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NEW QUESTION # 181
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?
Answer: D
Explanation:
Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model's performance.
NEW QUESTION # 182
An ML engineer uses one ML framework to train multiple ML models. The ML engineer needs to optimize inference costs and host the models on Amazon SageMaker AI.
Which solution will meet these requirements MOST cost-effectively?
Answer: B
Explanation:
Amazon SageMaker multi-model endpoints (MME) are designed to host multiple models behind a single endpoint, dynamically loading models into memory on demand. AWS documentation explicitly recommends MME as the most cost-effective solution when multiple models share the same ML framework and inference container.
With MME, SageMaker loads models from Amazon S3 only when they are invoked and unloads idle models automatically. This dramatically reduces the number of instances required and avoids paying for always-on resources for infrequently used models.
Multi-container endpoints are intended for inference pipelines or ensembles and require all containers to be loaded at startup, which increases cost. Deploying separate endpoints for each model results in the highest cost due to duplicated infrastructure.
AWS best practices clearly position multi-model endpoints as the optimal choice for reducing inference costs when hosting many models with similar runtime requirements.
Therefore, Option B is the correct and AWS-verified solution.
NEW QUESTION # 183
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?
Answer: A
Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.
NEW QUESTION # 184
A hospital is using an ML model to validate x-ray results. The hospital runs a nightly batch inference job. The hospital needs to produce a daily report about model data quality and model performance.
Which solution will meet these requirements?
Answer: D
Explanation:
Option A is correct because Amazon SageMaker Model Monitor is the AWS service specifically built to monitor data quality and model quality for ML models in production. AWS documentation states that Model Monitor supports continuous monitoring with a batch transform job that runs regularly and also supports on-schedule monitoring for asynchronous batch transform jobs . That aligns directly with the scenario of a hospital running a nightly batch inference job and needing a daily report on both the incoming data and the model's predictive performance.
AWS documentation also separates the two monitoring needs very clearly. Data quality monitoring can be scheduled for batch transform jobs by using DefaultModelMonitor with a BatchTransformInput. Model quality monitoring can also be scheduled for batch transform jobs by using ModelQualityMonitor, which compares predictions against actual ground-truth labels stored in Amazon S3. Since the question explicitly asks for both model data quality and model performance , SageMaker Model Monitor is the documented feature that covers both requirements together.
Option B is not sufficient because CloudWatch dashboards show operational and resource metrics, not the full ML-specific data quality and model quality reports required here. Option C is incorrect because AWS Glue DataBrew is for data preparation and profiling, not model performance monitoring. Option D is partially plausible because SageMaker Pipelines integrates with QualityCheck steps and can run monitoring jobs on demand, but the AWS docs position Model Monitor as the native solution for scheduled monitoring of production batch inference workloads. Therefore, the best AWS-documented answer is A .
NEW QUESTION # 185
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.
Which solution will meet these requirements?
Answer: A
Explanation:
Amazon SageMaker Data Wrangler is a comprehensive tool that streamlines the process of data preparation and offers built-in capabilities for anomaly detection and visualization.
Key Features of SageMaker Data Wrangler:
* Data Importation: Connects seamlessly to various data sources, including Amazon S3 and on- premises databases, facilitating the aggregation of transaction logs, customer profiles, and MySQL tables.
* Anomaly Detection: Provides built-in analyses to detect anomalies in time series data, enabling the identification of outliers that may indicate fraudulent activities.
* Visualization: Offers a suite of visualization tools, such as histograms and scatter plots, to help understand data distributions and relationships, which are crucial for feature engineering and model development.
Implementation Steps:
* Data Aggregation:
* Import data from Amazon S3 and on-premises MySQL databases into SageMaker Data Wrangler.
* Utilize Data Wrangler's data flow interface to combine and preprocess datasets, ensuring a unified dataset for analysis.
* Anomaly Detection:
* Apply the anomaly detection analysis feature to identify outliers in the dataset.
* Configure parameters such as the anomaly threshold to fine-tune the detection sensitivity.
* Visualization:
* Use built-in visualization tools to create charts and graphs that depict data distributions and highlight anomalies.
* Interpret these visualizations to gain insights into potential fraud patterns and feature interdependencies.
Advantages of Using SageMaker Data Wrangler:
* Integrated Workflow: Combines data preparation, anomaly detection, and visualization within a single interface, streamlining the ML development process.
* Operational Efficiency: Reduces the need for multiple tools and complex integrations, thereby minimizing operational overhead.
* Scalability: Handles large datasets efficiently, making it suitable for extensive transaction logs and customer profiles.
By leveraging SageMaker Data Wrangler, the ML engineer can effectively detect anomalies and visualize results, facilitating the development of a robust fraud detection model.
References:
* Analyze and Visualize - Amazon SageMaker
* Transform Data - Amazon SageMaker
NEW QUESTION # 186
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