Certificate Amazon MLA-C01 Exam - MLA-C01 PDF Guide

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Amazon MLA-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Exam Number:MLA-C01
Real Exam Qty:65 scored questions + 15 unscored questions
Available Languages:English, Simplified Chinese, Japanese, Korean
Certificate Validity Period:3 years
Passing Score:720/1000
Exam Price:USD 150
Exam Duration:130 minutes
Related Certifications:AWS Certified Data Engineer – Associate
AWS Certified DevOps Engineer – Professional
AWS Certified Solutions Architect – Associate
AWS Certified AI Practitioner
Exam Format:Multiple choice, Matching, Multiple response, Ordering
Recommended Training:AWS Skill Builder - ML Engineer Associate Exam Prep
Amazon SageMaker Documentation
Exam Registration:AWS Certification Registration
Sample Questions:Amazon MLA-C01 Sample Questions
Exam Way:Online proctored or test center exam
Pre Condition:Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles
Official Syllabus URL:https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q143-Q148):

NEW QUESTION # 143
A company is developing ML models by using PyTorch and TensorFlow estimators with Amazon SageMaker AI. An ML engineer configures the SageMaker AI estimator and now needs to initiate a training job that uses a training dataset.
Which SageMaker AI SDK method can initiate the training job?

Answer: C

Explanation:
In the Amazon SageMaker Python SDK, the fit() method is used to start a training job after an estimator has been configured. AWS documentation explicitly states that once an estimator (such as PyTorch or TensorFlow) is defined with parameters like instance type, framework version, and hyperparameters, the fit() method is responsible for launching the training process.
The fit() method accepts the training data location (commonly an Amazon S3 URI) and initiates the managed training job on SageMaker infrastructure. SageMaker then provisions the required compute resources, stages the data, executes the training script, and stores model artifacts in Amazon S3.
The create_model() method is used after training to create a SageMaker model object from trained artifacts.
The deploy() method deploys a trained model to an endpoint for inference. The predict() method is used only after deployment to request predictions from an endpoint.
AWS documentation clearly separates these lifecycle steps and identifies fit() as the correct method to initiate training.
Therefore, Option A is the correct and AWS-verified answer.


NEW QUESTION # 144
A company wants to use Amazon SageMaker AI to host an ML model that runs on CPU for real-time predictions. The model has intermittent traffic during business hours and periods of no traffic after business hours.
Which hosting option will serve inference requests in the MOST cost-effective manner?

Answer: D

Explanation:
AWS recommends SageMaker Serverless Inference for workloads with intermittent or unpredictable traffic.
Serverless inference automatically scales compute resources to zero when idle, eliminating costs during periods with no traffic.
For business-hour traffic spikes, provisioned concurrency ensures low-latency responses while still avoiding the cost of continuously running instances. This model is especially cost-effective for CPU-based inference workloads.
Real-time endpoints incur costs even when idle, and asynchronous inference is designed for long-running jobs rather than low-latency predictions.
AWS documentation explicitly states that Serverless Inference is the most cost-effective option for intermittent real-time workloads.
Therefore, Option B is the correct choice.


NEW QUESTION # 145
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

Answer: A


NEW QUESTION # 146
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
Which solution will meet these requirements?

Answer: D

Explanation:
Amazon SageMaker automatically tracks theInvocationsmetric, which represents the number of API calls made to the endpoint, inAmazon CloudWatch. By adding this metric to a CloudWatch dashboard, you can monitor the endpoint's activity in real-time. Setting up aCloudWatch alarmallows the system to send notifications whenever the API call events exceed the defined threshold, meeting both the monitoring and notification requirements efficiently.


NEW QUESTION # 147
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Which AWS service or feature can aggregate the data from the various data sources?

Answer: A

Explanation:
* Problem Description:
* The dataset includes multiple data sources:
* Transaction logs and customer profiles in Amazon S3.
* Tables in an on-premises MySQL database.
* There is a class imbalance in the dataset and interdependencies among features that need to be addressed.
* The solution requires data aggregation from diverse sources for centralized processing.
* Why AWS Lake Formation?
* AWS Lake Formation is designed to simplify the process of aggregating, cataloging, and securing data from various sources, including S3, relational databases, and other on-premises systems.
* It integrates with AWS Glue for data ingestion and ETL (Extract, Transform, Load) workflows, making it a robust choice for aggregating data from Amazon S3 and on-premises MySQL databases.
* How It Solves the Problem:
* Data Aggregation: Lake Formation collects data from diverse sources, such as S3 and MySQL, and consolidates it into a centralized data lake.
* Cataloging and Discovery: Automatically crawls and catalogs the data into a searchable catalog, which the ML engineer can query for analysis or modeling.
* Data Transformation: Prepares data using Glue jobs to handle preprocessing tasks such as addressing class imbalance (e.g., oversampling, undersampling) and handling interdependencies among features.
* Security and Governance: Offers fine-grained access control, ensuring secure and compliant data management.
* Steps to Implement Using AWS Lake Formation:
* Step 1: Set up Lake Formation and register data sources, including the S3 bucket and on-premises MySQL database.
* Step 2: Use AWS Glue to create ETL jobs to transform and prepare data for the ML pipeline.
* Step 3: Query and access the consolidated data lake using services such as Athena or SageMaker for further ML processing.
* Why Not Other Options?
* Amazon EMR Spark jobs: While EMR can process large-scale data, it is better suited for complex big data analytics tasks and does not inherently support data aggregation across sources like Lake Formation.
* Amazon Kinesis Data Streams: Kinesis is designed for real-time streaming data, not batch data aggregation across diverse sources.
* Amazon DynamoDB: DynamoDB is a NoSQL database and is not suitable for aggregating data from multiple sources like S3 and MySQL.
Conclusion: AWS Lake Formation is the most suitable service for aggregating data from S3 and on-premises MySQL databases, preparing the data for downstream ML tasks, and addressing challenges like class imbalance and feature interdependencies.
AWS Lake Formation Documentation
AWS Glue for Data Preparation


NEW QUESTION # 148
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