MLA-C01 Exam Preparation & MLA-C01 Exam Questions & MLA-C01 Online Test

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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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.

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Amazon MLA-C01 Practice Tests, Practice Test MLA-C01 Pdf

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q144-Q149):

NEW QUESTION # 144
A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML engineer do to minimize bias due to missing values?

Answer: D

Explanation:
Use supervised learning to predict missing values based on the values of other features. Different supervised learning approaches might have different performances, but any properly implemented supervised learning approach should provide the same or better approximation than mean or median approximation, as proposed in responses A and C. Supervised learning applied to the imputation of missing values is an active field of research.


NEW QUESTION # 145
Hotspot Question
A company needs to train an ML model that will use historical transaction data to predict customer behavior.
Select the correct AWS service from the following list to perform each task on the data. Each service should be selected one time or not at all. (Select three.)
- Amazon Athena
- AWS Glue
- Amazon Kinesis Data Streams
- Amazon S3

Answer:

Explanation:


NEW QUESTION # 146
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?

Answer: B

Explanation:
The primary requirement in this scenario is achieving sub-second latency for a real-time analytics dashboard powered by Amazon OpenSearch Service. The current architecture uses Amazon Data Firehose, which buffers incoming records based on time or size before delivering them to the destination. A buffer interval of
60 seconds introduces unavoidable latency, making it unsuitable for near-real-time or sub-second use cases.
According to AWS documentation, reducing or eliminating buffering in Firehose is the correct approach when low-latency ingestion is required. Setting the Firehose buffer interval to zero seconds forces Firehose to deliver records as soon as they are received. Additionally, tuning the PutRecordBatch batch size allows efficient ingestion while minimizing delivery delay. This configuration is explicitly recommended for latency- sensitive analytics pipelines.
Option B is incorrect because AWS DataSync is designed for batch-oriented data transfers between storage systems, not real-time streaming. Enhanced fan-out consumers are a feature of Amazon Kinesis Data Streams, not DataSync, making this option invalid.
Option C directly contradicts the requirement. Increasing the buffer interval from 60 seconds to 120 seconds would further increase latency and degrade real-time performance.
Option D is also incorrect because Amazon SQS is a message queueing service, not a streaming ingestion service optimized for indexing data into OpenSearch with minimal latency. Using SQS would add additional processing layers and would not inherently provide sub-second ingestion into OpenSearch.
Therefore, using zero buffering in the Firehose stream and tuning the PutRecordBatch batch size is the only change that aligns with AWS best practices for achieving sub-second latency in real-time analytics pipelines.


NEW QUESTION # 147
A company has an ML model that is deployed to an Amazon SageMaker AI endpoint for real-time inference.
The company needs to deploy a new model. The company must compare the new model's performance to the currently deployed model's performance before shifting all traffic to the new model.
Which solution will meet these requirements with the LEAST operational effort?

Answer: B

Explanation:
AWS recommends shadow testing to evaluate a new model against a production model with minimal operational overhead. Using production variants on a single SageMaker endpoint allows traffic to be routed to multiple models without managing additional endpoints.
With a shadow variant, the new model receives a copy of live traffic but does not affect production responses.
Performance metrics such as latency, accuracy, and error rates can be compared directly against the current model using Amazon CloudWatch metrics. This approach is natively supported by Amazon SageMaker Endpoints.
Options A, B, and D introduce unnecessary complexity by requiring additional endpoints, traffic routing infrastructure, or custom code.
Therefore, deploying the new model as a shadow variant on the same endpoint is the most efficient solution.


NEW QUESTION # 148
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.
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?

Answer: B


NEW QUESTION # 149
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