Free AWS-Certified-Machine-Learning-Specialty Exam Braindumps

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?

1. A. Logistic regression
2. B. Linear regression
3. C. K-means
4. D. Principal component analysis (PCA)

Correct Answer: B

A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

1. A. Run self-correlation on all features and remove highly correlated features
2. B. Normalize all numerical values to be between 0 and 1
3. C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
4. D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Correct Answer: B

A manufacturer of car engines collects data from cars as they are being driven The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine maintenance The engine data is loaded into a data lake for training
Which is the MOST suitable predictive model that can be deployed into production'?

1. A. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
2. B. This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
3. C. Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
4. D. This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.

Correct Answer: B

A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
• Real-time analytics
• Interactive analytics of historical data
• Clickstream analytics
• Product recommendations
Which services should the Specialist use?

1. A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
2. B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
3. C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
4. D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Correct Answer: A

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?

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

Correct Answer: B