MLA-C01 Latest Test Prep | MLA-C01 Free Practice Exams

Blog Article

Tags: MLA-C01 Latest Test Prep, MLA-C01 Free Practice Exams, Associate MLA-C01 Level Exam, MLA-C01 Valid Exam Camp, MLA-C01 Certification Exam Cost

Why we are ahead of the other sites in the IT training industry? Because the information we provide have a wider coverage, higher quality, and the accuracy is also higher. So PracticeVCE is not only the best choice for you to participate in the Amazon Certification MLA-C01 Exam, but also the best protection for your success.

Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

>> MLA-C01 Latest Test Prep <<

Pass Guaranteed Quiz Amazon - Updated MLA-C01 Latest Test Prep

In recent, PracticeVCE began to provide you with the latest exam dumps about IT certification test, such as Amazon MLA-C01 Certification Dumps are developed based on the latest IT certification exam. PracticeVCE Amazon MLA-C01 certification training dumps will tell you the latest news about the exam. The changes of the exam outline and those new questions that may appear are included in our dumps. So if you want to attend IT certification exam, you'd better make the best of PracticeVCE questions and answers. Only in this way can you prepare well for the exam.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q42-Q47):

NEW QUESTION # 42
A company has an ML model that needs to run one time each night to predict stock values. The model input is
3 MB of data that is collected during the current day. The model produces the predictions for the next day.
The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

A. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
B. Use a multi-model serverless endpoint. Enable caching.
C. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
D. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.

Answer: A

Explanation:
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.

NEW QUESTION # 43
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?

A. Amazon Kinesis Data Streams
B. Amazon DynamoDB
C. Amazon EMR Spark jobs
D. AWS Lake Formation

Answer: C

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 aclass imbalancein the dataset andinterdependenciesamong features that need to be addressed.
* The solution requiresdata aggregationfrom diverse sources for centralized processing.
* Why AWS Lake Formation?
* AWS Lake Formationis 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.
References:
* AWS Lake Formation Documentation
* AWS Glue for Data Preparation

NEW QUESTION # 44
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 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.
B. 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.
C. 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.
D. 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.

Answer: C

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 # 45
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Deploy models by using AWS Lambda functions.
B. Use PyTorch or TensorFlow with the distributed training option.
C. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
D. Use AWS Trainium instances for training.
E. Use Amazon SageMaker Ground Truth for data labeling.

Answer: C,D

Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 46
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

A. Initiate a manual Model Monitor job that uses the most recent production data.
B. Adjust the model's parameters and hyperparameters.
C. Include additional data in the existing training set for the model. Retrain and redeploy the model.
D. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

Answer: D

Explanation:
When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.

NEW QUESTION # 47
......

After successful competition of the MLA-C01 certification, the certified candidates can put their career on the right track and achieve their professional career objectives in a short time period. However, to pass the MLA-C01 Exam you have to prepare well. For the quick MLA-C01 exam preparation the MLA-C01 Questions are the right choice.

MLA-C01 Free Practice Exams: https://www.practicevce.com/Amazon/MLA-C01-practice-exam-dumps.html

Report this page

MLA-C01 LATEST TEST PREP | MLA-C01 FREE PRACTICE EXAMS

MLA-C01 Latest Test Prep | MLA-C01 Free Practice Exams