Instant Access Amazon.AWS-Certified-Machine-Learning-Specialty.v2024-06-17.q246 Actual Practice Test Engine for Free (Page 19)

Question 86

Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

A.Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
B.Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
C.Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
D.Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

Question 87

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

A.Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
B.Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
C.Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
D.Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

Question 88

Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

A.Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
B.Use Amazon Translate with the transcribed files to train and build a model for the key topics
C.Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
D.Use Amazon Comprehend with the transcribed files to build the key topics

Question 89

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 data. 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?

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

Correct Answer: C

Explanation
Amazon SageMaker supports encryption at rest for the ML storage volumes, the model artifacts, and the data in Amazon S3 using AWS Key Management Service (AWS KMS). AWS KMS is a service that allows customers to create and manage encryption keys that can be used to encrypt data. AWS KMS also provides an audit trail of key usage by logging key events to AWS CloudTrail. Customers can use either AWS managed keys or customer managed keys to encrypt their data. AWS managed keys are created and managed by AWS on behalf of the customer, while customer managed keys are created and managed by the customer. Customer managed keys offer more control and flexibility over the key policies, permissions, and rotation. Therefore, to meet the requirements of the company, the best option is to use customer managed keys in AWS KMS to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.
The other options are not correct because:
Option A: AWS Cloud HSM is a service that provides hardware security modules (HSMs) to store and use encryption keys. AWS Cloud HSM is not integrated with Amazon SageMaker, and cannot be used to encrypt the ML data volumes, the model artifacts, or the data in Amazon S3. AWS Cloud HSM is more suitable for customers who need to meet strict compliance requirements or who need direct control over the HSMs.
Option B: SageMaker built-in transient keys are temporary keys that are used to encrypt the ML data volumes and are discarded immediately after encryption. These keys do not provide persistent encryption or logging of key usage. Enabling default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes does not affect the ML data volumes, which are encrypted separately by SageMaker. Moreover, this option does not address the encryption of the model artifacts and data in Amazon S3.
Option D: AWS Security Token Service (AWS STS) is a service that provides temporary credentials to access AWS resources. AWS STS does not provide encryption keys or encryption services. AWS STS cannot be used to encrypt the ML storage volumes, the model artifacts, or the data in Amazon S3.
References:
Protect Data at Rest Using Encryption - Amazon SageMaker
What is AWS Key Management Service? - AWS Key Management Service
What is AWS CloudHSM? - AWS CloudHSM
What is AWS Security Token Service? - AWS Security Token Service

Question 90

A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?

A.A scatter plot showing the performance of the objective metric over each training iteration.
B.A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.
C.A scatter plot showing the correlation between maximum tree depth and the objective metric.
D.A histogram showing whether the most important input feature is Gaussian.

Question 86

Question 87

Question 88

Question 89

Question 90

Download PDF File