Improve: minor findings

Author: danieldanielecki

README.md

@@ -46,10 +46,10 @@
 
 ### A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant. Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?
 
-- [x] Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
+- [ ] Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
 - [x] Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
 - [ ] Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
-- [ ] Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data
+- [ ] Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.
 
 ### A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?
 
@@ -81,9 +81,9 @@
 
 ### A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist. Which machine learning model type should the Specialist use to accomplish this task?
 
-- [x] Linear regression.
+- [ ] Linear regression.
 - [x] Classification.
-- [x] Clustering.
+- [ ] Clustering.
 - [ ] Reinforcement learning.
 
 ### The displayed graph is from a forecasting model for testing a time series. Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?
@@ -404,9 +404,9 @@
 
 ![Question 54](images/question54.jpg)
 
-- [ ] Early stopping.
+- [x] Early stopping.
 - [ ] Random initialization of weights with appropriate seed.
-- [x] Increasing the number of epochs.
+- [ ] Increasing the number of epochs.
 - [ ] Adding another layer with the 100 neurons.
 
 ### A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices. Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
@@ -663,7 +663,7 @@
 
 ### 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? (Choose two.)
 
-![Question 91](images/question91.jpg)
+![Question 90](images/question90.jpg)
 
 - [ ] Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
 - [ ] Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
@@ -826,20 +826,6 @@ Data Firehose for clickstream analytics; AWS Glue to generate personalizedproduc
 for delivery to Amazon ES for clickstream analytics; Amazon EMR togenerate personalized product recommendations.
 - [ ] 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 productrecommendations.
 
-### A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist. Which machine learning model type should the Specialist use to accomplish this task?
-
-- [ ] Linear regression
-- [x] Classification
-- [ ] Clustering
-- [ ] Reinforcement learning
-
-### A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant. Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?
-
-- [ ] Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
-- [x] Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
-- [ ] Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
-- [ ] Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.
-
 ### A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data Which solution requires the LEAST effort to be able to query this data?
 
 - [ ] Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.