Free Amazon MLS-C01 Exam Questions

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

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.

New one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic.

Which solution will meet these requirements?

A : A/B testing

B : Canary release

C : Shadow deployment

D : Blue/green deployment

Answer: D

Question 3

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.

Which solution should the agency consider?

A : Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and createa stream processor to detect faces from a collection of known employees, and alert when non-employeesare detected.

B : Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detectfaces from a collection of known employees and alert when non-employees are detected.

C : Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video toAmazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video andcreate a stream processor to detect faces from a collection on each stream, and alert when nonemployeesare detected.

D : Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video toAmazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function tocapture image fragments and then call Amazon Rekognition Image to detect faces from a collection ofknown employees, and alert when non-employees are detected.

Answer: C

Question 4

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant

will default on a credit card payment. The company has collected data from a large number of sources with

thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are

highly correlated, the large number of features slows down the training speed significantly, and that there are

some overfitting issues.

The Data Scientist on this project would like to speed up the model training time without losing a lot of

information from the original dataset.

Which feature engineering technique should the Data Scientist use to meet the objectives?

A : n self-correlation on all features and remove highly correlated features

B : rmalize all numerical values to be between 0 and 1

C : an autoencoder or principal component analysis (PCA) to replace original features with new features

D : uster raw data using k-means and use sample data from each cluster to build a new dataset

Answer: B

Question 5

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3

The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3

Which solution takes the LEAST effort to implement?

A : Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 toserialize data as Parquet

B : gest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

C : Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use ApacheSpark to convert data into Parquet.

D : Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convertdata into Parquet.

Answer: B