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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Deployment and Orchestration of ML Workflows22%- ML pipeline orchestration
  • 1. CI/CD pipelines for ML workflows
    • 2. Workflow automation using AWS services
      - Model deployment
      • 1. Real-time and batch inference endpoints
        • 2. Auto scaling and infrastructure configuration
          Data Preparation for Machine Learning (ML)28%- Data preprocessing and transformation
          • 1. Data validation and quality checks
            • 2. Data cleaning, normalization, and feature engineering
              - Data ingestion and collection
              • 1. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue)
                • 2. Data acquisition from structured and unstructured sources
                  ML Model Development26%- Model selection and training
                  • 1. Training models using Amazon SageMaker
                    • 2. Choosing appropriate ML algorithms
                      - Model tuning and evaluation
                      • 1. Performance evaluation and metrics interpretation
                        • 2. Hyperparameter tuning
                          ML Solution Monitoring, Maintenance, and Security24%- Security and governance
                          • 1. IAM policies and access control
                            • 2. Compliance and secure ML system design
                              - Monitoring and observability
                              • 1. Infrastructure and data monitoring
                                • 2. Model drift detection and performance monitoring

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                                  Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Prüfungsfragen mit Lösungen (Q199-Q204):

                                  199. Frage
                                  Hotspot Question
                                  An ML engineer must choose the appropriate Amazon SageMaker algorithm to solve specific AI problems.
                                  Select the correct SageMaker built-in algorithm from the following list for each use case. Each algorithm should be selected one time.
                                  - Random Cut Forest (RCF) algorithm
                                  - Semantic segmentation algorithm
                                  - Sequence-to-Sequence (seq2seq) algorithm

                                  Antwort:

                                  Begründung:


                                  200. Frage
                                  A company collects customer data daily and stores it as compressed files in an Amazon S3 bucket partitioned by date. Each month, analysts process the data, check data quality, and upload results to Amazon QuickSight dashboards.
                                  An ML engineer needs to automatically check data quality before the data is sent to QuickSight, with the LEAST operational overhead.
                                  Which solution will meet these requirements?

                                  Antwort: D

                                  Begründung:
                                  AWS Glue Data Quality provides managed, declarative data quality checks with minimal configuration.
                                  Combined with Glue crawlers, it enables automatic schema discovery and quality validation without custom code.
                                  Option A uses native AWS services designed for this exact purpose, minimizing operational overhead.
                                  Options B and C require custom code and maintenance. Option D is not designed for data validation.
                                  AWS documentation explicitly recommends Glue Data Quality rules for scalable, automated data quality checks in analytics pipelines.
                                  Therefore, Option A is the correct and AWS-aligned solution.


                                  201. Frage
                                  A company has built more than 50 models and deployed the models on Amazon SageMaker Al as real-time inference endpoints. The company needs to reduce the costs of the SageMaker Al inference endpoints. The company used the same ML framework to build the models. The company's customers require low-latency access to the models.
                                  Select and order the correct steps from the following list to reduce the cost of inference and keep latency low.
                                  Select each
                                  step one time or not at all. (Select and order FIVE.)
                                  Create an endpoint configuration that references a multi-model container.
                                  . Create a SageMaker Al model with multi-model endpoints enabled.
                                  . Deploy a real-time inference endpoint by using the endpoint configuration.
                                  . Deploy a serverless inference endpoint configuration by using the endpoint configuration.
                                  Spread the existing models to multiple different Amazon S3 bucket paths.
                                  . Upload the existing models to the same Amazon S3 bucket path.
                                  . Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.

                                  Antwort:

                                  Begründung:

                                  Explanation:
                                  Step 1
                                  Upload the existing models to the same Amazon S3 bucket path.
                                  Multi-model endpoints require all models to be stored under a single S3 prefix so SageMaker can dynamically load them on demand.
                                  Step 2
                                  Create a SageMaker AI model with multi-model endpoints enabled.
                                  This creates a SageMaker model resource that uses a multi-model-capable container (for example, XGBoost, PyTorch, or TensorFlow MME-compatible containers).
                                  Step 3
                                  Create an endpoint configuration that references a multi-model container.
                                  The endpoint configuration defines:
                                  * Instance type
                                  * Initial instance count
                                  * The multi-model container reference
                                  Step 4
                                  Deploy a real-time inference endpoint by using the endpoint configuration.
                                  Real-time endpoints ensure low-latency inference, which is a strict customer requirement.
                                  Step 5
                                  Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.
                                  Each inference request specifies a model ID so SageMaker knows which model to load from S3.


                                  202. Frage
                                  A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket.
                                  The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model.
                                  The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference.
                                  Which solution will meet these requirements?

                                  Antwort: C

                                  Begründung:
                                  Option A is correct because Amazon SageMaker Feature Store is the AWS service designed to act as a centralized repository for ML features that are used consistently across training and inference . AWS documentation states that SageMaker Feature Store simplifies how you create, store, share, and manage features for data exploration, model training, and model inference. This directly matches the requirement to create a repository for streamed song ratings that will be used in both batch training and real-time inference.
                                  The most important requirement in the question is that the data must stay synchronized between batch training and real-time inference . AWS documents explain that Feature Store provides both an offline store and an online store . The offline store is used for historical data, model training, and batch inference, while the online store is a low-latency, high-availability store intended for real-time lookup during inference. This dual-store design is exactly why Feature Store is used to maintain feature consistency across training and serving workflows. AWS Well-Architected guidance also explicitly says Feature Store provides online storage for real-time inference and offline storage for model training and batch inference.
                                  The other options do not solve the full problem. Athena CTAS can organize query results but does not provide a synchronized feature repository for online and offline ML use. Lake Formation governs access to data lakes but is not a feature repository for training and inference consistency. Data Wrangler Generate Data Insights is for analysis and preparation, not synchronized feature serving. Therefore, the best AWS- documented answer is A .


                                  203. Frage
                                  A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker from collecting metadata from the training jobs?

                                  Antwort: B

                                  Begründung:
                                  Amazon SageMaker automatically collects training job metadata, but you can opt out of metadata tracking when submitting a training job. This disables collection of aggregated metadata, ensuring compliance with rules that prohibit metadata collection.


                                  204. Frage
                                  ......

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