2026 Perfect MLA-C01: Interactive AWS Certified Machine Learning Engineer - Associate Course

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q21-Q26):

NEW QUESTION # 21
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?

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 # 22
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.
What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

Answer: D


NEW QUESTION # 23
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
The key requirements are real-time processing, high throughput, and minimal operational overhead. Amazon Kinesis Data Streams is designed for ingesting thousands of events per second with low latency.
For anomaly detection on streaming data, Amazon Managed Service for Apache Flink provides a built-in Random Cut Forest (RCF) function. RCF is an unsupervised anomaly detection algorithm that works well on numerical streaming data and does not require labeled training data.
This fully managed combination eliminates the need to deploy or maintain SageMaker endpoints, EC2 instances, or custom ML pipelines. Options B and C introduce unnecessary infrastructure and model management overhead. Option D is batch-oriented and unsuitable for real-time anomaly detection.
Therefore, using Kinesis Data Streams with Flink's built-in Random Cut Forest is the most scalable and low- overhead solution.


NEW QUESTION # 24
A healthcare analytics company wants to segment patients into groups that have similar risk factors to develop personalized treatment plans. The company has a dataset that includes patient health records, medication history, and lifestyle changes. The company must identify the appropriate algorithm to determine the number of groups by using hyperparameters.
Which solution will meet these requirements?

Answer: D

Explanation:
The problem described is a patient segmentation use case, which is a classic example of unsupervised learning. The objective is to group patients with similar characteristics without predefined labels. AWS documentation clearly states that Amazon SageMaker k-means is designed specifically for clustering and segmentation tasks.
The SageMaker k-means algorithm groups data points into clusters based on feature similarity and requires the user to define the number of clusters using the k hyperparameter. This directly satisfies the requirement to
"determine the number of groups by using hyperparameters." AWS recommends k-means for applications such as customer segmentation, risk grouping, and pattern discovery in healthcare data.
Option A (XGBoost) is a supervised learning algorithm used for classification and regression. The max_depth hyperparameter controls tree complexity, not the number of groups, making it unsuitable for this task.
Option C (DeepAR) is a time-series forecasting algorithm optimized for predicting future values, not clustering patients.
Option D (Random Cut Forest) is an anomaly detection algorithm. While useful for identifying outliers or unusual patient behavior, it does not perform clustering or group segmentation.
AWS SageMaker documentation explicitly identifies k-means as the correct choice when the goal is to partition data into a predefined number of clusters using a tunable hyperparameter.
Therefore, Option B is the correct and AWS-verified answer.


NEW QUESTION # 25
An ML engineer needs to choose the most appropriate data format for various data uses. Different teams will access the data for analytics, ML, and reporting purposes.
Select the correct data format from the following list to meet the requirements for each use case. Select each data format one time. (Select FOUR.)

Answer:

Explanation:

Explanation:

The best answers are Parquet, JSON, CSV, and ORC in that order.
Parquet is the strongest choice for complex analytical queries over large structured datasets because it is a columnar format. Columnar storage allows query engines such as Amazon Athena, AWS Glue, and Spark to read only the columns required by the query instead of scanning full rows. AWS documentation states that Apache Parquet and ORC are columnar storage formats optimized for fast retrieval in analytical applications, and that column-level compression can reduce storage space and I/O during query processing. This directly matches the need to filter, aggregate, reduce query response time, and lower storage/query cost.
JSON is correct for semi-structured real-time logs because JSON supports flexible and nested data structures.
AWS Glue documentation describes JSON as a format for data structures with consistent shape but flexible contents and notes that it is not row-based or column-based. That makes it appropriate for application logs, event records, and evolving schemas used later for analytics or ML ingestion.
CSV is correct for small spreadsheet exports and occasional human-readable analysis. AWS Glue describes CSV as a minimal, row-based data format. CSV is widely supported by spreadsheet tools and is easy for humans to inspect, but it is not ideal for large-scale analytical performance because it lacks efficient column pruning and rich schema support.
ORC is correct for the Apache Hive read-heavy big data pipeline. ORC is a performance-oriented, column- based format, and it is strongly associated with Hive-based analytics workloads. It provides high compression and efficient reads, making it well suited for structured data in read-heavy big data pipelines.


NEW QUESTION # 26
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