100% Free Amazon MLS-C01 Practice Test Questions

Question 1

A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent

How should the Specialist frame this business problem'?

A : Streaming classification

B : Binary classification

C : Multi-category classification

D : Regression classification

Answer: A

Question 2

An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.

What should the Specialist do to meet these requirements?

A : Create one-hot word encoding vectors.

B : Produce a set of synonyms for every word using Amazon Mechanical Turk.

C : Create word embedding factors that store edit distance with every other word.

D : Download word embedding's pre-trained on a large corpus.

Answer: D

Question 3

A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:

* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.

* Support event-driven ETL pipelines.

* Provide a quick and easy way to understand metadata.

Which approach meets trfese requirements?

A : Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.

B : Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

C : Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.

D : Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Answer: A

Question 4

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 5

A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among

200 categories, and the date of the final outcome. Some partial information on claim contents is also provided,

but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed

over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from

month to month, a few months in advance.

What type of machine learning model should be used?

A : Classification month-to-month using supervised learning of the 200 categories based on claim contents.

B : Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in

each category to expect from month to month.

C : Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from

month to month.

D : Classification with supervised learning of the categories for which partial information on claim contents is

provided, and forecasting using claim IDs and timestamps for all other categories.

Answer: D

Free Practice Amazon MLS-C01 Exam Questions 2025