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?

1. A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
2. 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.
3. 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.
4. D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

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

This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
• Two dense layers one output neuron
• 100 neurons in each layer
• 100 epochs
• Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

1. A. Early stopping
2. B. Random initialization of weights with appropriate seed
3. C. Increasing the number of epochs
4. D. Adding another layer with the 100 neurons

Correct Answer: D

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 needs to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

1. A. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
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 eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).
5. E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Correct Answer: BD

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.
Which solution should the agency consider?

1. A. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video strea
2. B. On each stream, use Amazon Rekognition Video and createa stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
3. C. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a uniqueAmazon Kinesis Video Streams video strea
4. D. On each stream, use Amazon Rekognition Image to detectfaces from a collection of known employees and alert when non-employees are detected.
5. E. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camer
6. F. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when nonemployees are detected.
7. G. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camer
8. H. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

Correct Answer: C

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.
The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.
Which solution for text extraction and entity detection will require the LEAST amount of effort?

1. A. Extract text from receipt images by using Amazon Textrac
2. B. Use the Amazon SageMaker BlazingTextalgorithm to train on the text for entities and custom entities.
3. C. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplac
4. D. Use the NER deep learning model to extract entities.
5. E. Extract text from receipt images by using Amazon Textrac
6. F. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
7. G. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplac
8. H. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

Correct Answer: C