Pass the Amazon Web Services AWS Certified Associate MLA-C01 Questions and answers with CertsForce

AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.

Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.

This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.

Step 1: Create a feature group.Step 2: Ingest the records.Step 3: Access the store to build datasets for training.

Step 1: Create a Feature Group

Why?A feature group is the foundational unit in SageMaker Feature Store, where features are defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for the features and the primary keys for data identification.

How?Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name, schema, and S3 storage location for offline access.

Step 2: Ingest the Records

Why?After creating the feature group, the raw data must be ingested into the Feature Store. This step populates the feature group with data, making it available for both real-time and offline use.

How?Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into the feature group. Ensure the records conform to the feature group schema.

Step 3: Access the Store to Build Datasets for Training

Why?Once the features are stored, they can be accessed to create training datasets. These datasets combine relevant features into a single format for machine learning model training.

How?Use the SageMaker Python SDK to query the offline store or retrieve real-time features using the online store API. The offline store is typically used for batch training, while the online store is used for inference.

Order Summary:

Create a feature group.

Ingest the records.

Access the store to build datasets for training.

This process ensures the features are properly managed, ingested, and accessible for model training using Amazon SageMaker Feature Store.

By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user's business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group. This approach is secure, scalable, and aligns with AWS best practices for access control.

SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state. By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.

AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost-effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

AWS Glue DataBrew provides an easy-to-use interface for preparing and transforming data, including masking or obfuscating sensitive information. It offers built-in data masking features, allowing the ML engineer to handle sensitive data securely while retaining its structure and meaning. This solution is efficient and requires minimal coding, making it ideal for ensuring sensitive data is masked before model building begins.

Amazon FSx for Lustre is designed for high-performance workloads like ML training. It provides fast, low-latency access to data by linking directly to the existing S3 bucket and caching frequently accessed files locally. This significantly improves training performance compared to directly accessing millions of files from S3. It requires minimal changes to the training job and avoids the overhead of transferring or restructuring data, making it the fastest and most efficient solution.

Scenario:The ML engineer needs a low-overhead solution to query thousands of existing and new CSV objects stored in Amazon S3 based on a transaction date.

Why Athena?

Serverless:Amazon Athena is a serverless query service that allows direct querying of data stored in S3 using standard SQL, reducing operational overhead.

Ease of Use:By using the CTAS statement, the engineer can create a table with optimized partitions based on the transaction date. Partitioning improves query performance and minimizes costs by scanning only relevant data.

Low Operational Overhead:No need to manage or provision additional infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table creation and optimization.

Steps to Implement:

Organize Data in S3:Store CSV files in a bucket in a consistent format and directory structure if possible.

Configure Athena:Use the AWS Management Console or Athena CLI to set up Athena to point to the S3 bucket.

Run CTAS Statement:

CREATE TABLE processed_data

WITH (

format = 'PARQUET',

external_location = 's3://processed-bucket/',

partitioned_by = ARRAY['transaction_date']

) AS

SELECT *

FROM input_data;

This creates a new table with data partitioned by transaction date.

Query the Data:Use standard SQL queries to fetch data based on the transaction date.

References:

Amazon Athena CTAS Documentation

Partitioning Data in Athena

SageMaker real-time inference is designed for low-latency, real-time use cases, such as detecting fraudulent transactions in banking applications. It eliminates the delays associated with SageMaker Asynchronous Inference, improving inference performance.

SageMaker Model Monitor provides tools to monitor deployed models for deviations in data quality, model performance, and other metrics. It can be configured to send notifications when a deviation in model quality is detected, ensuring the system remains reliable.

 Problem Description:

The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model’s ability to learn patterns from the minority class.

 Why SageMaker Data Wrangler?

SageMaker Data Wrangler provides a built-in operation called "Balance Data," which includes oversampling and undersampling techniques to address class imbalances.

Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.

 Steps to Implement:

Import the dataset into SageMaker Data Wrangler.

Apply the "Balance Data" operation and configure it to oversample the minority class.

Export the balanced dataset for training.

 Advantages:

Ease of Use: Minimal configuration is required.

Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.

Time Efficiency: Reduces manual effort compared to external tools or scripts.

The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real-world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.