Home > Latest News > AWS Certified Machine Learning Engineer - Associate (MLA-C01) Dumps

ALL IT Certifications

AWS Certified Machine Learning Engineer - Associate (MLA-C01) Dumps

December 05,2024

If you are aiming to advance your career in machine learning, the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam is a significant step towards validating your skills and expertise. One of the most effective ways to ensure you pass this challenging exam is to use the latest AWS Certified Machine Learning Engineer - Associate (MLA-C01) dumps from Passcert. These dumps comprehensively cover all the essential objectives, helping you navigate through the exam with ease. By leveraging these AWS Certified Machine Learning Engineer - Associate (MLA-C01) Dumps, you can confidently test your knowledge and skills in AWS ML services, ultimately positioning yourself for success in machine learning-related roles.

AWS Certified Machine Learning Engineer - Associate (MLA-C01) Dumps

Overview of the AWS Certified Machine Learning Engineer - Associate (MLA-C01) Exam

The AWS Certified Machine Learning Engineer - Associate certification is designed for professionals who have hands-on experience in deploying, operating, and maintaining machine learning (ML) solutions using AWS Cloud. As businesses continue to leverage machine learning to enhance their data-driven decision-making processes, there is a growing demand for certified professionals with proven skills in operationalizing machine learning solutions.

The MLA-C01 exam specifically tests your ability to implement machine learning workloads in production environments and operationalize them effectively. The exam validates a range of competencies, from data preparation and model development to deployment and orchestration of ML workflows. By achieving this certification, you boost your credibility and significantly enhance your career prospects in the highly sought-after field of machine learning engineering.

Why Become an AWS Certified Machine Learning Engineer - Associate?

Becoming an AWS Certified Machine Learning Engineer - Associate (MLA-C01) offers several benefits:

● Career Advancement: With the rapid adoption of machine learning across industries, AWS-certified professionals are in high demand. This certification opens up opportunities in various roles such as ML engineers, data scientists, MLOps engineers, and DevOps developers.

● Industry Recognition: AWS is a leader in the cloud computing space, and certification validates your ability to work with cutting-edge machine learning technologies within the AWS ecosystem.

● Hands-on Skills: The certification exam validates practical experience, ensuring that you're ready to handle real-world machine learning challenges with AWS tools and services.

Key Responsibilities of a Machine Learning Engineer (Associate)

As an AWS Certified Machine Learning Engineer - Associate, you'll be expected to perform the following tasks:

● Ingest and Prepare Data for ML: You'll need to prepare data pipelines, ensuring the data is clean, transformed, and ready for machine learning model training.

● Model Development: You'll choose appropriate modeling techniques, train machine learning models, and fine-tune them for optimal performance.

● Deployment: You will be responsible for deploying models to production environments and scaling them as required.

● Continuous Monitoring: Keeping an eye on the models and infrastructure to ensure everything is functioning as expected.

This skill set is critical in today's data-driven world, and this certification proves that you have the capability to drive ML initiatives within organizations.

Target Candidate Description for the AWS MLA-C01 Exam

The ideal candidate for the AWS Certified Machine Learning Engineer - Associate exam should have the following:

● Experience with AWS Services: At least 1 year of hands-on experience working with Amazon SageMaker, a key service for deploying and managing machine learning models, as well as other relevant AWS services like Lambda, EC2, and S3.

● Experience in Related Roles: Ideal candidates might have backgrounds as backend developers, data engineers, MLOps engineers, or data scientists. These professionals are already familiar with the broader software development lifecycle and the application of machine learning concepts.

The exam is particularly useful for those looking to specialize in machine learning workflows on AWS, leveraging cloud-native tools and practices to optimize models for production use.

Exam Overview: Format and Cost

The MLA-C01 exam tests candidates' skills and knowledge in various domains of machine learning. Here's a brief overview of the exam details:

Category: Associate

Exam Duration: 130 minutes

Number of Questions: 65 multiple-choice and multiple-response questions

Cost: $150 USD

Languages Offered: English, Japanese, Korean, and Simplified Chinese

Testing Options: You can take the exam at Pearson VUE testing centers or opt for an online proctored exam from the comfort of your home.

This exam is designed to test both theoretical knowledge and practical skills related to machine learning on AWS. It's not just about memorizing facts; candidates must demonstrate the ability to apply ML concepts in real-world scenarios.

Exam Content Outline

The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam content is divided into four key domains, each focusing on different areas of machine learning and AWS service integration. Below is an overview of the content outline and the percentage each domain contributes to the exam score:

Domain 1: Data Preparation for Machine Learning (ML) (28% of scored content)

Task Statement 1.1: Ingest and store data.

