Free MLS-C01 Exam Questions - Easiest Way for Success

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Total 367 Questions | Updated On: Sep 13, 2024

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Question 1

A machine learning (ML) specialist is training a multilayer perceptron (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes in the dataset, but it does not achieve an acceptable recall metric. The ML specialist varies the number and size of the MLP's hidden layers, but the results do not improve significantly.

Which solution will improve recall in the LEAST amount of time?

A : Add class weights to the MLP's loss function, and then retrain.

B : Gather more data by using Amazon Mechanical Turk, and then retrain.

C : Train a k-means algorithm instead of an MLP.

D : Train an anomaly detection model instead of an MLP.

Answer: A

Question 2

A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

The regular operation label is marked as 0 The abnormal operation label is marked as 1. Process abnormalities have a significant negative effect on the company’s profits. The company must avoid these abnormalities.

Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

A : Precision = 0.91 -Recall = 0.6

B : Precision = 0.61 -Recall = 0.98

C : Precision = 0.7 -Recall = 0.9

D : Precision = 0.98 -Recall = 0.8

Answer: A

Question 3

A bank wants to use a machine learning (ML) model to predict if users will default on credit card payments. The

training data consists of 30,000 labeled records and is evenly balanced between two categories. For the model,

an ML specialist selects the Amazon SageMaker built-in XGBoost algorithm and configures a SageMaker

automatic hyperparameter optimization job with the Bayesian method. The ML specialist uses the validation

accuracy as the objective metric.

When the bank implements the solution with this model, the prediction accuracy is 75%. The bank has given

the ML specialist 1 day to improve the model in production.

Which approach is the FASTEST way to improve the model's accuracy?

A : Run a SageMaker incremental training based on the best candidate from the current model's tuning job. Monitor the same metric that was used as the objective metric in the previous tuning, and look for improvements

B : Set the Area Under the ROC Curve (AUC) as the objective metric for a new SageMaker automatic hyperparameter tuning job. Use the same maximum training jobs parameter that was used in the previous tuning job.

C : Run a SageMaker warm start hyperparameter tuning job based on the current model’s tuning job. Use the same objective metric that was used in the previous tuning.

D : Set the F1 score as the objective metric for a new SageMaker automatic hyperparameter tuning job. Double the maximum training jobs parameter that was used in the previous tuning job.

Answer: A

Question 4

A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

A : Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.

B : Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.

C : Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

D : Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer: D

Question 5

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A : Redeploy the model as a batch transform job on an M5 instance.

B : Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C : Redeploy the model on a P3dn instance.

D : Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: D

Page: 1 / 74

Total 367 Questions | Updated On: Sep 13, 2024

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