AWS MLA-C01 Free Dumps (Part 1, Q1-Q40) V9.02: Preview AWS Certified Machine Learning Engineer – Associate Updated Questions

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AWS MLA-C01 Free Dumps (Part 1, Q1-Q40): Preview 40 Free Sample Questions

You can preview exam questions by reading MLA-C01 free dumps. We will share 80 free demo questions in two parts, acting as a free demo of MLA-C01 dumps (V9.02). Today is the AWS MLA-C01 free dumps (Part 1, Q1-Q40), containing 40 free sample questions. They cover key AWS ML Engineer topics such as data preparation, feature engineering, SageMaker tools, model selection, evaluation, deployment, monitoring, and security. They help you review practical ML workflows across development, deployment, and maintenance, making them useful for assessing exam readiness.

1. A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.

A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.

Which solution will meet these requirements with the LEAST implementation effort?

Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.

Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.

Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.

Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.

2. A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.

Which solution will meet these requirements?

Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.

Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.

Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.

Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.

3. 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?

Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.

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.

Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.

Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

4. 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?

Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.

Create an Apache Spark job that uses a custom processing script on Amazon EM

Create a SageMaker processing job by calling the SageMaker Python SD

Create a data flow in SageMaker Data Wrangler. Configure a transform step.

5. 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?

Use AWS Glue to transform the categorical data into numerical data.

Use AWS Glue to transform the numerical data into categorical data.

Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

6. A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.

Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.

Which update to the network configuration will meet this requirement?

Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.

Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.

Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.

Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

7. HOTSPOT

A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML

engineer needs to prepare and store the data so that the company can use the data to train ML models.

Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)

• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.

• Store the resulting data back in Amazon S3.

• Use Amazon Athena to infer the schemas and available columns.

• Use AWS Glue crawlers to infer the schemas and available columns.

• Use AWS Glue DataBrew for data cleaning and feature engineering.

8. 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?

CSV files compressed with Snappy

JSON objects in JSONL format

JSON files compressed with gzip

Apache Parquet files

9. An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.

What should the ML engineer do to MINIMIZE the communication overhead between the instances?

Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.

Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.

Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.

Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.

10. A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.

An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.

Which solution will meet these requirements with the LEAST development effort?

Create code to evaluate each instance's memory and compute usage.

Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.

Check AWS CloudTrail event history for the creation of the resources.

Run AWS Compute Optimizer.

11. A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.

The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.

Which solution will meet these requirements with the LEAST operational effort?

Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.

Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.

Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.

Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.

12. An ML engineer needs to use an ML model to predict the price of apartments in a specific location.

Which metric should the ML engineer use to evaluate the model's performance?

Accuracy

Area Under the ROC Curve (AUC)

F1 score

Mean absolute error (MAE)

13. An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.

The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.

Which solution will improve the model's accuracy in the LEAST amount of time?

Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.

Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.

Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.

Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.

14. A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.

Which solution will meet these requirements?

Use Amazon Made to categorize the sensitive data.

Prepare the data by using AWS Glue DataBrew.

Run an AWS Batch job to change the sensitive data to random values.

Run an Amazon EMR job to change the sensitive data to random values.

15. A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.

An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.

Which solution will meet these requirements?

Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.

Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.

Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.

16. An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.

The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.

Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

The ML engineer and the Canvas user must be in separate SageMaker domains.

The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.

The model must be registered in the SageMaker Model Registry.

The ML engineer must host the model on AWS Marketplace.

The ML engineer must deploy the model to a SageMaker endpoint.

17. A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.

Which solution will meet these requirements?

Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

18. A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 ТВ of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.

An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.

Which solution will meet these requirements?

Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.

Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.

Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

19. An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.

Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

AWS::SageMaker::Model

AWS::SageMaker::Endpoint

AWS::SageMaker::NotebookInstance

AWS::SageMaker::Pipeline

20. 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.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

Use AWS Glue DataBrew built-in features to oversample the minority class.

Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

21. A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.

What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

Adjust the model's parameters and hyperparameters.

Initiate a manual Model Monitor job that uses the most recent production data.

Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

Include additional data in the existing training set for the model. Retrain and redeploy the model.

22. An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.

Which solution will meet these requirements with the LEAST operational overhead?

Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.

Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.

Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.

Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

23. An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region.

The ML engineer needs to copy the model to Account В in the same Region.

Which solution will meet this requirement with the LEAST development effort?

Use Amazon S3 to make a copy of the model. Transfer the copy to Account

Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account

Use AWS DataSync to replicate the model from Account A to Account

Create an AWS Site-to-Site VPN connection between Account A and Account В to transfer the model.

24. A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.

The company needs to centralize management of the team's permissions.

Which solution will meet this requirement?

Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.

Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.

Create a single IAM user. Attach the Administrator Access AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.

Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the Administrator Access AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.

25. A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.

Which solution will meet these requirements with the LEAST effort?

Use SageMaker built-in algorithms to train the proprietary datasets.

Use SageMaker script mode and premade images for ML frameworks.

Build a container on AWS that includes custom packages and a choice of ML frameworks.

Purchase similar production models through AWS Marketplace.

26. 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?

Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.

Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.

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.

Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.

27. Case Study

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company is experimenting with consecutive training jobs.

How can the company MINIMIZE infrastructure startup times for these jobs?

Use Managed Spot Training.

Use SageMaker managed warm pools.

Use SageMaker Training Compiler.

Use the SageMaker distributed data parallelism (SMDDP) library.

28. HOTSPOT

An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features.

The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:

• Feature splitting

• Logarithmic transformation

• One-hot encoding

• Standardized distribution

Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

29. 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?

Spot Instances

Reserved Instances

On-Demand Instances

Dedicated Instances

30. Case Study

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.

Which solution will meet this requirement?

Use SageMaker Experiments to facilitate the approval process during model registration.

Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.

Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.

Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

31. A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.

Which modeling approach should the company use to meet this requirement?

Anomaly detection

Linear regression

Logistic regression

Semantic segmentation

32. An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate.

During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.

What should the ML engineer do to improve the fraud detection for new transactions?

Increase the learning rate.

Remove some irrelevant features from the training dataset.

Increase the value of the max_depth hyperparameter.

Decrease the value of the max_depth hyperparameter.

33. 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?

LightGBM

Linear learner

К-means clustering

Neural Topic Model (NTM)

34. 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?

Use Amazon Athena to automatically detect the anomalies and to visualize the result.

Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

35. 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.

Which AWS service or feature can aggregate the data from the various data sources?

Amazon EMR Spark jobs

Amazon Kinesis Data Streams

Amazon DynamoDB

AWS Lake Formation

36. A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.

Which solution will meet these requirements?

Set up SageMaker Debugger and create a custom rule.

Set up blue/green deployments with all-at-once traffic shifting.

Set up blue/green deployments with canary traffic shifting.

Set up shadow testing with a shadow variant of the new model.

37. A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.

What should the company do to MINIMIZE false positives?

Set the value of the weight decay hyperparameter to zero.

Increase the number of training epochs.

Increase the value of the target_precision hyperparameter.

Change the value of the predictorjype hyperparameter to regressor.

38. 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?

Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

Create a model group for each category. Move the existing models into these category model groups.

Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.

Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

39. A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.

The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.

Which metric should the ML engineer use for the model recalibration?

Accuracy

Precision

Recall

Specificity

40. A company has a large collection of chat recordings from customer interactions after a product

release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.

Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

Use Amazon Rekognition to analyze sentiments of the chat conversations.

Train a Naive Bayes classifier to analyze sentiments of the chat conversations.

Use Amazon Comprehend to analyze sentiments of the chat conversations.

Use random forests to classify sentiments of the chat conversations.

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