Free Amazon MLS-C01 Exam Questions

Become Amazon Certified with updated MLS-C01 exam questions and correct answers

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Total 392 Questions | Updated On: Feb 19, 2026

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Question 1

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3

The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3

Which solution takes the LEAST effort to implement?

A : Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 toserialize data as Parquet

B : gest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.

C : Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use ApacheSpark to convert data into Parquet.

D : Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convertdata into Parquet.

Answer: B

Question 2

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

Which architecture changes would ensure that provisioned resources are being utilized effectively?

A : deploy the model as a batch transform job on an M5 instance.

B : deploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

C : deploy the model on a P3dn instance.

D : ploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: D

Question 3

A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package. During model training, the data scientist needs to evaluate model performance. Which metrics should the data scientist use to meet this requirement? (Select TWO.)

A : InferenceLatency

B : Mean squared error (MSE)

C : Root mean squared error (RMSE)

D : Precision

E : Accuracy

Answer: B,C

Question 4

The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand The team has a set of training data

Which machine learning algorithm should the researchers use that BEST meets their requirements?

A : tent Dirichlet Allocation (LDA)

B : current neural network (RNN)

C : means

D : nvolutional neural network (CNN)

Answer: C

Question 5

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.

Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

A : XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.

B : XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

C : XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.

D : XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Answer: C

Page: 1 / 79

Total 392 Questions | Updated On: Feb 19, 2026

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