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[General] 100% Pass Quiz 2026 High Pass-Rate Amazon MLA-C01 Reliable Exam Review

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【General】 100% Pass Quiz 2026 High Pass-Rate Amazon MLA-C01 Reliable Exam Review

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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
  • 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 3
  • 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 4
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q26-Q31):NEW QUESTION # 26
An ML engineer is tuning an image classification model that shows poor performance on one of two available classes during prediction. Analysis reveals that the images whose class the model performed poorly on represent an extremely small fraction of the whole training dataset.
The ML engineer must improve the model's performance.
Which solution will meet this requirement?
  • A. Optimize for accuracy. Use image augmentation on the less common images to generate new samples.
  • B. Optimize for F1 score. Use Synthetic Minority Oversampling Technique (SMOTE) on the less common images to generate new samples.
  • C. Optimize for F1 score. Use image augmentation on the less common images to generate new samples.
  • D. Optimize for accuracy. Use Synthetic Minority Oversampling Technique (SMOTE) on the less common images to generate new samples.
Answer: C
Explanation:
This problem describes severe class imbalance in an image classification task, where the minority class has poor predictive performance. In such cases, accuracy is a misleading metric, because a model can achieve high accuracy by predicting only the majority class. AWS ML best practices recommend using F1 score, which balances precision and recall and is more appropriate for imbalanced classification problems.
To improve performance on the minority image class, image augmentation is the preferred approach.
Augmentation techniques-such as rotation, cropping, flipping, and brightness adjustment-create realistic new training examples while preserving semantic meaning. AWS documentation recommends augmentation for computer vision workloads to improve generalization without collecting new data.
SMOTE (Options C and D) is designed for tabular data, not image data, and generating synthetic pixel-level images using SMOTE is not appropriate or supported in typical computer vision pipelines.
Option A is incorrect because optimizing for accuracy does not address minority-class performance. Option D is incorrect because SMOTE is unsuitable for images.
Therefore, optimizing for F1 score and using image augmentation on the minority class is the correct solution.

NEW QUESTION # 27
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded.
The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.
  • B. Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.
  • C. Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket.
    Query the objects from the new S3 bucket.
  • D. Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.
Answer: B
Explanation:
Scenario:The ML engineer needs a low-overhead solution to query thousands of existing and new CSV objects stored in Amazon S3 based on a transaction date.
Why Athena?
* Serverless:Amazon Athena is a serverless query service that allows direct querying of data stored in S3 using standard SQL, reducing operational overhead.
* Ease of Use:By using the CTAS statement, the engineer can create a table with optimized partitions based on the transaction date. Partitioning improves query performance and minimizes costs by scanning only relevant data.
* Low Operational Overhead:No need to manage or provision additional infrastructure. Athena integrates seamlessly with S3, and CTAS simplifies table creation and optimization.
Steps to Implement:
* Organize Data in S3:Store CSV files in a bucket in a consistent format and directory structure if possible.
* Configure Athena:Use the AWS Management Console or Athena CLI to set up Athena to point to the S3 bucket.
* Run CTAS Statement:
CREATE TABLE processed_data
WITH (
format = 'PARQUET',
external_location = 's3://processed-bucket/',
partitioned_by = ARRAY['transaction_date']
) AS
SELECT *
FROM input_data;
This creates a new table with data partitioned by transaction date.
* Query the Data:Use standard SQL queries to fetch data based on the transaction date.
References:
* Amazon Athena CTAS Documentation
* Partitioning Data in Athena

NEW QUESTION # 28
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?
  • A. Dedicated Instances
  • B. Spot Instances
  • C. On-Demand Instances
  • D. Reserved Instances
Answer: B

NEW QUESTION # 29
A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns.
The company needs a no-code solution to transform the data. The solution must store the transformed data back to the same S3 bucket for model training.
Which solution will meet these requirements?
  • A. Configure an AWS Glue DataBrew project that connects to the data. Use the DataBrew interactive interface to create a recipe that performs the one-hot encoding transformation. Create a job to apply the transformation and write the output back to an S3 bucket.
  • B. Use an AWS Glue ETL interactive notebook to perform the transformation.
  • C. Use Amazon Redshift Spectrum to perform the transformation.
  • D. Use Amazon Athena SQL queries to perform the one-hot encoding transformation.
Answer: A
Explanation:
AWS Glue DataBrew is specifically designed to provide no-code and low-code data preparation for analytics and machine learning. It supports common file formats such as Apache Parquet and integrates directly with Amazon S3.
Using DataBrew, users can visually create recipes that apply transformations such as one-hot encoding without writing any code. Once the recipe is defined, a DataBrew job can be run to process the dataset and store the transformed output back into Amazon S3.
Options B, C, and D all require writing SQL or code, which violates the no-code requirement. AWS documentation clearly identifies DataBrew as the correct service for interactive, visual data transformation at scale.
Therefore, Option A is the correct solution.

NEW QUESTION # 30
A company is using Amazon SageMaker AI to build an ML model to predict customer behavior. The company needs to explain the bias in the model to an auditor. The explanation must focus on demographic data of the customers.
Which solution will meet these requirements?
  • A. Use AWS Glue DataBrew to create a job to detect drift in the model's data quality. Send the job output to the auditor.
  • B. Use Amazon QuickSight integration with SageMaker AI to generate a bias report. Send the report to the auditor.
  • C. Use SageMaker Clarify to generate a bias report. Send the report to the auditor.
  • D. Use Amazon CloudWatch metrics from the SageMaker AI namespace to create a bias dashboard. Share the dashboard with the auditor.
Answer: C
Explanation:
AWS documentation identifies Amazon SageMaker Clarify as the primary service for detecting, measuring, and explaining bias in ML models, particularly across demographic and sensitive attributes such as age, gender, and location. Clarify can analyze bias before training, after training, and during inference, making it suitable for audit and compliance requirements.
SageMaker Clarify generates bias reports using established fairness metrics such as difference in positive proportions, disparate impact, and conditional demographic disparity. These reports are exportable and auditor-friendly, directly meeting the requirement to explain bias to an external party.
AWS Glue DataBrew focuses on data preparation and quality, not bias detection. Amazon QuickSight does not provide ML fairness metrics. Amazon CloudWatch captures operational metrics, not demographic bias indicators.
AWS best practices explicitly recommend SageMaker Clarify for model transparency, fairness evaluation, and regulatory reporting.
Therefore, Option A is the correct and AWS-verified solution.

NEW QUESTION # 31
......
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