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Amazon MLA-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Exam Number:	MLA-C01
Related Certifications:	AWS Certified Data Engineer – Associate AWS Certified DevOps Engineer – Professional AWS Certified Solutions Architect – Associate AWS Certified AI Practitioner
Real Exam Qty:	65 scored questions + 15 unscored questions
Available Languages:	Japanese, Simplified Chinese, Korean, English
Exam Duration:	130 minutes
Certificate Validity Period:	3 years
Passing Score:	720/1000
Exam Price:	USD 150
Exam Format:	Multiple response, Matching, Multiple choice, Ordering
Recommended Training:	Amazon SageMaker Documentation AWS Skill Builder - ML Engineer Associate Exam Prep
Exam Registration:	AWS Certification Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or test center exam
Pre Condition:	Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles
Official Syllabus URL:	https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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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 (Q216-Q221):

NEW QUESTION # 216
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.
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 AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
B. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
C. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
D. Use Amazon Athena to automatically detect the anomalies and to visualize the result.

Answer: B

Explanation:
Amazon SageMaker Data Wrangler is a comprehensive tool that streamlines the process of data preparation and offers built-in capabilities for anomaly detection and visualization.
Key Features of SageMaker Data Wrangler:
* Data Importation: Connects seamlessly to various data sources, including Amazon S3 and on-premises databases, facilitating the aggregation of transaction logs, customer profiles, and MySQL tables.
* Anomaly Detection: Provides built-in analyses to detect anomalies in time series data, enabling the identification of outliers that may indicate fraudulent activities.
* Visualization: Offers a suite of visualization tools, such as histograms and scatter plots, to help understand data distributions and relationships, which are crucial for feature engineering and model development.
Implementation Steps:
* Data Aggregation:
* Import data from Amazon S3 and on-premises MySQL databases into SageMaker Data Wrangler.
* Utilize Data Wrangler's data flow interface to combine and preprocess datasets, ensuring a unified dataset for analysis.
* Anomaly Detection:
* Apply the anomaly detection analysis feature to identify outliers in the dataset.
* Configure parameters such as the anomaly threshold to fine-tune the detection sensitivity.
* Visualization:
* Use built-in visualization tools to create charts and graphs that depict data distributions and highlight anomalies.
* Interpret these visualizations to gain insights into potential fraud patterns and feature interdependencies.
Advantages of Using SageMaker Data Wrangler:
* Integrated Workflow: Combines data preparation, anomaly detection, and visualization within a single interface, streamlining the ML development process.
* Operational Efficiency: Reduces the need for multiple tools and complex integrations, thereby minimizing operational overhead.
* Scalability: Handles large datasets efficiently, making it suitable for extensive transaction logs and customer profiles.
By leveraging SageMaker Data Wrangler, the ML engineer can effectively detect anomalies and visualize results, facilitating the development of a robust fraud detection model.
Analyze and Visualize - Amazon SageMaker
Transform Data - Amazon SageMaker

NEW QUESTION # 217
A company uses a batching solution to process data analytics each day. The company wants to build an analytics platform to provide near real-time updates. The company wants to use open source technology and does not want to manage or scale the infrastructure.
Which solution will meet these requirements?

A. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless clusters to process the data.
B. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) Provisioned clusters. Configure the clusters based on data volume.
C. Create data streams in Amazon Kinesis Data Streams. Use AWS Application Auto Scaling to scale the infrastructure.
D. Create self-hosted Apache Flink applications on Amazon EC2. Run the applications as containers.

Answer: A

Explanation:
The correct answer is A. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless clusters to process the data.
Amazon MSK Serverless allows organizations to run Apache Kafka workloads without managing or provisioning the underlying infrastructure. Unlike MSK Provisioned clusters (Option B), serverless clusters automatically scale to accommodate variable workloads and handle operational tasks such as patching, scaling, and monitoring. This meets the company's requirement to avoid managing or scaling infrastructure while providing near real-time streaming analytics.
Option C, using Amazon Kinesis Data Streams with Application Auto Scaling, provides a managed streaming solution but is not based on open-source technology. While Kinesis can handle near real-time data, the question specifically emphasizes the company's desire for open-source technology, which points toward Kafka.
Option D, self-hosting Apache Flink on EC2, requires manual management of servers, scaling, patching, and container orchestration. This approach contradicts the requirement of not managing or scaling infrastructure and introduces operational complexity.
MSK Serverless provides seamless integration with open-source Kafka APIs, enabling applications to produce and consume streaming data in real time while AWS handles scaling and availability. It is ideal for analytics pipelines where data ingestion is continuous and variable, and infrastructure overhead must be minimized.
By choosing MSK Serverless, the company can implement near real-time updates for its analytics platform using open-source Kafka without the operational burden of cluster management, fully aligning with AWS best practices for deployment and orchestration of ML workflows in a managed, scalable, and serverless manner.

NEW QUESTION # 218
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?

A. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
B. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
C. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
D. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

Answer: C

NEW QUESTION # 219
An ML engineer needs to merge and transform data from two sources to retrain an existing ML model. One data source consists of .csv files that are stored in an Amazon S3 bucket. Each .csv file consists of millions of records. The other data source is an Amazon Aurora DB cluster.
The result of the merge process must be written to a second S3 bucket. The ML engineer needs to perform this merge-and-transform task every week.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.
B. Create a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data.
C. Create an AWS Lambda function that runs Apache Spark code every week to merge and transform the data. Configure the Lambda function to connect to the initial S3 bucket and the DB cluster.
D. Create an AWS Batch job that runs Apache Spark code on Amazon EC2 instances every week.Configure the Spark code to save the data from the EC2 instances to the second S3 bucket.

Answer: B

NEW QUESTION # 220
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?

A. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.
B. Put each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.
C. Create AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.
D. Keep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.

Answer: A

Explanation:
Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier FlexibleRetrieval ensures cost- effective data retention and storage management while maintaining high performance for recent data retrieval.

NEW QUESTION # 221
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