Task Statement 1.2: Transform data and perform feature engineering.

Task Statement 1.3: Ensure data integrity and prepare data for modeling.

Domain 2: ML Model Development (26% of scored content)

Task Statement 2.1: Choose a modeling approach.

Task Statement 2.2: Train and refine models.

Task Statement 2.3: Analyze model performance.

Domain 3: Deployment and Orchestration of ML Workflows (22% of scored content)

Task Statement 3.1: Select deployment infrastructure based on existing architecture and requirements.

Task Statement 3.2: Create and script infrastructure based on existing architecture and requirements.

Task Statement 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.

Domain 4: ML Solution Monitoring, Maintenance, and Security (24% of scored content)

Task Statement 4.1: Monitor model inference.

Task Statement 4.2: Monitor and optimize infrastructure and costs.

Task Statement 4.3: Secure AWS resources.

Share AWS Certified Machine Learning Engineer - Associate (MLA-C01) Free Dumps

1. A company stores its training datasets on Amazon S3 in the form of tabular data running into millions of rows. The company needs to prepare this data for Machine Learning jobs. The data preparation involves data selection, cleansing, exploration, and visualization using a single visual interface.

Which Amazon SageMaker service is the best fit for this requirement?

A. Amazon SageMaker Feature Store

B. Amazon SageMaker Data Wrangler

C. SageMaker Model Dashboard

D. Amazon SageMaker Clarify

Answer: B

2. Which of the following strategies best aligns with the defense-in-depth security approach for generative AI applications on AWS?

A. Relying solely on data encryption to protect the AI training data

B. Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities

C. Using a single authentication mechanism for all users and services accessing the AI models

D. Implementing a single-layer firewall to block unauthorized access to the AI models

Answer: B

3. A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company needs to use the central model registry to manage different versions of models in the application.

Which action will meet this requirement with the LEAST operational overhead?

A. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.

B. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.

C. Use the SageMaker Model Registry and model groups to catalog the models.

D. Use the SageMaker Model Registry and unique tags for each model version.

Answer: C

4. A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company is experimenting with consecutive training jobs.

How can the company MINIMIZE infrastructure startup times for these jobs?

A. Use Managed Spot Training.

B. Use SageMaker managed warm pools.

C. Use SageMaker Training Compiler.

D. Use the SageMaker distributed data parallelism (SMDDP) library.

Answer: B

5. 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.

The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.

Which algorithm should the ML engineer use to meet this requirement?

A. LightGBM

B. Linear learner

C. К-means clustering

D. Neural Topic Model (NTM)

Answer: A

6. A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.

Which action will meet this requirement?

A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.

B. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.

C. Use AWS Glue Data Quality to monitor bias.

D. Use SageMaker notebooks to compare the bias.

Answer: A

7. 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.

Which AWS service or feature can aggregate the data from the various data sources?

A. Amazon EMR Spark jobs

B. Amazon Kinesis Data Streams

C. Amazon DynamoDB

D. AWS Lake Formation

Answer: A

8. 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.

After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.

Which solution will meet these requirements?

A. Use Amazon Athena to automatically detect the anomalies and to visualize the result.

B. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

C. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

D. Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Answer: C

9. 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 training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.

Which action will meet this requirement with the LEAST operational overhead?

A. Use AWS Glue to transform the categorical data into numerical data.

B. Use AWS Glue to transform the numerical data into categorical data.

C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

D. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Answer: C

10. 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.

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

Related Suggestion: AWS Certified AI Practitioner AIF-C01 Dumps September 02,2024; AWS Certified Machine Learning - Specialty (MLS-C01) Exam Dumps January 02,2024; AWS Certified Security - Specialty (SCS-C02) Dumps September 02,2023; AWS Certified DevOps Engineer – Professional DOP-C02 Dumps August 24,2023; AWS Certified Solutions Architect Professional (SAP-C02) Real Dumps June 20,2023; AWS Certified Database – Specialty (DBS-C01) Dumps May 19,2023; 2023 Updated AWS Certified Cloud Practitioner CLF-C01 Dumps May 04,2023; Tips To Pass AWS Certified Developer - Associate DVA-C02 exam April 29,2023; How To Pass AWS Certified Solutions Architect – Associate SAA-C03 Exam? April 08,2023; AWS Certified Cloud Practitioner (CLF-C02) Exam Dumps Replacement of CLF-C01 September 26,2023; AWS Certification Pathway 2023 For Your Career August 12,2023