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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: ML Solution Monitoring, Maintenance, and Security24%- Monitoring and observability
  • 1. Infrastructure and data monitoring
    • 2. Model drift detection and performance monitoring
      - Security and governance
      • 1. IAM policies and access control
        • 2. Compliance and secure ML system design
          Topic 2: ML Model Development26%- Model tuning and evaluation
          • 1. Performance evaluation and metrics interpretation
            • 2. Hyperparameter tuning
              - Model selection and training
              • 1. Training models using Amazon SageMaker
                • 2. Choosing appropriate ML algorithms
                  Topic 3: Data Preparation for Machine Learning (ML)28%- Data ingestion and collection
                  • 1. Data acquisition from structured and unstructured sources
                    • 2. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue)
                      - Data preprocessing and transformation
                      • 1. Data validation and quality checks
                        • 2. Data cleaning, normalization, and feature engineering
                          Topic 4: Deployment and Orchestration of ML Workflows22%- Model deployment
                          • 1. Real-time and batch inference endpoints
                            • 2. Auto scaling and infrastructure configuration
                              - ML pipeline orchestration
                              • 1. CI/CD pipelines for ML workflows
                                • 2. Workflow automation using AWS services

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                                  Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q154-Q159):

                                  NEW QUESTION # 154
                                  An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
                                  Which solutions will mitigate this problem? (Choose two.)

                                  Answer: D,E


                                  NEW QUESTION # 155
                                  A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
                                  An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
                                  Which solution will meet these requirements with the LEAST operational overhead?

                                  Answer: A


                                  NEW QUESTION # 156
                                  A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item.
                                  Which Amazon SageMaker algorithm will meet these requirements?

                                  Answer: A


                                  NEW QUESTION # 157
                                  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 ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
                                  Which algorithm should the ML engineer use to meet this requirement?

                                  Answer: A

                                  Explanation:
                                  Why Linear Learner?
                                  * SageMaker'sLinear Learneralgorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options forweight balancing across classes.
                                  * Linear Learner can capture patterns in the data while being computationally efficient.
                                  Key Features of Linear Learner:
                                  * Automatically weights minority and majority classes.
                                  * Supports both classification and regression tasks.
                                  * Handles interdependencies among features effectively through gradient optimization.
                                  Steps to Implement:
                                  * Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.
                                  * Configure the hyperparameters to enable balanced class weights.
                                  * Train the model with the balanced dataset created using SageMaker Data Wrangler.


                                  NEW QUESTION # 158
                                  A company is using an ML model to classify motion in videos. The data is stored in MP4 format in Amazon S3. When the company created the model, the company needed 4 months to label all the video frames.
                                  The company needs to retrain the model with an existing training workflow in Amazon SageMaker AI. An ML engineer must implement a solution that decreases the labeling time.
                                  Which solution will meet these requirements?

                                  Answer: C

                                  Explanation:
                                  Option A is correct because Amazon SageMaker Ground Truth is the AWS service specifically designed to build labeled datasets for machine learning, including video classification and video frame labeling use cases. AWS documentation states that Ground Truth can take video files in MP4 format stored in Amazon S3 , automatically connect to that data, and extract video frames for labeling. This directly matches the scenario in the question, where the source data is MP4 in S3 and the company needs to relabel or expand training data for retraining an existing SageMaker workflow.
                                  Ground Truth is also the best choice because AWS documents explain that it can reduce labeling time and cost through automated data labeling (active learning) for supported built-in task types. Instead of relying only on manual human labeling for every item, Ground Truth can use ML-assisted labeling to lower the total amount of human work needed, which is exactly what the company wants after previously spending four months labeling frames.
                                  The other options do not fit as well. JumpStart provides pretrained models and solution templates, but AWS documentation does not position it as the service for running a video-frame labeling operation. Data Wrangler is for data preparation, transformation, and feature engineering, not human labeling of video frames. Amazon A2I with Rekognition is mainly documented for human review workflows such as Rekognition moderation outputs, not as the primary service for large-scale MP4 video-frame annotation used to create training datasets.


                                  NEW QUESTION # 159
                                  ......

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