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[General] Real Amazon MLA-C01 Exam Dumps - Free MLA-C01 Study Material

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【General】 Real Amazon MLA-C01 Exam Dumps - Free MLA-C01 Study Material

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Real MLA-C01 Exam Dumps - First-grade Free AWS Certified Machine Learning Engineer - Associate Study MaterialThree versions of MLA-C01 test materials are available. You can choose the one you prefer to have a practice. MLA-C01 PDF version is printable, and if you prefer to practice on paper, this version will be your best choice. You can print them into hard one, and take them with you. MLA-C01 Soft test engine can stimulate the real exam environment, and this version will help you to relieve your nerves. MLA-C01 Online test engine supports all web browsers, with this version you can have a brief review of what you have finished last time.
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 (Q134-Q139):NEW QUESTION # 134
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels.
The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?
  • A. Accuracy
  • B. Specificity
  • C. Recall
  • D. Precision
Answer: A

NEW QUESTION # 135
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?
  • A. Create code to evaluate each instance's memory and compute usage.
  • B. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
  • C. Check AWS CloudTrail event history for the creation of the resources.
  • D. Run AWS Compute Optimizer.
Answer: D

NEW QUESTION # 136
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 EMR Spark jobs
  • C. AWS Lake Formation
  • D. Amazon DynamoDB
Answer: B
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 # 137
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.
  • B. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre- processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
  • C. Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF- IDF) scores to identify and extract relevant keywords.
  • D. Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
Answer: A

NEW QUESTION # 138
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?
  • A. Increase the learning rate.
  • B. Introduce early stopping.
  • C. Increase the size of the test set.
  • D. Decrease the learning rate.
Answer: D

NEW QUESTION # 139
......
Our Amazon MLA-C01 practice exam also provides users with a feel for what the real Amazon MLA-C01 exam will be like. Both AWS Certified Machine Learning Engineer - Associate (MLA-C01) practice exams are the same as the Actual MLA-C01 Test and give candidates the experience of taking the real AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam. These MLA-C01 practice tests can be customized according to your needs.
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