Free Amazon MLS-C01 Exam Questions

Question 1

n online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model for each city where the company operates.

Operation engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory. The operation engineers want to avoid managing unnecessary resources.

Which solution will enable the company to achieve its goal with the LEAST operational overhead?

A : Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.

B : Prepare an Amazon SageMaker Docker container based on the open-source multi-model server. Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models. Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.

C : Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.

D : Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints, one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpointName parameter according to the city of each request.

Answer: B

Question 2

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 3

A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.

Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

A : ails exchanged by customers and the company's customer service agents

B : cial media posts containing the name of the company or its products

C : publicly available collection of news articles

D : publicly available collection of customer reviews

E : oduct sales revenue figures for the company

F : truction manuals for the company's products

Answer: A,B,D

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

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

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

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

Answer: D

Question 5

A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.

Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

A : tecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.

B : sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.

C : les data is aggregated by week. Request daily sales data from the source database to enable building a daily model.

D : sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.

E : ly 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.

Answer: A,B