NEW QUESTION 1
A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of 1,000 hand-labeled images covering 10 distinct items. The training results were poor.
Which machine learning approach fulfills the company’s long-term needs?

• A. Convert the images to grayscale and retrain the model
• B. Reduce the number of distinct items from 10 to 2, build the model, and iterate
• C. Attach different colored labels to each item, take the images again, and build the model
• D. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.

Answer: A

NEW QUESTION 2
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

• A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
• B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
• C. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
• D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Answer: D

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html

NEW QUESTION 3
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

• A. Decision tree
• B. Linear support vector machine (SVM)
• C. Naive Bayesian classifier
• D. Single Perceptron with sigmoidal activation function

Answer: C

NEW QUESTION 4
A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster.
Which nodes should the Specialist launch on Spot Instances?

• A. Master node
• B. Any of the core nodes
• C. Any of the task nodes
• D. Both core and task nodes

Answer: A

NEW QUESTION 5
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?

• A. Call the CreateNotebookInstanceLifecycleConfig API operation
• B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
• C. Stop and then restart the SageMaker notebook instance
• D. Call the UpdateNotebookInstanceLifecycleConfig API operation

Answer: C

NEW QUESTION 6
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)

• A. AWS Secrets Manager
• B. AWS CodeStar
• C. Amazon ECR
• D. Amazon ECS
• E. Amazon S3

Answer: CE

NEW QUESTION 7
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

• A. 1
• B. 4
• C. 7
• D. 10

Answer: C

NEW QUESTION 8
A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. Which solution will result in the MOST cost savings?

• A. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance ha
• B. Stop the notebook when it is not in us
• C. Run both data preprocessing and feature engineering development on that instance.
• D. Keep the notebook instance type and size the sam
• E. Stop the notebook when it is not in us
• F. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• G. Change the notebook instance type to a smaller general purpose instanc
• H. Stop the notebook when it is not in us
• I. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• J. Change the notebook instance type to a smaller general purpose instanc
• K. Stop the notebook when it is not in us
• L. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

Answer: B

NEW QUESTION 9
A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences.
Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time.
How can the company implement the testing model with the LEAST amount of operational overhead?

• A. Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview featur
• B. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version.
• C. Configure two SageMaker hosted endpoints that serve the different versions of the mode
• D. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string paramete
• E. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview featur
• F. When the new version of the model is ready for release, change the ALB's routing algorithm to weighted until all users have the updated version.
• G. Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview featur
• H. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version.
• I. Configure two SageMaker hosted endpoints that serve the different versions of the mode
• J. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the mode
• K. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other user
• L. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version.

Answer: D

NEW QUESTION 10
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )

• A. An AWS KMS key policy that allows access to the customer master key (CMK)
• B. A SageMaker notebook security group that allows access to Amazon S3
• C. An 1AM role that allows access to the specific S3 bucket
• D. A permissive S3 bucket policy
• E. An S3 bucket owner that matches the notebook owner
• F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

Answer: ACF

NEW QUESTION 11
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

• A. AWSDMS
• B. Amazon Kinesis Data Streams
• C. Amazon Kinesis Data Firehose
• D. Amazon Kinesis Data Analytics

Answer: C

NEW QUESTION 12
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

• A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth.Perform a manual review on those words before performing a business validation.
• B. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
• C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (AmazonA2I). Perform a manual review on those words before performing a business validation.
• D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images.Use this information to process the loan applications.

Answer: C

NEW QUESTION 13
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
• Profiles for all past and existing customers
• Profiles for all past and existing insured pets
• Policy-level information
• Premiums received
• Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• B. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segment
• D. Find similar profiles on social media
• E. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segment
• F. Find similar profiles on social media

Answer: C

NEW QUESTION 14
A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent
Why could this be an issue for the linear least squares regression model?

• A. It could cause the backpropagation algorithm to fail during training
• B. It could create a singular matrix during optimization which fails to define a unique solution
• C. It could modify the loss function during optimization causing it to fail during training
• D. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model

Answer: C

NEW QUESTION 15
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed
What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

• A. Create a SageMaker endpoint and configuration for the new model versio
• B. Redirect production traffic to the new endpoint by updating the client configuratio
• C. Revert traffic to the last version if the model does not perform as expected.
• D. Create a SageMaker endpoint and configuration for the new model versio
• E. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
• F. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new varian
• G. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
• H. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.

Answer: A

NEW QUESTION 16
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

• A. Autoregressive Integrated Moving Average (AIRMA)
• B. Exponential Smoothing (ETS)
• C. Convolutional Neural Network - Quantile Regression (CNN-QR)
• D. Prophet

Answer: B

NEW QUESTION 17
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