Amazon MLA-C01 Dumps

Exam Code MLA-C01
Exam Name AWS Certified Machine Learning Engineer - Associate
Update Date 07 May, 2026
Total Questions 241 Questions Answers With Explanation
$45

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Add Your Review About Amazon MLA-C01 Exam Dumps
35 Review for Amazon MLA-C01 Exam Dumps
James Smith

Model packaging with Docker and SageMaker containers was mentioned in the guide.

Raj Mehta

Model Monitor showed up in almost every section of my exam.

Rahul Venkatesh

Knowledge of Ground Truth labeling service was definitely needed in my exam.

Nitin Saxena

Glue and Data Wrangler were tested in one scenario for me.

Liam Harris

I just passed the MLA-C01 exam and the practice questions really helped me understand SageMaker workflows.

Kiran Joshi

I found the practice sets on reinforcement learning in SageMaker to be very insightful.

Siddharth Bansal

Bias detection tools came up in multiple exam questions.

Matthew Evans

There were several questions about integrating ML with streaming data services.

Neha Verma

Model deployment to endpoints and scaling options were tested directly in my exam.

Priya Patel

How deep should we go into algorithms like XGBoost and linear learner for this certification?

Chloe Phillips

Only the major algorithms like XGBoost and linear learner were tested for me.

Olivia Johnson

I really appreciated the coverage of monitoring ML models in production environments.

Hannah Reed

Batch inference was included in one of my scenario questions.

Pooja Menon

The practice tests covered model versioning and rollback scenarios nicely.

Ella Turner

The study material made hyperparameter tuning with SageMaker automatic tuning easy to understand.

Kabir Malhotra

Feature store usage and benefits were discussed thoroughly in the material.

Emma Clark

Yes, this cert definitely boosts job opportunities for ML engineers.

Ethan Allen

Data preprocessing and cleaning techniques were tested in multiple questions.

David Hall

Congrats! SageMaker Pipelines showed up in my test as well.

Mia Green

I liked how the course highlighted responsible AI practices and governance.

Meera Kulkarni

The course explained integration with APIs for deploying ML models really well.

Manish Agarwal

TensorFlow and PyTorch were only tested at a basic level for me.

Rekha Pillai

Monitoring with CloudWatch and SageMaker Model Monitor was clearly explained.

Varun Chawla

Glue knowledge was tested lightly, just high-level concepts.

Samuel Baker

Model security and governance practices were an important part of my exam.

Jacob Carter

I found a lot of questions on hyperparameter tuning, and the guide covered it thoroughly.

Isabella Hill

Can someone share if reinforcement learning is heavily tested in MLA-C01?

Sneha Iyer

I had a case study around SageMaker Debugger detecting training errors.

Vikram Chopra

SageMaker Debugger was tested for training error detection in one of my questions.

Daniel Wilson

Data labeling had about 2–3 questions in my exam.

Sophia Taylor

Model deployment strategies in SageMaker were well explained in the practice sets.

Rohit Reddy

Real-time inference latency optimization came up in one of my exam case studies.

Suresh Rao

Model explainability and bias detection came up in my exam, and I was prepared thanks to the guide.

Michael Brown

The guide explained cost optimization for training jobs very clearly.

Charlotte Young

The study material explained feature engineering concepts in a very simple and practical way.

Amit Sharma

Data preprocessing with AWS Glue was tested more than I expected.

George Mitchell

SageMaker training jobs and optimization techniques came up multiple times in my exam.

Divya Krishnan

How much detail is required about AWS Glue for preparing this exam?

Ishita Desai

Do we need to memorize all the built-in algorithms in SageMaker or just the major ones?

William Lewis

Data preparation with AWS Glue and SageMaker Data Wrangler was useful for the test.

Lakshmi Prasad

The practice sets included detailed explanations of model hosting and autoscaling, which helped a lot.

Alexander Wright

I appreciated the detailed coverage of batch transform jobs.

