A data engineer is building a scikit-learn `Pipeline` that includes a `PCA` step followed by a `LogisticRegression` step. What is the primary role of the `PCA` step in this pipeline?

It transforms the input features into a lower-dimensional space while retaining most of the variance.

It selects the most important features by their correlation with the target.

It fits a model to predict the target variable from the input features.

It scales the features to have zero mean and unit variance.

A data scientist increases the maximum depth of a decision tree from 3 to 15. What is the most likely effect on the model's bias and variance?

Both bias and variance decrease.

Bias decreases and variance increases.

Both bias and variance increase.

Bias increases and variance decreases.

A model is trained to predict the log-transformed price of a product. The RMSE on the log-transformed test set is 0.5. What is the correct interpretation of this error in the original price scale?

The error is multiplicative; a typical prediction is off by a factor of approximately e^0.5 ≈ 1.65.

The error is additive; a typical prediction is off by 0.5 units of price.

The error is additive; a typical prediction is off by 0.5% of the price.

The error cannot be interpreted without additional information.

DatabricksCertified Machine Learning Associate

Domain 3Objective 1

Model Selection and Evaluation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 4)

Part of the Section 3: Model Development domain, which makes up ~23% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–14 in this domain), expect 2–4 from this objective — we provide 22 practice questions to prepare you well beyond it. (estimate)

22questions here

5free pages

7concepts

Questions 16–20

16foundation · easy
A binary classifier produces the following confusion matrix on a test set: TP=80, FP=20, FN=40, TN=60. What is the F1 score of this model?
Select an answer first
17foundation · easy
A data engineer is building a scikit-learn `Pipeline` that includes a `PCA` step followed by a `LogisticRegression` step. What is the primary role of the `PCA` step in this pipeline?
Select an answer first
18foundation · easy
A data scientist increases the maximum depth of a decision tree from 3 to 15. What is the most likely effect on the model's bias and variance?
Select an answer first
19foundation · easy
A model is trained to predict the log-transformed price of a product. The RMSE on the log-transformed test set is 0.5. What is the correct interpretation of this error in the original price scale?
Select an answer first
20foundation · easy
A regression model is evaluated on a test set with the following results: the sum of squared errors (SSE) is 150, the sum of squared total (SST) is 600, and the number of samples is 50. What is the R-squared value of this model?
Select an answer first

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Model Selection and Evaluation MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 4)

Questions 16–20

A binary classifier produces the following confusion matrix on a test set: TP=80, FP=20, FN=40, TN=60. What is the F1 score of this model?

A data engineer is building a scikit-learn `Pipeline` that includes a `PCA` step followed by a `LogisticRegression` step. What is the primary role of the `PCA` step in this pipeline?

A data scientist increases the maximum depth of a decision tree from 3 to 15. What is the most likely effect on the model's bias and variance?

A model is trained to predict the log-transformed price of a product. The RMSE on the log-transformed test set is 0.5. What is the correct interpretation of this error in the original price scale?

A regression model is evaluated on a test set with the following results: the sum of squared errors (SSE) is 150, the sum of squared total (SST) is 600, and the number of samples is 50. What is the R-squared value of this model?

Keep studying — Section 3: Model Development