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| Section | Weight | Objectives |
|---|---|---|
| Model Development | 30% | - Distributed training - ML frameworks (SageMaker, built-in algorithms) - Transfer learning and fine-tuning - Training and validation strategies - Algorithm selection and model architecture - Hyperparameter optimization |
| Data Processing | 22% | - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data validation and quality assessment - Data ingestion and transformation - Data preprocessing and feature engineering |
| MLOps and Monitoring | 28% | - Cost optimization for ML workloads - Incident response and remediation - Model lineage and reproducibility - Model monitoring and drift detection - CI/CD pipelines for ML - Security and access management for ML |
| Model Deployment and Inference | 20% | - A/B testing and shadow mode deployment - SageMaker endpoints configuration - Inference optimization (latency, throughput) - Model versioning and rollback - Model deployment strategies (real-time, batch) |
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NEW QUESTION # 236
A company has multiple models that are hosted on Amazon SageMaker Al. The models need to be re-trained.
The requirements for each model are different, so the company needs to choose different deployment strategies to transfer all requests to a new model.
Select the correct strategy from the following list for each requirement. Select each strategy one time. (Select THREE.)
. Canary traffic shifting
. Linear traffic shifting guardrail
. All at once traffic shifting
Answer:
Explanation:
Explanation:
1## Simultaneous calls to the endpoint must reach models with the same configuration Correct strategy: All at once traffic shifting Why:
"All at once" replaces the old model with the new model immediately. After the switch, all concurrent requests hit only the new model configuration, guaranteeing configuration consistency across simultaneous calls.
2## The new model must receive only a fraction of the requests for validation before receiving all the traffic Correct strategy: Canary traffic shifting Why:
Canary deployments route a small percentage of traffic (for example, 5% or 10%) to the new model first. This allows validation of correctness and performance before shifting 100% of traffic.
3## Traffic to the new model must increase gradually to ensure that pipelines that rely on the endpoint do not fail because of changes in latency Correct strategy: Linear traffic shifting guardrail Why:
Linear traffic shifting gradually increases traffic in equal increments over time and includes guardrails (such as CloudWatch alarms) to automatically roll back if latency or errors exceed thresholds.
NEW QUESTION # 237
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.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?
Answer: C
Explanation:
Preparing a training dataset that includes both categorical and numerical data is essential for maximizing the accuracy of a machine learning model. Transforming categorical data into numerical format is a critical step, as most ML algorithms require numerical input.
Why Transform Categorical Data into Numerical Data?
* Model Compatibility: Many ML algorithms cannot process categorical data directly and require numerical representations.
* Improved Performance: Proper encoding of categorical variables can enhance model accuracy and convergence speed.
Why Use Amazon SageMaker Data Wrangler?
Amazon SageMaker Data Wrangler offers a visual interface with over 300 built-in data transformations, including tools for encoding categorical variables.
Implementation Steps:
* Import Data:
* Load the dataset into SageMaker Data Wrangler from sources like Amazon S3 or on-premises databases.
* Identify Categorical Features:
* Use Data Wrangler's data type inference to detect categorical columns.
* Apply Categorical Encoding:
* Choose appropriate encoding techniques (e.g., one-hot encoding or ordinal encoding) from Data Wrangler's transformation options.
* Apply the selected transformation to convert categorical features into numerical format.
* Validate Transformations:
* Review the transformed dataset to ensure accuracy and completeness.
Advantages of Using SageMaker Data Wrangler:
* Ease of Use: Provides a user-friendly interface for data transformation without extensive coding.
* Operational Efficiency: Integrates data preparation steps, reducing the need for multiple tools and minimizing operational overhead.
* Flexibility: Supports various data sources and transformation techniques, accommodating diverse datasets.
By utilizing SageMaker Data Wrangler to transform categorical data into numerical format, the ML engineer can efficiently prepare the dataset, thereby enhancing the model's accuracy with minimal operational overhead.
