An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Select TWO.)

• A.The factorization machines (FM) algorithm
• B.The Latent Dirichlet Allocation (LDA) algorithm
• C.The principal component analysis (PCA) algorithm
• D.The k-means algorithm
• E.The Random Cut Forest (RCF) algorithm

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A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

• A.The model needs to be completely re-engineered because it is unable to handle product inventory changes
• B.The model should be periodically retrained using the original training data plus new data as product inventory changes
• C.The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
• D.The model's hyperparameters should be periodically updated to prevent drift

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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?

• A.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.
• B.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.
• C.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.
• D.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.

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A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.
How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

• A.Copy the training program to directory /opt/ml/train
• B.Use CMD config in the Dockerfile to add the training program as a CMD of the image
• C.Modify the bash_profile file in the container and add a bash command to start the training program
• D.Configure the training program as an ENTRYPOINT named train

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A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

• A.Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
• B.Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
• C.Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
• D.Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

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