Amazon MLS-C01 AWS ML Specialty Exam Questions Instant Access

Question 1

[Modeling]

A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer dat

a. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.

Which implementation will meet these requirements?

AUse encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

BUse SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.

CUse customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

DUse AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.

Answer : C

Question 2

[Data Engineering]

A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data.

The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the dat

a. The data scientists also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data.

Which solution will meet these requirements with the LEAST amount of compute resources?

AImport the data by using the None option.

BImport the data by using the Stratified option.

CImport the data by using the First K option. Infer the value of K from domain knowledge.

DImport the data by using the Randomized option. Infer the random size from domain knowledge.

Answer : C

Question 3

[Modeling]

A large consumer goods manufacturer has the following products on sale:

* 34 different toothpaste variants

* 48 different toothbrush variants

* 43 different mouthwash variants

The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.

Which solution should a machine learning specialist apply?

ATrain a custom ARIMA model to forecast demand for the new product.

BTrain an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.

CTrain an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.

DTrain a custom XGBoost model to forecast demand for the new product.

Answer : B

Question 4

[Exploratory Data Analysis]

A data engineer at a bank is evaluating a new tabular dataset that includes customer dat

a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.

Which steps should the data engineer take to address this issue? (Choose two.)

AUse a linear-based algorithm to train the model.

BApply principal component analysis (PCA).

CRemove a portion of highly correlated features from the dataset.

DApply min-max feature scaling to the dataset.

EApply one-hot encoding category-based variables.

Answer : B, C

Question 5

[Data Engineering]

A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional dat

a. The company also wants to perform transformations on the transactional data that is in Amazon S3.

The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.

Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)

AUse Amazon Athena to scan the data and identify the schema.

BUse AWS Glue crawlers to scan the data and identify the schema.

CUse Amazon Redshift to store procedures to perform data transformations

DUse AWS Glue workflows and AWS Glue jobs to perform data transformations.

EUse Amazon Redshift ML to train a model to detect fraud.

FUse Amazon Fraud Detector to train a model to detect fraud.

Answer : B, D, F

Question 6

[Modeling]

A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions.

Which solution will meet these requirements?

AAmazon S3 with S3 Cross-Region Replication (CRR)

BAmazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region

CAmazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability

DAWS Storage Gateway Volume Gateway

Answer : A

Question 7

[Machine Learning Implementation and Operations]

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 versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.

Which solution satisfies these requirements with MINIMAL effort?

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 : B

Amazon AWS Certified Machine Learning - Specialty MLS-C01 AWS ML Specialty Exam Questions