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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

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

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 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q54-Q59):

NEW QUESTION # 54
A company runs training jobs on Amazon SageMaker by using a compute optimized instance.
Demand for training runs will remain constant for the next 55 weeks. The instance needs to run for 35 hours each week. The company needs to reduce its model training costs.
Which solution will meet these requirements?

A. Use SageMaker Edge Manager for the training. Specify the instance requirement in the edge device configuration. Run the training.
B. Opt in to a SageMaker Savings Plan with a 1-year term and an All Upfront payment. Run a SageMaker Training job on the instance.
C. Use a serverless endpoint with a provisioned concurrency of 35 hours for each week. Run the training on the endpoint.
D. Use the heterogeneous cluster feature of SageMaker Training. Configure the instance_type, instance_count, and instance_groups arguments to run training jobs.

Answer: B

NEW QUESTION # 55
An ML engineer is using AWS CodeDeploy to deploy new container versions for inference on Amazon ECS.
The deployment must shift 10% of traffic initially, and the remaining 90% must shift within 10-15 minutes.
Which deployment configuration meets these requirements?

A. CodeDeployDefault.LambdaCanary10Percent15Minutes
B. CodeDeployDefault.ECSCanary10Percent15Minutes
C. CodeDeployDefault.LambdaLinear10PercentEvery10Minutes
D. CodeDeployDefault.ECSAllAtOnce

Answer: B

Explanation:
AWS CodeDeploy provides predefined deployment configurations for ECS that support canary and linear traffic shifting. The ECSCanary10Percent15Minutes configuration shifts 10% of traffic initially, waits 15 minutes, and then shifts the remaining traffic.
This matches the exact requirement: a 10% initial shift followed by the remaining 90% within the specified time window.
Lambda deployment configurations are not applicable to ECS. ECSAllAtOnce does not perform gradual traffic shifting.
AWS documentation explicitly defines ECSCanary10Percent15Minutes for controlled, low-risk ECS deployments.
Therefore, Option C is the correct and AWS-verified answer.

NEW QUESTION # 56
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

A. Use an AWS Batch job to process the files and generate embeddings. Use AWS Glue to store the embeddings. Use SQL queries to perform the semantic searches.
B. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendra. Query Amazon Kendra to perform the semantic searches.
C. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucket. Query Amazon Textract to perform the semantic searches.
D. Use a custom Amazon SageMaker notebook to run a custom script to generate embeddings. Use SageMaker Feature Store to store the embeddings. Use SQL queries to perform the semantic searches.

Answer: B

NEW QUESTION # 57
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

A. Accuracy
B. Area Under the ROC Curve (AUC)
C. F1 score
D. Mean absolute error (MAE)

Answer: D

Explanation:
When predicting continuous variables, such as apartment prices, it's essential to evaluate the model's performance using appropriate regression metrics. The Mean Absolute Error (MAE) is a widely used metric for this purpose.
Understanding Mean Absolute Error (MAE):
MAE measures the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average absolute difference between predicted values and actual values, providing a straightforward interpretation of prediction accuracy.

Advantages of MAE:
* Interpretability: MAE is expressed in the same units as the target variable, making it easy to understand.
* Robustness to Outliers: Unlike metrics that square the errors (e.g., Mean Squared Error), MAE does not disproportionately penalize larger errors, making it more robust to outliers.
Comparison with Other Metrics:
* Accuracy, AUC, F1 Score: These metrics are designed for classification tasks, where the goal is to predict discrete labels. They are not suitable for regression problems involving continuous target variables.
* Mean Squared Error (MSE): While MSE also measures prediction errors, it squares the differences, giving more weight to larger errors. This can be useful in certain contexts but may be sensitive to outliers.
Conclusion:
For evaluating the performance of a model predicting apartment prices-a continuous variable-MAE is an appropriate and effective metric. It provides a clear indication of the average prediction error in the same units as the target variable, facilitating straightforward interpretation and comparison.
References:
Regression Metrics - GeeksforGeeks
Evaluation Metrics for Your Regression Model - Analytics Vidhya
Regression Metrics for Machine Learning - Machine Learning Mastery

NEW QUESTION # 58
A company's dataset for prediction analytics contains duplicate records, missing data, and unusually extreme high or low values. The company needs a solution to resolve the data quality issues quickly. The solution must maintain data integrity and have the LEAST operational overhead.
Which solution will meet these requirements?

A. Use AWS Glue DataBrew to delete duplicate records, fill missing values with medians, and replace extreme values with values in a normal range.
B. Use Amazon SageMaker Data Wrangler to delete duplicates, apply statistical modeling for missing values, and apply outlier detection algorithms.
C. Create an Amazon EMR Spark job to replace missing values with zeros and merge duplicate records.
D. Configure an AWS Glue job to identify records with missing values and extreme measurements and delete them.

Answer: A

Explanation:
AWS Glue DataBrew is designed specifically for no-code and low-code data preparation, making it the fastest and lowest-overhead solution for resolving common data quality issues. DataBrew provides built-in transformations for deduplication, missing value imputation, and outlier handling while preserving data integrity.
Option A uses standard statistical techniques such as median imputation and value normalization, which are widely accepted and maintain the distribution of the data. DataBrew jobs are fully managed and do not require infrastructure setup or maintenance.
Option B deletes records, which can lead to data loss and does not preserve integrity. Option C introduces unnecessary infrastructure complexity and uses poor data imputation practices. Option D provides advanced capabilities but requires more configuration and ML expertise, increasing operational overhead.
AWS documentation clearly positions DataBrew as the preferred solution for quick, reliable data cleaning with minimal effort.
Therefore, Option A is the correct answer.

NEW QUESTION # 59
......

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