Amazon Web Services MLS-C01 Questions Answers

 Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Amazon Athena can also query data from other sources, such as MySQL and Amazon Redshift, by using federated queries. Federated queries allow Amazon Athena to run SQL queries across data sources, such as relational and non-relational databases, data warehouses, and data lakes. By using Amazon Athena, the Data Scientist can perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon Redshift, and then calculating the average of a few selected columns from the joined data. Amazon Athena can also integrate with other AWS services, such as AWS Glue and Amazon QuickSight, to provide additional features, such as data cataloging and visualization.

References:

• What is Amazon Athena? - Amazon Athena
• Federated Query Overview - Amazon Athena
• Querying Data from Amazon S3 - Amazon Athena
• Querying Data from MySQL - Amazon Athena
• [Querying Data from Amazon Redshift - Amazon Athena]

The solution C meets the requirements because it minimizes costs for compute infrastructure, maximizes the scalability of resources for training, and facilitates the use of an ML model in low-connectivity environments. The solution C involves the following steps:

• Move the training data to an Amazon S3 bucket. This will enable the company to store the large amount of data in a durable, scalable, and cost-effective way. It will also allow the company to access the data from the cloud for training and evaluation purposes1.
• Train and evaluate the model by using Amazon SageMaker. This will enable the company to use a fully managed service that provides various features and tools for building, training, tuning, and deploying ML models. Amazon SageMaker can handle large-scale data processing and distributed training, and it can leverage the power of AWS compute resources such as Amazon EC2, Amazon EKS, and AWS Fargate2.
• Optimize the model by using SageMaker Neo. This will enable the company to reduce the size of the model and improve its performance and efficiency. SageMaker Neo can compile the model into an executable that can run on various hardware platforms, such as CPUs, GPUs, and edge devices3.
• Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. This will enable the company to deploy the model on a local device that can run inference in real time, even in low-connectivity environments. AWS IoT Greengrass can extend AWS cloud capabilities to the edge, and it can securely communicate with the cloud for updates and synchronization4.
• Deploy the model on the edge device. This will enable the company to automate quality control in its facilities by using the model to detect defects in new parts as they move on a conveyor belt. The model can run inference locally on the edge device without requiring internet connectivity, and it can send the results to the cloud when the connection is available4.

The other options are not suitable because:

• Option A: Deploying the model on a SageMaker hosting services endpoint will not facilitate the use of the model in low-connectivity environments, as it will require internet access to perform inference. Moreover, it may incur higher costs for hosting and data transfer than deploying the model on an edge device.
• Option B: Training and evaluating the model on premises will not minimize costs for compute infrastructure, as it will require the company to maintain and upgrade its own hardware and software. Moreover, it will not maximize the scalability of resources for training, as it will limit the company’s ability to leverage the cloud’s elasticity and flexibility.
• Option D: Training the model on premises will not minimize costs for compute infrastructure, nor maximize the scalability of resources for training, for the same reasons as option B.

References:

• 1: Amazon S3
• 2: Amazon SageMaker
• 3: SageMaker Neo
• 4: AWS IoT Greengrass

The best way to ensure that required packages are automatically available on the notebook instance for the data scientist to use is to create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance. A lifecycle configuration is a shell script that runs when you create or start a notebook instance. You can use a lifecycle configuration to customize the notebook instance by installing libraries, changing environment variables, or downloading datasets. You can also use a lifecycle configuration to automate the installation of custom Python packages that are not natively available on Amazon SageMaker.

Option A is incorrect because installing AWS Systems Manager Agent on the underlying Amazon EC2 instance and using Systems Manager Automation to execute the package installation commands is not a recommended way to customize the notebook instance. Systems Manager Automation is a feature that lets you safely automate common and repetitive IT operations and tasks across AWS resources. However, using Systems Manager Automation would require additional permissions and configurations, and it would not guarantee that the packages are installed before the notebook instance is ready to use.

Option B is incorrect because creating a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and placing the file under the /etc/init directory of each Amazon SageMaker notebook instance is not a valid way to customize the notebook instance. The /etc/init directory is used to store scripts that are executed during the boot process of the operating system, not the Jupyter notebook application. Moreover, a Jupyter notebook file is not a shell script that can be executed by the operating system.

Option C is incorrect because using the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook is not an automatic way to customize the notebook instance. This option would require the data scientist to manually run the conda commands every time they create or start a new notebook instance. This would not be efficient or convenient for the data scientist.

References:

• Customize a notebook instance using a lifecycle configuration script - Amazon SageMaker
• AWS Systems Manager Automation - AWS Systems Manager
• Conda environments - Amazon SageMaker

The problem that the Machine Learning Specialist is facing is likely due to concept drift, which is a phenomenon where the statistical properties of the target variable change over time, making the model less accurate and relevant. Concept drift can occur due to various reasons, such as changes in customer preferences, market trends, product inventory, seasonality, etc. In this case, the product recommendations model may have become outdated as the product inventory changed over time, making the recommendations less appealing to the customers. To address this issue, the model should be periodically retrained using the original training data plus new data as product inventory changes. This way, the model can learn from the latest data and adapt to the changing customer behavior and preferences. Retraining the model from scratch using the original data while adding a regularization term may not be sufficient, as it does not account for the new data. Updating the model’s hyperparameters may not help either, as it does not address the underlying data distribution change. Re-engineering the model completely may not be necessary, as the model may still be valid and useful with periodic retraining.

References:

• Concept Drift - Amazon SageMaker
• Detecting and Handling Concept Drift - Amazon SageMaker
• Machine Learning Concepts - Amazon Machine Learning