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Amazon Web Services MLS-C01 Dumps Questions Answers

MLS-C01 exam

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AWS Certified Machine Learning - Specialty

Last Update Apr 30, 2025
Total Questions : 322 With Comprehensive Analysis

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AWS Certified Machine Learning - Specialty Questions and Answers

Questions 1

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.

How should the Data Scientist correct this issue?

Options:

A.

Drop all records from the dataset where age has been set to 0.

B.

Replace the age field value for records with a value of 0 with the mean or median value from the dataset.

C.

Drop the age feature from the dataset and train the model using the rest of the features.

D.

Use k-means clustering to handle missing features.

Questions 2

A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.

The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.

Which action will MOST likely improve the performance for the forecasting model?

Options:

A.

Aggregate sales from stores in the same geographic area.

B.

Apply smoothing to correct for seasonal variation.

C.

Change the forecast frequency from daily to weekly.

D.

Replace missing values in the dataset by using linear interpolation.

Questions 3

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.

The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

Options:

A.

Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).

B.

Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

C.

Increase the XGBoost max_depth parameter because the model is currently underfitting the data.

D.

Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).

E.

Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

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