A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data
Which AWS service should the Data Scientist use?

1. A. Amazon Athena
2. B. Amazon Redshift Spectrum
3. C. AWS Glue
4. D. Amazon QuickSight

Correct Answer: A

A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

1. A. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
2. B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
3. C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
4. D. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
5. E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Correct Answer: DE

A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes
Which function will produce the desired output?

1. A. Dropout
2. B. Smooth L1 loss
3. C. Softmax
4. D. Rectified linear units (ReLU)

Correct Answer: D

A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis
What is the MOST efficient way to accomplish these tasks'?

1. A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3
2. B. Ingest the data into Apache Spark Streaming using Amazon EM
3. C. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake
4. D. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
5. E. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

Correct Answer: B

A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

1. A. The learning rate should be increased because the optimization process was trapped at a local minimum.
2. B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
3. C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
4. D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Correct Answer: D