A team has a trained model that expects features in a specific order: `['age', 'income', 'score']`. The batch data is in a DataFrame `df` with columns `['score', 'age', 'income']`. Which code correctly prepares the feature matrix for prediction?

`X = df.sort_values('age'); predictions = model.predict(X)`

`X = df[['income', 'score', 'age']]; predictions = model.predict(X)`

`X = df[['score', 'age', 'income']]; predictions = model.predict(X)`

`X = df[['age', 'income', 'score']]; predictions = model.predict(X)`

A model returns predictions as a numpy array. A data engineer needs to add these predictions to a pandas DataFrame `df` that contains the original input rows. The DataFrame has 1,000 rows and the model returns 1,000 predictions. Which code correctly adds the predictions?

`df['prediction'] = predictions.reshape(-1, 1)`

`df = df.assign(prediction=predictions.tolist())`

`df['prediction'] = predictions`

`df = df.join(pd.Series(predictions))`

Which pandas method writes a DataFrame to a columnar file format that preserves data types and is often used for efficient storage and querying in big data environments?

df.to_json('predictions.parquet')

df.to_csv('predictions.parquet')

df.to_parquet('predictions.parquet')

df.to_excel('predictions.parquet')

DatabricksCertified Machine Learning Associate

Domain 4Objective 3

Use Pandas to Perform Batch Inference MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 1)

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 1–5

1expert · medium
A team has a trained model that expects features in a specific order: `['age', 'income', 'score']`. The batch data is in a DataFrame `df` with columns `['score', 'age', 'income']`. Which code correctly prepares the feature matrix for prediction?
Select an answer first
2foundation · easy
Which pandas method is most appropriate for creating a DataFrame from a list of dictionaries where each dictionary contains feature values for one record?
Select an answer first
3application · easy
A model returns predictions as a numpy array. A data engineer needs to add these predictions to a pandas DataFrame `df` that contains the original input rows. The DataFrame has 1,000 rows and the model returns 1,000 predictions. Which code correctly adds the predictions?
Select an answer first
4foundation · easy
Which approach correctly applies a fitted preprocessing object (e.g., a `StandardScaler`) to batch data before inference?
Select an answer first
5foundation · easy
Which pandas method writes a DataFrame to a columnar file format that preserves data types and is often used for efficient storage and querying in big data environments?
Select an answer first

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Use Pandas to Perform Batch Inference MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

A team has a trained model that expects features in a specific order: `['age', 'income', 'score']`. The batch data is in a DataFrame `df` with columns `['score', 'age', 'income']`. Which code correctly prepares the feature matrix for prediction?

Which pandas method is most appropriate for creating a DataFrame from a list of dictionaries where each dictionary contains feature values for one record?

A model returns predictions as a numpy array. A data engineer needs to add these predictions to a pandas DataFrame `df` that contains the original input rows. The DataFrame has 1,000 rows and the model returns 1,000 predictions. Which code correctly adds the predictions?

Which approach correctly applies a fitted preprocessing object (e.g., a `StandardScaler`) to batch data before inference?

Which pandas method writes a DataFrame to a columnar file format that preserves data types and is often used for efficient storage and querying in big data environments?

Keep studying — Section 4: Model Deployment