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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q40-Q45):
NEW QUESTION # 40
Case Study
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 needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?
Answer: A
Explanation:
Amazon SageMaker Model Registry is a feature designed to manage machine learning (ML) models throughout their lifecycle. It allows users to catalog, version, and deploy models systematically, ensuring efficient model governance and management.
Key Features of SageMaker Model Registry:
* Centralized Cataloging: Organizes models intoModel Groups, each containing multiple versions.
* Version Control: Maintains a history of model iterations, making it easier to track changes.
* Metadata Association: Attach metadata such as training metrics and performance evaluations to models.
* Approval Status Management: Allows setting statuses like PendingManualApproval or Approved to ensure only vetted models are deployed.
* Seamless Deployment: Direct integration with SageMaker deployment capabilities for real-time inference or batch processing.
Implementation Steps:
* Create a Model Group: Organize related models into groups to simplify management and versioning.
* Register Model Versions: Each model iteration is registered as a version within a specific Model Group.
* Set Approval Status: Assign approval statuses to models before deploying them to ensure quality control.
* Deploy the Model: Use SageMaker endpoints for deployment once the model is approved.
Benefits:
* Centralized Management: Provides a unified platform to manage models efficiently.
* Streamlined Deployment: Facilitates smooth transitions from development to production.
* Governance and Compliance: Supports metadata association and approval processes.
By leveraging the SageMaker Model Registry, the company can ensure organized management of models, version control, and efficient deployment workflows with minimal operational overhead.
References:
* AWS Documentation: SageMaker Model Registry
* AWS Blog: Model Registry Features and Usage
NEW QUESTION # 41
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?
Answer: A
Explanation:
When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.
NEW QUESTION # 42
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 43
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded.
The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
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
NEW QUESTION # 44
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.
What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?
Answer: C
Explanation:
SageMaker Data Wrangler provides a no-code/low-code interface for preparing and transforming data, including dropping unnecessary columns. By creating a data flow and configuring a transform step, the ML engineer can easily remove correlated or unneeded columns from the Parquet file with minimal effort. This approach avoids the need for custom coding or managing additional infrastructure.
NEW QUESTION # 45
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