AWS-Certified-Machine-Learning-Specialty Free Practice Test

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

A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

1. A. Increase the training data by adding variation in rotation for training images.
2. B. Increase the number of epochs for model training.
3. C. Increase the number of layers for the neural network.
4. D. Increase the dropout rate for the second-to-last layer.

Correct Answer: A

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS
Which approach should the Specialist use for training a model using that data?

1. A. Write a direct connection to the SQL database within the notebook and pull data in
2. B. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
3. C. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
4. D. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

Correct Answer: B

A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. Which solution will result in the MOST cost savings?

1. A. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance ha
2. B. Stop the notebook when it is not in us
3. C. Run both data preprocessing and feature engineering development on that instance.
4. D. Keep the notebook instance type and size the sam
5. E. Stop the notebook when it is not in us
6. F. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
7. G. Change the notebook instance type to a smaller general purpose instanc
8. H. Stop the notebook when it is not in us
9. I. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
10. J. Change the notebook instance type to a smaller general purpose instanc
11. K. Stop the notebook when it is not in us
12. L. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.

Correct Answer: B

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