MLA-C01 Online Test, MLA-C01 Praxisprüfung

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Amazon MLA-C01 Prüfungsplan:

ThemaEinzelheiten
Thema 1
  • 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.
Thema 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.
Thema 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.
Thema 4
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Prüfungsfragen mit Lösungen (Q99-Q104):

99. Frage
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?

Antwort: B


100. Frage
A travel company has trained hundreds of geographic data models to answer customer questions by using Amazon SageMaker AI. Each model uses its own inferencing endpoint, which has become an operational challenge for the company.
The company wants to consolidate the models' inferencing endpoints to reduce operational overhead.
Which solution will meet these requirements?

Antwort: A

Begründung:
AWS provides Amazon SageMaker Multi-Model Endpoints to host multiple trained models behind a single inference endpoint. This feature dynamically loads models into memory based on request traffic, significantly reducing endpoint sprawl and operational overhead.
Multi-model endpoints are ideal when an organization has hundreds or thousands of related models, such as geographic or regional models, that are not all invoked simultaneously. AWS documentation highlights this approach as a best practice for cost optimization and simplified deployment management.
Option B (multi-container endpoints) is intended for chaining a small number of models, not hundreds.
Option C does not address endpoint consolidation. Option D changes the modeling strategy and is not required.
Therefore, using a single SageMaker multi-model endpoint is the correct solution.


101. 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 data quality for the models and must receive alerts when changes in data quality occur.
Which solution will meet these requirements?

Antwort: C

Begründung:
This requirement combines asynchronous inference on large datasets with automated data quality monitoring and alerting. AWS documentation explicitly recommends Amazon SageMaker batch transform for large- scale, asynchronous inference workloads. Batch transform jobs process large datasets stored in Amazon S3 without requiring a persistent endpoint, making them cost-effective and scalable.
For data quality monitoring, Amazon SageMaker Model Monitor is the AWS-native solution. Model Monitor can be scheduled to analyze inference data, compare it against a baseline, and detect data quality issues such as missing values, schema changes, or statistical drift. When violations occur, Model Monitor emits metrics to Amazon CloudWatch, where alarms can trigger alerts.
Options A, B, and C lack ML-aware data quality monitoring capabilities. AWS Glue and Batch are not designed for model data quality analysis, and CloudTrail tracks API activity-not data quality.
AWS best practices clearly position Batch Transform + Model Monitor as the correct architecture for asynchronous inference with automated monitoring and alerting.
Therefore, Option D is the correct and AWS-verified solution.


102. Frage
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?

Antwort: C


103. Frage
An ML engineer is tuning an image classification model that shows poor performance on one of two available classes during prediction. Analysis reveals that the images whose class the model performed poorly on represent an extremely small fraction of the whole training dataset.
The ML engineer must improve the model's performance.
Which solution will meet this requirement?

Antwort: C

Begründung:
This problem describes severe class imbalance in an image classification task, where the minority class has poor predictive performance. In such cases, accuracy is a misleading metric, because a model can achieve high accuracy by predicting only the majority class. AWS ML best practices recommend using F1 score, which balances precision and recall and is more appropriate for imbalanced classification problems.
To improve performance on the minority image class, image augmentation is the preferred approach.
Augmentation techniques-such as rotation, cropping, flipping, and brightness adjustment-create realistic new training examples while preserving semantic meaning. AWS documentation recommends augmentation for computer vision workloads to improve generalization without collecting new data.
SMOTE (Options C and D) is designed for tabular data, not image data, and generating synthetic pixel-level images using SMOTE is not appropriate or supported in typical computer vision pipelines.
Option A is incorrect because optimizing for accuracy does not address minority-class performance. Option D is incorrect because SMOTE is unsuitable for images.
Therefore, optimizing for F1 score and using image augmentation on the minority class is the correct solution.


104. Frage
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