A team has a model that returns predictions as a pandas Series with a custom index. They want to add these predictions to a DataFrame `df` that has a different index. Which code correctly aligns the predictions by position?

`df['prediction'] = predictions`

`df['prediction'] = predictions.reset_index(drop=True)`

`df['prediction'] = predictions.values`

During training, a team standardized numeric features using `StandardScaler` and one-hot encoded categorical features using `OneHotEncoder`. For batch inference on new data, what is the correct approach?

Apply only the scaler, since one-hot encoding is not needed for inference.

Skip preprocessing because the model handles raw data internally.

Refit the scaler and encoder on the new batch data before predicting.

Apply the same fitted scaler and encoder to the new data before calling predict.

A team trained a model on data where a categorical feature had 5 categories. During batch inference, the new data contains a 6th category that was never seen in training. The team uses a `OneHotEncoder` fitted during training. What will happen?

The encoder will silently create a new column for the 6th category.

The encoder will drop the row with the unseen category.

The encoder will raise an error because it encounters an unknown category.

The encoder will map the unseen category to the first category.

DatabricksCertified Machine Learning Associate

Domain 4Objective 3

Use Pandas to Perform Batch Inference MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 2)

Part of the Section 4: Model Deployment domain, which makes up ~30% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~11–18 in this domain), expect 2–3 from this objective — we provide 15 practice questions to prepare you well beyond it. (estimate)

15questions here

3free pages

5concepts

Questions 6–10

6foundation · easy
Which pandas method is commonly used to apply a custom function to each row of a DataFrame when the model's predict method cannot accept the entire DataFrame at once?
Select an answer first
7application · medium
A team has a model that returns predictions as a pandas Series with a custom index. They want to add these predictions to a DataFrame `df` that has a different index. Which code correctly aligns the predictions by position?
Select an answer first
8application · medium
During training, a team standardized numeric features using `StandardScaler` and one-hot encoded categorical features using `OneHotEncoder`. For batch inference on new data, what is the correct approach?
Select an answer first
9application · easy
After batch inference, a team writes the results DataFrame to a CSV file. They later discover that the CSV file contains an extra unnamed index column. Which parameter should they set to avoid this?
Select an answer first
10expert · medium
A team trained a model on data where a categorical feature had 5 categories. During batch inference, the new data contains a 6th category that was never seen in training. The team uses a `OneHotEncoder` fitted during training. What will happen?
Select an answer first

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Use Pandas to Perform Batch Inference MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 2)

Questions 6–10

Which pandas method is commonly used to apply a custom function to each row of a DataFrame when the model's predict method cannot accept the entire DataFrame at once?

A team has a model that returns predictions as a pandas Series with a custom index. They want to add these predictions to a DataFrame `df` that has a different index. Which code correctly aligns the predictions by position?

During training, a team standardized numeric features using `StandardScaler` and one-hot encoded categorical features using `OneHotEncoder`. For batch inference on new data, what is the correct approach?

After batch inference, a team writes the results DataFrame to a CSV file. They later discover that the CSV file contains an extra unnamed index column. Which parameter should they set to avoid this?

A team trained a model on data where a categorical feature had 5 categories. During batch inference, the new data contains a 6th category that was never seen in training. The team uses a `OneHotEncoder` fitted during training. What will happen?

Keep studying — Section 4: Model Deployment