Databricks Certified Machine Learning Associate (Databricks Certified Machine Learning) - Databricks - Free Practice

1

Question

A data scientist is working with a feature set with the following schema:

Exhibit

The customer_id column is the primary key in the feature set. Each of the columns in the feature set has missing values. They want to replace the missing values by imputing a common value for each Which of the following lists all of the columns in the feature set that need to be imputed using the feature. most common value of the column?

Options

A loyalty_tier

B customer_id

C spend

D units

E customer_id, loyalty_tier

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2

Question

Which of the following tools can be used to parallelize the hyperparameter tuning process for single- node machine learning models using a Spark cluster?

Options

A Autoscaling clusters

B Delta Lake

C Spark ML

D Autoscaling clusters

E MLflow Experiment Tracking

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3

Question

A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.

Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

Options

A import pandas as pd
df = pd.DataFrame(spark_df)

B spark_df.to_pandas()

C import pyspark.pandas as ps
df = ps.to_pandas(spark_df)

D spark_df.to_sql()

E import pyspark.pandas as ps
df = ps.DataFrame(spark_df)

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4

Question

A data scientist has replaced missing values in their feature set with each respective feature variable’s median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.

Which of the following approaches can they take to include as much information as possible in the feature set?

Options

A Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them

B Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing

C Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed

D Remove all feature variables that originally contained missing values from the feature set

E Impute the missing values using each respective feature variable's mean value instead of the median value

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5

Question

A data scientist has developed a linear regression model using Spark ML and computed the predictions in a Spark DataFrame preds_df with the following schema: prediction DOUBLE actual DOUBLE Which of the following code blocks can be used to compute the root mean-squared-error of the model according to the data in preds_df and assign it to the rmse variable?

Exhibit