QUESTION: 117

An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.

How should a machine learning specialist meet these requirements?

Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.

Answer(s): A

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QUESTION: 118

A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor.
The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.

Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)

Set PerformAutoML to true.
Set ForecastHorizon to 4.
Set ForecastFrequency to W for weekly.
Set PerformHPO to true.
Set FeaturizationMethodName to filling.

Answer(s): A,D

Explanation:

A - If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the objective function, set PerformAutoML to true.
D - The following algorithms support HPO: - > DeepAR+.

Reference:

https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf

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QUESTION: 119

A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.
Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.
Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.
Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

Answer(s): A

Explanation:

Amazon Forecast requires the input data to be separated into a target time series dataset and an item metadata dataset.

The target time series dataset should include the time series data that you want to use for forecasting, such as inventory demand in this case. The item metadata dataset should include the metadata that describes the items in the time series, such as product IDs, categories, and attributes.

Therefore, the data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Both datasets should be uploaded as .csv files to Amazon S3, which is a suitable storage option for input data to Amazon Forecast.

Reference:

https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html

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QUESTION: 120

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

Redeploy the model as a batch transform job on an M5 instance.
Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
Redeploy the model on a P3dn instance.
Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer(s): B

Reference:

https://aws.amazon.com/machine-learning/elastic-inference/

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QUESTION: 118

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QUESTION: 119

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QUESTION: 120

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