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Amazon MLA-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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

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 Sample Questions (Q53-Q58):

NEW QUESTION # 53
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
Which solution will meet these requirements in the LEAST amount of time?

A. Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
B. Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.
C. Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
D. Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.

Answer: C

Explanation:
Amazon FSx for Lustre is designed for high-performance workloads like ML training. It provides fast, low- latency access to data by linking directly to the existing S3 bucket and caching frequently accessed files locally. This significantly improves training performance compared to directly accessing millions of files from S3. It requires minimal changes to the training job and avoids the overhead of transferring or restructuring data, making it the fastest and most efficient solution.

NEW QUESTION # 54
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

A. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
B. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
C. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
D. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Answer: B

Explanation:
SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.

NEW QUESTION # 55
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

A. AWS::SageMaker::Pipeline
B. AWS::SageMaker::Model
C. AWS::SageMaker::Endpoint
D. AWS::SageMaker::NotebookInstance

Answer: B

NEW QUESTION # 56
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score.
After several months of no change, the model's F1 score decreases significantly.
What could be the reason for the reduced F1 score?

A. Concept drift occurred in the underlying customer data that was used for predictions.
B. The original baseline data had a data quality issue of missing values.
C. The model was not sufficiently complex to capture all the patterns in the original baseline data.
D. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Answer: A

NEW QUESTION # 57
An ML engineer needs to organize a large set of text documents into topics. The ML engineer will not know what the topics are in advance. The ML engineer wants to use built-in algorithms or pre-trained models available through Amazon SageMaker AI to process the documents.
Which solution will meet these requirements?

A. Use the Sequence-to-Sequence algorithm to summarize the text and to create a set of topics based on the documents.
B. Use the BlazingText algorithm to identify the relevant text and to create a set of topics based on the documents.
C. Use the Latent Dirichlet Allocation (LDA) algorithm to process the documents and to create a set of topics based on the documents.
D. Use the Object2Vec algorithm to create embeddings and to create a set of topics based on the embeddings.

Answer: C

Explanation:
The task described is unsupervised topic modeling, where topics are unknown in advance. Latent Dirichlet Allocation (LDA) is a probabilistic generative model specifically designed to discover latent topics from a corpus of documents without labeled data. AWS provides LDA as a built-in algorithm in Amazon SageMaker, making it well suited for this requirement.
LDA models documents as mixtures of topics and topics as mixtures of words, enabling interpretable topic discovery at scale. This aligns precisely with the need to organize documents into topics when the topics are not predefined.
BlazingText is optimized for word embeddings and supervised text classification, not topic modeling.
Sequence-to-sequence models are used for translation or summarization. Object2Vec creates embeddings but does not itself perform topic discovery without additional clustering steps.
Therefore, LDA is the correct and purpose-built solution.

NEW QUESTION # 58
......

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