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MLA-C01 Exam Introduction | Authorized MLA-C01 Exam Dumps

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The AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification exam is one of the hottest and most industrial-recognized credentials that has been inspiring beginners and experienced professionals since its beginning. With the MLA-C01 certification exam successful candidates can gain a range of benefits which include career advancement, higher earning potential, industrial recognition of skills and job security, and more career personal and professional growth.

>> MLA-C01 Exam Introduction <<

Authorized MLA-C01 Exam Dumps | Exam Dumps MLA-C01 Demo

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Amazon MLA-C01 Exam Syllabus Topics:

Topic
Details

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 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 3

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 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 (Q23-Q28):

NEW QUESTION # 23
A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns.
The company needs a no-code solution to transform the data. The solution must store the transformed data back to the same S3 bucket for model training.
Which solution will meet these requirements?

A. Use Amazon Athena SQL queries to perform the one-hot encoding transformation.
B. Configure an AWS Glue DataBrew project that connects to the data. Use the DataBrew interactive interface to create a recipe that performs the one-hot encoding transformation. Create a job to apply the transformation and write the output back to an S3 bucket.
C. Use an AWS Glue ETL interactive notebook to perform the transformation.
D. Use Amazon Redshift Spectrum to perform the transformation.

Answer: B

Explanation:
AWS Glue DataBrew is specifically designed to provide no-code and low-code data preparation for analytics and machine learning. It supports common file formats such as Apache Parquet and integrates directly with Amazon S3.
Using DataBrew, users can visually create recipes that apply transformations such as one-hot encoding without writing any code. Once the recipe is defined, a DataBrew job can be run to process the dataset and store the transformed output back into Amazon S3.
Options B, C, and D all require writing SQL or code, which violates the no-code requirement. AWS documentation clearly identifies DataBrew as the correct service for interactive, visual data transformation at scale.
Therefore, Option A is the correct solution.

NEW QUESTION # 24
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.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
C. Use AWS Glue DataBrew built-in features to oversample the minority class.
D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Answer: D

Explanation:
Problem Description:
* The training dataset has a class imbalance, meaning one class (e.g., fraudulent transactions) has fewer samples compared to the majority class (e.g., non-fraudulent transactions). This imbalance affects the model's ability to learn patterns from the minority class.
Why SageMaker Data Wrangler?
* SageMaker Data Wrangler provides a built-in operation called "Balance Data," which includes oversampling and undersampling techniques to address class imbalances.
* Oversampling the minority class replicates samples of the minority class, ensuring the algorithm receives balanced inputs without significant additional operational overhead.
Steps to Implement:
* Import the dataset into SageMaker Data Wrangler.
* Apply the "Balance Data" operation and configure it to oversample the minority class.
* Export the balanced dataset for training.
Advantages:
* Ease of Use: Minimal configuration is required.
* Integrated Workflow: Works seamlessly with the SageMaker ecosystem for preprocessing and model training.
* Time Efficiency: Reduces manual effort compared to external tools or scripts.

NEW QUESTION # 25
A company has an ML model that is deployed to an Amazon SageMaker AI endpoint for real-time inference.
The company needs to deploy a new model. The company must compare the new model's performance to the currently deployed model's performance before shifting all traffic to the new model.
Which solution will meet these requirements with the LEAST operational effort?

A. Use AWS Lambda functions with custom logic to route traffic between the current model and the new model.
B. Deploy the new model to a separate endpoint. Use Amazon CloudFront to distribute traffic between the two endpoints.
C. Deploy the new model to a separate endpoint. Manually split traffic between the two endpoints.
D. Deploy the new model as a shadow variant on the same endpoint as the current model. Route a portion of live traffic to the shadow model for evaluation.

Answer: D

Explanation:
AWS recommends shadow testing to evaluate a new model against a production model with minimal operational overhead. Using production variants on a single SageMaker endpoint allows traffic to be routed to multiple models without managing additional endpoints.
With a shadow variant, the new model receives a copy of live traffic but does not affect production responses.
Performance metrics such as latency, accuracy, and error rates can be compared directly against the current model using Amazon CloudWatch metrics. This approach is natively supported by Amazon SageMaker Endpoints.
Options A, B, and D introduce unnecessary complexity by requiring additional endpoints, traffic routing infrastructure, or custom code.
Therefore, deploying the new model as a shadow variant on the same endpoint is the most efficient solution.

NEW QUESTION # 26
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use.
The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

A. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
B. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
C. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.
D. Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.

Answer: B

NEW QUESTION # 27
Hotspot Question
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
- An S3 event notification invokes the pipeline when new data is
uploaded.
- S3 Lifecycle rule invokes the pipeline when new data is uploaded.
- SageMaker retrains the model by using the data in the S3 bucket.
- The pipeline deploys the model to a SageMaker endpoint.
- The pipeline deploys the model to SageMaker Model Registry.

Answer:

Explanation:

NEW QUESTION # 28
......

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