Free Amazon MLS-C01 Exam Questions

Question 1

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

Question 2

An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.

Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

A : twise deletion

B : t observation carried forward

C : ltiple imputation

D : an substitution

Answer: C

Question 3

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 4

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 5

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 : n 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 : t 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 : n 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 : t 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