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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 2
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.
Topic 3
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
Topic 4
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q33-Q38):

NEW QUESTION # 33
An ML engineer is building a model to predict house and apartment prices. The model uses three features:
Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment.
The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment.
Which solution will meet these requirements?

Answer: B

Explanation:
In regression problems such as house price prediction, extreme values can significantly distort model learning.
In this dataset, the presence of a large mansion and an extremely small apartment represents clear outliers in the Square Meters feature. According to AWS Machine Learning best practices, outliers can disproportionately influence loss functions (such as mean squared error), leading to poor predictions for the majority of typical data points.
Removing these outliers helps the model focus on learning patterns that apply to the majority of houses and apartments, which aligns with the requirement to produce accurate predictions for typical properties. After removing outliers, applying a log transformation to the Square Meters feature further improves model performance by reducing skewness and stabilizing variance. Log transformations are commonly recommended in AWS and general ML documentation when numerical features span multiple orders of magnitude.
Option B is incorrect because normalization alone does not address the undue influence of extreme outliers.
Option C and D are incorrect because one-hot encoding is intended for categorical variables, not continuous numerical features such as square meters.
Therefore, removing outliers and applying a log transformation is the most statistically sound preprocessing approach.


NEW QUESTION # 34
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?

Answer: A

Explanation:
AWS provides Amazon SageMaker Model Monitor as a native solution for detecting data drift and model quality issues in production ML pipelines. Model Monitor continuously analyzes incoming inference data and compares it with baseline training data to identify schema drift, feature distribution drift, and data quality anomalies.
When drift thresholds are violated, Model Monitor generates CloudWatch metrics and alerts. These alerts can directly trigger an AWS Lambda function, which can then programmatically initiate a SageMaker retraining job or start a SageMaker Pipeline execution. This design is explicitly documented by AWS as the recommended architecture for automated retraining workflows.
Option A is incorrect because AWS Glue is a data integration service and does not provide ML-specific drift detection capabilities.
Option B is incorrect because Apache Flink is designed for stream processing, not ML data drift detection.
Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence metrics, not ML feature drift.
Therefore, using SageMaker Model Monitor with AWS Lambda automation is the correct, AWS-native solution for drift-driven retraining.


NEW QUESTION # 35
A company uses Amazon SageMakerAI to support ML workflows such as model training and deployment.
Select the correct registry from the following list to meet the requirements for each use case with the LEAST operational overhead. Each registry should be selected one or more times. (Select FOUR.)
* Amazon Elastic Container Registry (Amazon ECR)
* SageMaker Model Registry

Answer:

Explanation:

Explanation:
* Tag model packages and use model package groups that include container images for training and deployment # SageMaker Model Registry
* Store predefined language packages, kernels, and relevant dependencies # Amazon Elastic Container Registry (Amazon ECR)
* Organize models and their images into model groups for better discoverability # SageMaker Model Registry
* Pull built-in SageMaker AI images for model training # Amazon Elastic Container Registry (Amazon ECR) The correct hotspot mapping is based on the different purposes of SageMaker Model Registry and Amazon ECR .
For model packages , model package groups , and model discoverability, the correct choice is SageMaker Model Registry . AWS documentation states that a model package group is a collection of versioned model packages, and each version can contain the model artifacts, metadata, and container information used for deployment. AWS also documents that registered models can be organized into groups to support governance, discoverability, and lifecycle management. That is why both " tag model packages and use model package groups " and " organize models and their images into model groups " map to SageMaker Model Registry
.
For storing software environments and pulling training images, the correct choice is Amazon ECR . AWS documentation says SageMaker uses Docker container images , and these images contain the software stack such as frameworks, language packages, kernels, and dependencies. AWS also states that SageMaker provides prebuilt Docker images for training and inference, and these are referenced through Amazon ECR image URIs . Therefore, both " store predefined language packages, kernels, and relevant dependencies " and " pull built-in SageMaker AI images for model training " map to Amazon ECR .


NEW QUESTION # 36
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?

Answer: C

Explanation:
By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user ' s business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group.
This approach is secure, scalable, and aligns with AWS best practices for access control.


NEW QUESTION # 37
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

Answer: D

Explanation:
Amazon Kinesis Data Firehose allows for near real-time data streaming. Setting thebuffering hintsto zero or a very small value minimizes the buffering delay and ensures that records are delivered to the destination (Amazon OpenSearch Service) as quickly as possible. Additionally, tuning thebatch sizein thePutRecordBatchoperation can further optimize the data ingestion for sub-second latency. This approach minimizes latency while maintaining the operational simplicity of using Firehose.


NEW QUESTION # 38
......

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