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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 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	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.

>> Latest MLA-C01 Exam Objectives <<

Updated MLA-C01 - Latest AWS Certified Machine Learning Engineer - Associate Exam Objectives

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q16-Q21):

NEW QUESTION # 16
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

A. Use Amazon Mechanical Turk jobs to detect duplicates.
B. Use Amazon QuickSight ML Insights to build a custom deduplication model.
C. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
D. Use the AWS Glue FindMatches transform to detect duplicates.

Answer: D

Explanation:
Scenario:The dataset contains duplicate records that need to be detected with minimal code development.
Why FindMatches in AWS Glue?
* Purpose-Built for Deduplication:The FindMatches transform in AWS Glue is specifically designed to identify duplicate records in structured or semi-structured datasets.
* Machine Learning-Based:It uses ML to identify duplicates based on configurable thresholds and provides flexibility for tuning accuracy.
* Low Code Overhead:Minimal development effort is required as Glue provides an interactive console for configuring and running FindMatches transforms.
Steps to Implement:
* Prepare the Data:Upload the unstructured dataset to an S3 bucket and define a schema if needed.
* Create a Glue Job:
* Use the AWS Glue Studio to create a job and select the FindMatches transform.
* Specify key attributes for deduplication.
* Run and Evaluate:Execute the Glue job, and review the results for duplicates.
* Resolve Duplicates:Export results to an S3 bucket or process them as needed.
References:
* AWS Glue FindMatches Documentation
* FindMatches Transform Example

NEW QUESTION # 17
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

A. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
B. Apply label encoding to the color categories. Automatically assign each color a unique integer.
C. Perform dimensionality reduction on the color categories.
D. Implement padding to ensure that all color feature vectors have the same length.

Answer: A

Explanation:
One-hot encodingis the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.

NEW QUESTION # 18
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models.
The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?

A. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.
B. Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.
C. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
D. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.

Answer: A

Explanation:
Amazon SageMaker batch transform is ideal for obtaining inferences from large datasets in an asynchronous manner, as it processes data in batches rather than requiring real-time inputs.
SageMaker Model Monitor allows scheduled monitoring of data quality, detecting shifts in input data characteristics, and generating alerts when changes in data quality occur.
This solution provides a fully managed, efficient way to handle both asynchronous inference and data quality monitoring with minimal operational overhead.

NEW QUESTION # 19
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?

A. Run the primary node, core nodes, and task nodes on Spot Instances.
B. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
C. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
D. Run the primary node, core nodes, and task nodes on On-Demand Instances.

Answer: C

Explanation:
For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additionalprocessing but do not store data, can use Spot Instances to reduce costs without compromising the cluster's resilience or data integrity. This configuration balances cost-effectiveness and reliability.

NEW QUESTION # 20
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

A. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
B. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
C. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
D. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.

Answer: D

Explanation:
Amazon Kinesis Data Firehose allows for near real-time data streaming. Setting thebuffering hintsto zero or a very small value minimizes the buffering delay and ensures that records are delivered to the destination (Amazon OpenSearch Service) as quickly as possible. Additionally, tuning thebatch sizein thePutRecordBatchoperation can further optimize the data ingestion for sub-second latency. This approach minimizes latency while maintaining the operational simplicity of using Firehose.

NEW QUESTION # 21
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