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MLS-C01: AWS Certified Machine Learning - Specialty Dumps

Free Amazon MLS-C01 Exam Dumps August 2026

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Question No: 1

MultipleChoice

[Modeling]

A machine learning specialist works for a fruit processing company and needs to build a system that

categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.

The company requires at least 85% accuracy to make use of the model.

After an exhaustive grid search, the optimal hyperparameters produced the following:

68% accuracy on the training set

67% accuracy on the validation set

What can the machine learning specialist do to improve the system's accuracy?

Options

AUpload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.

BAdd more data to the training set and retrain the model using transfer learning to reduce the bias.

CUse a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

DTrain a new model using the current neural network architecture.

Answer
B

Question No: 2

MultipleChoice

[Modeling]

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.

The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.

Which solution for text extraction and entity detection will require the LEAST amount of effort?

Options

AExtract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.

BExtract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.

CExtract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

DExtract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

Answer
C

Question No: 3

MultipleChoice

[Modeling]

A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased.

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

Options

ATrain a model by using a user-based collaborative filtering algorithm on Amazon SageMaker. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.

BUse an Amazon Personalize PERSONALIZED_RANKING recipe to train a model. Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetPersonalizedRanking API operation to get the real-time recommendations.

CUse an Amazon Personalize USER_ PERSONAL IZATION recipe to train a model Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetRecommendations API operation to get the real-time recommendations.

DTrain a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.

Answer
C

Question No: 4

MultipleChoice

[Machine Learning Implementation and Operations]

A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network. However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet.

Which set of actions should the data science team take to fix the issue?

Options

AModify the notebook instances' security group to allow traffic only from the CIDR ranges of the VPC. Apply this security group to all of the notebook instances' VPC interfaces.

BCreate an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Apply this policy to all IAM users, groups, and roles used to access the notebook instances.

CAdd a NAT gateway to the VPC. Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses.

DChange the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC.

Answer
A

Question No: 5

MultipleChoice

[Modeling]

A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials.

...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.

...should the company retrain the model to meet these requirements?

Options

ASet the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

BSet the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.

CUse 90% of the historical data for training Set the number of epochs to 20.

DSet the normalize_jabel hyperparameter to true. Set the number of classes to 2.

Answer
A

Question No: 6

MultipleChoice

[Data Engineering]

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:

March 1st, 2020, 08:14pm -

There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.

Which solution requires the LEAST operational overhead to create a new dataset with the added features?

Options

ACreate an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

BCreate a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

CCreate a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.

DCreate an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

Answer
C

Question No: 7

MultipleChoice

[Data Engineering]

A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to. pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.

How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?

Options

AUse an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.

BUse Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

CUse Amazon Kinesis Data Streams to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.

DUse an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

Answer
C

Question No: 8

MultipleChoice

An agriculture company wants to improve crop yield forecasting for the upcoming season by using crop yields from the last three seasons. The company wants to compare the performance of its new scikit-learn model to the benchmark.

A data scientist needs to package the code into a container that computes both the new model forecast and the benchmark.

The data scientist wants AWS to be responsible for the operational maintenance of the container.

Which solution will meet these requirements?

Options

APackage the code as the training script for an Amazon SageMaker scikit-learn container.

BPackage the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR).

CPackage the code into a custom-built container. Push the container to AWS Fargate.

DPackage the code by extending an Amazon SageMaker scikit-learn container.

Answer
DExplanation

To compare a custom scikit-learn model with a benchmark model in a managed environment, the most effective and maintainable solution is to extend an existing SageMaker scikit-learn container.

''If you are using a framework like scikit-learn and need custom logic, you can extend the prebuilt SageMaker containers to include your own inference or training scripts. AWS manages the container base and you only maintain your code.''

Question No: 9

MultipleChoice

A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.

How can the data scientist meet this requirements?

Options

ACall the CreateNotebookInstanceLifecycleConfig API operation

BCreate a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance

CStop and then restart the SageMaker notebook instance

DCall the UpdateNotebookInstanceLifecycleConfig API operation

Answer
C

Question No: 10

MultipleChoice

A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep

tracking application on AWS. The application collects device usage information from device users. The

company's Data Science team is building a machine learning model to predict if and when a user will stop

utilizing the company's devices. Predictions from this model are used by a downstream application that

determines the best approach for contacting users.

The Data Science team is building multiple versions of the machine learning model to evaluate each version

against the company's business goals. To measure long-term effectiveness, the team wants to run multiple

served by the models.

Which solution satisfies these requirements with MINIMAL effort?

Options

ABuild and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one
for each model. Programmatically control invoking different models for inference at the application layer.

BBuild and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint
configuration with multiple production variants. Programmatically control the portion of the inferences
served by the multiple models by updating the endpoint configuration.

CBuild and host multiple models in Amazon SageMaker Neo to take into account different types of medical
devices. Programmatically control which model is invoked for inference based on the medical device type.

DBuild and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple
models. Use Amazon SageMaker batch transform to control invoking the different models through the
single endpoint.

Answer
BExplanation

A/B testing with Amazon SageMaker is required in the Exam. In A/B testing, you test different variants of your models and compare how each variant performs. Amazon SageMaker enables you to test multiple models or model versions behind the `same endpoint` using `production variants`. Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model. To test multiple models by `distributing traffic` between them, specify the `percentage of the traffic` that gets routed to each model by specifying the `weight` for each `production variant` in the endpoint configuration.

https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html#model-testing-target-variant

Question No: 11

MultipleChoice

A Data Engineer needs to build a model using a dataset containing customer credit card information.

How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker

instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Options

BUse an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically
discard credit card numbers and insert fake credit card numbers.

CUse an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker
instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length
of the credit card numbers.

DUse AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Answer
D

Question No: 12

MultipleChoice

An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

Options

AThe model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1

BThe model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1

CThe model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model

DThe model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Answer
C

Question No: 13

MultipleChoice

A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents How should a Machine Learning Specialist address this issue for future documents'?

Options

AConvert current documents to SSML with pronunciation tags

BCreate an appropriate pronunciation lexicon.

COutput speech marks to guide in pronunciation

DUse Amazon Lex to preprocess the text files for pronunciation

Answer
A

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