features used by the model at model_uri are in a single Feature Store table
• E.When all of the features used by the model at model_uri are in a Spark DataFrame in the PySpark

A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem. The following root-mean-squared-error values are calculated on each of the validation folds:
* 10.0
* 12.0
* 17.0
Which of the following values represents the overall cross-validation root-mean-squared error?

• A.39.0
• B.13.0
• C.12.0
• D.10.0
• E.17.0

Which of the following machine learning algorithms typically uses bagging?

• A.IGradient boosted trees
• B.Decision tree
• C.K-means
• D.Random forest

A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFrame features_df. A list of the names of the string columns is assigned to the input_columns variable.
They have developed this code block to accomplish this task:

The code block is returning an error.
Which of the following adjustments does the data scientist need to make to accomplish this task?

• A.They need to remove the line with the fit operation.
• B.They need to use VectorAssembler prior to one-hot encoding the features.
• C.They need to use Stringlndexer prior to one-hot encodinq the features.
• D.They need to specify the method parameter to the OneHotEncoder.