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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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 (Q94-Q99):

NEW QUESTION # 94
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?

Answer: B

Explanation:
Option D is correct because Amazon SageMaker Model Registry is the AWS feature specifically designed to catalog models, manage model versions, and organize them into model groups . AWS documentation states that you can create a Model Group and then register each model you train , and the Model Registry adds it to the Model Group as a new model version . That directly matches the requirement to automatically create versions of ML models as models are created.
The AWS docs also describe a standard workflow in which you first create a Model Group, then create an ML pipeline, and for each run of the ML pipeline , create a model version that is registered in that group. This means Model Registry supports exactly the kind of lifecycle management and version tracking needed in an MLOps workflow where new model artifacts are produced over time and need consistent governance.
The other options do not meet the requirement. Amazon ECR stores container images, not model version history. SageMaker Marketplace model packages are for discovering or subscribing to third-party or prebuilt solutions, not for automatically versioning your organization's trained models. SageMaker ML Lineage Tracking helps trace artifacts, datasets, and processing steps for reproducibility, but AWS documentation distinguishes lineage from the actual model version management capability provided by Model Registry. Model Registry is the AWS-native feature for keeping versioned records of trained models and managing approval status, deployment readiness, and metadata in a centralized way.
Therefore, the fully verified AWS-docs answer is D , because SageMaker Model Registry is the service purpose-built to create and manage model versions as new models are produced.


NEW QUESTION # 95
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: A,E


NEW QUESTION # 96
A company is creating an application that will recommend products for customers to purchase.
The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

Answer: C


NEW QUESTION # 97
A company stores training data as a .csv file in an Amazon S3 bucket. The company must encrypt the data and must control which applications have access to the encryption key. Which solution will meet these requirements?

Answer: B

Explanation:
The correct approach is to create a new AWS KMS key and use the AWS Encryption CLI to encrypt the file with that key. This ensures the data in S3 is encrypted, and access to the encryption key can be controlled through KMS key policies and IAM permissions, meeting both encryption and access control requirements.


NEW QUESTION # 98
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

Answer:

Explanation:

Explanation:
Use case 1:
Summarize the text of a research paper
## Sequence-to-Sequence (seq2seq) algorithm
Why:
Seq2seq models are designed for natural language generation tasks such as text summarization, translation, and paraphrasing. AWS documentation explicitly lists text summarization as a primary use case for the SageMaker seq2seq algorithm.
Use case 2:
Scan every pixel of an image to help self-driving cars identify objects in their path
## Semantic segmentation algorithm
Why:
Semantic segmentation performs pixel-level classification, assigning a class label to every pixel in an image.
This is exactly what is required for applications such as autonomous driving, road scene understanding, and object boundary detection.
Use case 3:
Identify abnormal data points in a dataset
## Random Cut Forest (RCF) algorithm
Why:
Random Cut Forest is an unsupervised anomaly detection algorithm. AWS SageMaker RCF is purpose-built to identify outliers, unusual patterns, and anomalies in numerical datasets, making it ideal for fraud detection, monitoring, and abnormal data point detection.


NEW QUESTION # 99
......

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