References:
* Transform Data - Amazon SageMaker
* Prepare ML Data with Amazon SageMaker Data Wrangler
NEW QUESTION # 238
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata.
Which solution will meet these requirements with the LEAST manual effort?
Answer: B
Explanation:
Option A is correct because AWS Glue is the AWS-native managed ETL service built specifically to discover schema , run ETL jobs , and store metadata in the AWS Glue Data Catalog . AWS documentation states that Glue crawlers can automatically discover and catalog new or updated data sources , and that the Data Catalog automatically captures and manages schema metadata. This directly matches the requirement to run an ETL job on data in Amazon S3, discover the schema, and store the metadata with the least manual effort.
AWS Glue is also the lowest-effort answer because the service is managed and purpose-built for this workflow. The Glue Data Catalog serves as a persistent metadata repository, and AWS documents that crawlers infer schema information and integrate it into the catalog automatically. That means the ML engineer does not need to build custom schema inference logic or manually maintain metadata storage. This is exactly the kind of manual work the question is trying to avoid.
The other options are not as good. SageMaker Data Wrangler is primarily for visual data preparation and feature engineering, not for running a managed ETL-plus-catalog workflow with schema stored in a metadata catalog. Athena with Step Functions would require assembling more custom orchestration and still does not naturally replace the Glue Data Catalog workflow. Launching an EC2 instance introduces the highest operational overhead and does not align with the requirement for least manual effort. Therefore, the best verified AWS-docs answer is A , because AWS Glue combines ETL, schema discovery, and metadata cataloging in one managed service.
NEW QUESTION # 239
A company is using Amazon SageMaker AI to build an ML model to predict customer behavior. The company needs to explain the bias in the model to an auditor. The explanation must focus on demographic data of the customers.
Which solution will meet these requirements?
Answer: A
Explanation:
AWS documentation identifies Amazon SageMaker Clarify as the primary service for detecting, measuring, and explaining bias in ML models, particularly across demographic and sensitive attributes such as age, gender, and location. Clarify can analyze bias before training, after training, and during inference, making it suitable for audit and compliance requirements.
SageMaker Clarify generates bias reports using established fairness metrics such as difference in positive proportions, disparate impact, and conditional demographic disparity. These reports are exportable and auditor-friendly, directly meeting the requirement to explain bias to an external party.
AWS Glue DataBrew focuses on data preparation and quality, not bias detection. Amazon QuickSight does not provide ML fairness metrics. Amazon CloudWatch captures operational metrics, not demographic bias indicators.
AWS best practices explicitly recommend SageMaker Clarify for model transparency, fairness evaluation, and regulatory reporting.
Therefore, Option A is the correct and AWS-verified solution.
NEW QUESTION # 240
A company has multiple models that are hosted on Amazon SageMaker Al. The models need to be re-trained.
The requirements for each model are different, so the company needs to choose different deployment strategies to transfer all requests to a new model.
Select the correct strategy from the following list for each requirement. Select each strategy one time. (Select THREE.)
. Canary traffic shifting
. Linear traffic shifting guardrail
. All at once traffic shifting
Answer:
Explanation:
Explanation:
1## Simultaneous calls to the endpoint must reach models with the same configuration Correct strategy: All at once traffic shifting Why:
"All at once" replaces the old model with the new model immediately. After the switch, all concurrent requests hit only the new model configuration, guaranteeing configuration consistency across simultaneous calls.
2## The new model must receive only a fraction of the requests for validation before receiving all the traffic Correct strategy: Canary traffic shifting Why:
Canary deployments route a small percentage of traffic (for example, 5% or 10%) to the new model first. This allows validation of correctness and performance before shifting 100% of traffic.
3## Traffic to the new model must increase gradually to ensure that pipelines that rely on the endpoint do not fail because of changes in latency Correct strategy: Linear traffic shifting guardrail Why:
Linear traffic shifting gradually increases traffic in equal increments over time and includes guardrails (such as CloudWatch alarms) to automatically roll back if latency or errors exceed thresholds.
NEW QUESTION # 241
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