HOTSPOT
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
• Store the resulting data back in Amazon S3.
• Use Amazon Athena to infer the schemas and available columns.
• Use AWS Glue crawlers to infer the schemas and available columns.
• Use AWS Glue DataBrew for data cleaning and feature engineering.

Solution:


Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?

1. A. Use Managed Spot Training.
2. B. Use SageMaker managed warm pools.
3. C. Use SageMaker Training Compiler.
4. D. Use the SageMaker distributed data parallelism (SMDDP) library.

Correct Answer: B

Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?

1. A. Use AWS Glue to transform the categorical data into numerical data.
2. B. Use AWS Glue to transform the numerical data into categorical data.
3. C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
4. D. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Correct Answer: C

Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Which AWS service or feature can aggregate the data from the various data sources?

1. A. Amazon EMR Spark jobs
2. B. Amazon Kinesis Data Streams
3. C. Amazon DynamoDB
4. D. AWS Lake Formation

Correct Answer: A

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

1. A. Use a multi-model serverless endpoin
2. B. Enable caching.
3. C. Use an asynchronous inference endpoin
4. D. Set the InitialInstanceCount parameter to 0.
5. E. Use a real-time endpoin
6. F. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
7. G. Use a serverless inference endpoin
8. H. Set the MaxConcurrency parameter to 1.

Correct Answer: D