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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing | 22% | - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data ingestion and transformation - Data preprocessing and feature engineering - Data validation and quality assessment |
| Topic 2: Model Development | 30% | - ML frameworks (SageMaker, built-in algorithms) - Distributed training - Training and validation strategies - Transfer learning and fine-tuning - Hyperparameter optimization - Algorithm selection and model architecture |
| Topic 3: Model Deployment and Inference | 20% | - Model deployment strategies (real-time, batch) - A/B testing and shadow mode deployment - Model versioning and rollback - SageMaker endpoints configuration - Inference optimization (latency, throughput) |
| Topic 4: MLOps and Monitoring | 28% | - CI/CD pipelines for ML - Incident response and remediation - Model monitoring and drift detection - Security and access management for ML - Model lineage and reproducibility - Cost optimization for ML workloads |
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21. Frage
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models.
The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?
Antwort: C
Begründung:
For asynchronous inference on large datasets, AWS recommends SageMaker batch transform, which is designed for offline and large-scale inference workloads. Batch transform jobs do not require real-time endpoints and can process large volumes of data efficiently.
For monitoring data quality, AWS provides Amazon SageMaker Model Monitor, which supports scheduled monitoring of data quality, schema drift, and feature distribution drift. Model Monitor can publish metrics to Amazon CloudWatch and trigger alerts when thresholds are breached.
The other options lack native ML-specific monitoring capabilities. CloudTrail audits API activity, not data quality. Glue, Batch, and ECS would require extensive custom monitoring logic.
Therefore, SageMaker batch transform combined with Model Monitor is the correct solution.
22. Frage
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset.
Which methods will meet these requirements? (Choose two.)
Antwort: A,E
Begründung:
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.
23. Frage
A company wants to build an anomaly detection ML model. The model will use large-scale tabular data that is stored in an Amazon S3 bucket. The company does not have expertise in Python, Spark, or other languages for ML.
An ML engineer needs to transform and prepare the data for ML model training.
Which solution will meet these requirements?
Antwort: A
Begründung:
The company lacks programming expertise, so a no-code/low-code solution is required. Amazon SageMaker Canvas includes Data Wrangler's visual interface, which enables users to import, transform, and prepare data using a graphical workflow.
Data Wrangler in Canvas supports common preprocessing tasks-such as handling missing values, normalization, outlier detection, and feature engineering-without writing code. It integrates directly with Amazon S3 and is suitable for large tabular datasets.
Options A, C, and D require coding skills (Spark, SQL, or Python), which violates the stated constraint.
Therefore, using the visual interface of SageMaker Data Wrangler in Canvas is the correct solution.
24. Frage
A company uses Amazon SageMaker AI to create ML models. The data scientists need fine-grained control of ML workflows, DAG visualization, experiment history, and model governance for auditing and compliance.
Which solution will meet these requirements?
Antwort: D
Begründung:
Amazon SageMaker Pipelines provides native orchestration of ML workflows with fine-grained control, DAG-based visualization, and seamless integration with SageMaker Studio. AWS documentation explicitly states that Pipelines is designed for end-to-end ML workflow automation and visualization.
SageMaker ML Lineage Tracking records relationships between datasets, models, training jobs, and endpoints, enabling full auditability and governance, which is essential for compliance.
SageMaker Experiments tracks experiment metrics but does not provide lineage-level governance.
CodePipeline is a general CI/CD service and lacks ML-specific DAG visualization and lineage tracking.
AWS best practices recommend combining SageMaker Pipelines + SageMaker Studio + ML Lineage Tracking for enterprise-grade ML workflow management.
Therefore, Option C is the correct and AWS-verified solution.
25. Frage
A company needs an AWS solution that will automatically create versions of ML models as the models are created.
Which solution will meet this requirement?
Antwort: C
26. Frage
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