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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
  • 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.
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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q62-Q67):

NEW QUESTION # 62
A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1.
The company needs to implement a solution to minimize the risk of v2 generating incorrect output in production. The solution must prevent any disruption of production traffic during the change to v2.
Which solution will meet these requirements?

Answer: A

Explanation:
A shadow variant allows the new model (v2) to receive a copy of 100% of production traffic while only v1's outputs are returned to users. This enables safe side-by-side evaluation of v2 without impacting production responses, minimizing risk and ensuring no disruption of live traffic until v2 is validated and promoted.


NEW QUESTION # 63
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 64
Hotspot Question
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
- Feature splitting
- Logarithmic transformation
- One-hot encoding
- Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Answer:

Explanation:


NEW QUESTION # 65
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 # 66
A company uses an NFS-based data store to store data for ML training. Linux-based systems access the data store.
The company needs a hybrid system to make the shared data store accessible to on-premises servers and Amazon SageMaker AI notebooks that will consume the data. File locking is required for the data producers.
Which AWS storage solution will meet these requirements?

Answer: D

Explanation:
Option B is correct because Amazon EFS is a managed NFS-based shared file system that supports Linux clients, hybrid access, and file-system semantics such as file locking . AWS documentation states that Amazon EFS provides strong data consistency and file locking , which is an explicit requirement in the question. AWS also states that you can mount EFS file systems on on-premises data center servers when connected to your Amazon VPC through AWS Direct Connect or Site-to-Site VPN , which directly satisfies the hybrid access requirement.
For the SageMaker side, AWS documentation states that SageMaker AI creates a default Amazon EFS volume for Studio domains, and you can also add a custom Amazon EFS file system so domain users can access it in SageMaker Studio. AWS also notes that SageMaker AI Studio notebooks use Amazon EFS as a persistent storage layer. That makes EFS the most natural AWS storage service for a shared file repository that must be accessible from SageMaker notebooks.
The other choices are weaker. Amazon S3 with Mountpoint is object storage access and is not the best answer for a shared NFS-style data store with file locking requirements. FSx for Lustre is high-performance parallel file storage and can support on-premises bursting, but the question explicitly starts from an NFS- based shared data store, and EFS is AWS's native NFS file service. Amazon EBS cannot be mounted simultaneously across on-premises servers and SageMaker notebooks as a shared, managed hybrid file system. Therefore, the best verified AWS-docs answer is B .


NEW QUESTION # 67
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