Free Amazon MLS-C01 Exam Questions

Question 1

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?

A : an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.

B : eate AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

C : eate AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.

D : eate an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Answer: D

Question 2

A company’s data scientist has trained a new machine learning model that performs better on test data than the company’s existing model performs in the production environment. The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment. However, the company is concerned that the new model might not work well on the production environment data. The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data. Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)

A : eate a new endpoint configuration that includes a production variant for each of the two models.

B : eate a new endpoint configuration that includes two target variants that point to different endpoints.

C : ploy the new model to the existing endpoint

D : date the existing endpoint to activate the new model.

E : date the existing endpoint to use the new endpoint configuration.

Answer: A,C

Question 3

A company has a podcast platform that has thousands of users. The company has implemented an anomaly detection algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening, pausing, and exiting the podcast. A machine learning (ML) specialist is designing the data ingestion of these events with the knowledge that the event payload needs some small transformations before inference. How should the ML specialist design the data ingestion to meet these requirements with the LEAST operational overhead?

A : gest event data by using a GraphQLAPI in AWS AppSync. Store the data in an Amazon DynamoDB table. Use DynamoDB Streams to call an AWS Lambda function to transform the most recent 10 minutes of data before inference.

B : gest event data by using Amazon Kinesis Data Streams. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Glue to transform the most recent 10 minutes of data before inference.

C : gest event data by using Amazon Kinesis Data Streams. Use an Amazon Kinesis Data Analytics for Apache Flink application to transform the most recent 10 minutes of data before inference.

D : Ingest event data by using Amazon Managed Streaming for Apache Kafka (Amazon MSK). Use an AWS Lambda function to transform the most recent 10 minutes of data before inference

Answer: B

Question 4

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 fraudulent.

How should the Specialist frame this business problem?

A : reaming classification

B : nary classification

C : lti-category classification

D : gression classification

Answer: C

Question 5

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

What should be done to reduce the impact of having such a large number of features?

A : rform one-hot encoding on highly correlated features

B : matrix multiplication on highly correlated features.

C : eate a new feature space using principal component analysis (PCA)

D : ply the Pearson correlation coefficient

Answer: C