Henry Adams

A key career benefit of this exam is proving expertise in applied machine learning on AWS.

Grace Scott

SageMaker Pipelines and CI/CD for ML models were included in several practice questions.

Arjun Nair

The study questions around distributed training were very helpful.

Anjali Gupta

The practice material helped me understand evaluation metrics like precision and recall.

Question # 1

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

Question # 2

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? 

A. Download the file to a local workstation. Perform one-hot encoding by using a custom Python script. 
B. Create an Apache Spark job that uses a custom processing script on Amazon EMR. 
C. Create a SageMaker processing job by calling the SageMaker Python SDK. 
D. Create a data flow in SageMaker Data Wrangler. Configure a transform step. 

Question # 3

An ML engineer is analyzing a classification dataset before training a model in Amazon SageMaker AI. The ML engineer suspects that the dataset has a significant imbalance between class labels that could lead to biased model predictions. To confirm class imbalance, the ML engineer needs to select an appropriate pre-training bias metric. Which metric will meet this requirement?

A. Mean squared error (MSE) 
B. Difference in proportions of labels (DPL)
 C. Silhouette score 
D. Structural similarity index measure (SSIM)

Question # 4

An ML engineer has a custom container that performs k-fold cross-validation and logs an average F1 score during training. The ML engineer wants Amazon SageMaker AI Automatic Model Tuning (AMT) to select hyperparameters that maximize the average F1 score. How should the ML engineer integrate the custom metric into SageMaker AI AMT? 

A. Define the average F1 score in the TrainingInputMode parameter. 
B. Define a metric definition in the tuning job that uses a regular expression to capture the average F1 score from the training logs. 
C. Publish the average F1 score as a custom Amazon CloudWatch metric. 
D. Write the F1 score to a JSON file in Amazon S3 and reference it in ObjectiveMetricName. 

Question # 5

A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs from its own standalone Docker image. The company needs to perform a lift and shift from the on-premises Kubernetes cluster to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Which solution will meet this requirement with the LEAST operational overhead? 

A. Redesign the ML services to be configured in Kubeflow. Deploy the new Kubeflow managed ML services to the EKS cluster. 
B. Upload the Docker images to an Amazon Elastic Container Registry (Amazon ECR) repository. Configure a deployment pipeline to deploy the images to the EKS cluster. 
C. Migrate the training data to an Amazon Redshift cluster. Retrain the models from the migrated training data by using Amazon Redshift ML. Deploy the retrained models to the EKS cluster. 
D. Configure an Amazon SageMaker AI notebook. Retrain the models with the same code. Deploy the retrained models to the EKS cluster. 

Question # 6

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. 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. 
B. 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. 
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. 

Question # 7

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 ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model. Which algorithm should the ML engineer use to meet this requirement?

A. LightGBM  
B. Linear learner 
C. -means clustering 
D. Neural Topic Model (NTM) 

Question # 8

An ML engineer is configuring auto scaling for an inference component of a model that runs behind an Amazon SageMaker AI endpoint. The ML engineer configures SageMaker AI auto scaling with a target tracking scaling policy set to 100 invocations per model per minute. The SageMaker AI endpoint scales appropriately during normal business hours. However, the ML engineer notices that at the start of each business day, there are zero instances available to handle requests, which causes delays in processing. The ML engineer must ensure that the SageMaker AI endpoint can handle incoming requests at the start of each business day. Which solution will meet this requirement? 

A. Reduce the SageMaker AI auto scaling cooldown period to the minimum supported value. Add an auto scaling lifecycle hook to scale the SageMaker AI instances. 
B. Change the target metric to CPU utilization. 
C. Modify the scaling policy target value to one. 
D. Apply a step scaling policy that scales based on an Amazon CloudWatch alarm. Apply a second CloudWatch alarm and scaling policy to scale the minimum number of instances from zero to one at the start of each business day. 

Question # 9

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 scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts. 
B. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts. 
C. 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. 
D. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts. 

Question # 10

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? 

A. Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to control tree complexity for risk groups. 
B. Use the Amazon SageMaker k-means clustering algorithm. Set k to specify the number of clusters. 
C. Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to determine the number of training iterations for risk groups. 
D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set a contamination hyperparameter for risk anomaly detection. 

Question # 11

A company uses an Amazon EMR cluster to run a data ingestion process for an ML model. An ML engineer notices that the processing time is increasing. Which solution will reduce the processing time MOST cost-effectively? 

A. Use Spot Instances to increase the number of primary nodes. 
B. Use Spot Instances to increase the number of core nodes. 
C. Use Spot Instances to increase the number of task nodes. 
D. Use On-Demand Instances to increase the number of core nodes. 

Question # 12

An ML engineer wants to use Amazon SageMaker Data Wrangler to perform preprocessing on a dataset. The ML engineer wants to use the processed dataset to train a classification model. During preprocessing, the ML engineer notices that a text feature has a range of thousands of values that differ only by spelling errors. The ML engineer needs to apply an encoding method so that after preprocessing is complete, the text feature can be used to train the model. Which solution will meet these requirements? 

A. Perform ordinal encoding to represent categories of the feature. 
B. Perform similarity encoding to represent categories of the feature. 
C. Perform one-hot encoding to represent categories of the feature. 
D. Perform target encoding to represent categories of the feature. 

Question # 13

A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and training. An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC. What should the ML engineer do to meet the encryption requirement? 

A. Enable network isolation. 
B. Configure traffic encryption by using security groups. 
C. Enable inter-container traffic encryption. 
D. Enable VPC flow logs. 

Question # 14

An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data. Which file format will meet these requirements? 

A. CSV files compressed with Snappy 
B. JSON objects in JSONL format 
C. JSON files compressed with gzip 
D. Apache Parquet files 

Question # 15

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?

A. Use AWS Glue to transform the categorical data into numerical data. 
B. Use AWS Glue to transform the numerical data into categorical data. 
C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data. 
D. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data. 

Question # 16

A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry. The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings. Which solution will meet these requirements? 

A. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry. 
B. Create a model group for each category. Move the existing models into these category model groups. 
C. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models. 
D. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections. 

Question # 17

A company collects customer data daily and stores it as compressed files in an Amazon S3 bucket partitioned by date. Each month, analysts process the data, check data quality, and upload results to Amazon QuickSight dashboards. An ML engineer needs to automatically check data quality before the data is sent to QuickSight, with the LEAST operational overhead. Which solution will meet these requirements? 

A. Run an AWS Glue crawler monthly and use AWS Glue Data Quality rules to check data quality. 
B. Run an AWS Glue crawler and create a custom AWS Glue job with PySpark to evaluate data quality. 
C. Use AWS Lambda with Python scripts triggered by S3 uploads to evaluate data quality. 
D. Send S3 events to Amazon SQS and use Amazon CloudWatch Insights to evaluate data quality. 

Question # 18

A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine. The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data. Which solution will meet these requirements with the LEAST operational overhead? 

A. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data. 
B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data. 
C. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data. 
D. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data. 

Question # 19

A company is developing a customer support AI assistant by using an Amazon Bedrock Retrieval Augmented Generation (RAG) pipeline. The AI assistant retrieves articles from a knowledge base stored in Amazon S3. The company uses Amazon OpenSearch Service to index the knowledge base. The AI assistant uses an Amazon Bedrock Titan Embeddings model for vector search. The company wants to improve the relevance of the retrieved articles to improve the quality of the AI assistant's answers. Which solution will meet these requirements? 

A. Use auto-summarization on the retrieved articles by using Amazon SageMaker JumpStart. 
B. Use a reranker model before passing the articles to the foundation model (FM). 
C. Use Amazon Athena to pre-filter the articles based on metadata before retrieval. 
D. Use Amazon Bedrock Provisioned Throughput to process queries more efficiently. 

Question # 20

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. 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. 
B. 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. 
C. 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. 
D. 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.