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Download AWS Certified Machine Learning - Specialty Exam Dumps

NEW QUESTION 30
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 accuracy?

A. Support vector machine (SVM) with non-linear kernelB. Single perceptron with tanh activation functionC. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))D. Logistic regression

Answer: A

 

NEW QUESTION 31
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

A. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.B. Bundle the NVIDIA drivers with the Docker image.C. Organize the Docker container's file structure to execute on GPU instances.D. Build the Docker container to be NVIDIA-Docker compatible.

Answer: D

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf
If you plan to use GPU devices, make sure that your containers are nvidia-docker compatible.
Only the CUDA toolkit should be included on containers. Don't bundle NVIDIA drivers with the image.
For more information about nvidia-docker, see NVIDIA/nvidia-docker.

 

NEW QUESTION 32
A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.

How should the data scientist transform the data?

A. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.B. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine.C. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora.D. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3.

Answer: C

 

NEW QUESTION 33
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Select TWO.)

A. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.B. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.C. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.D. Customize the built-in image classification algorithm to use Inception and use this for model training.E. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

Answer: B,C

 

NEW QUESTION 34
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 a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social mediaB. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social mediaC. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.D. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Answer: D

 

NEW QUESTION 35
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