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

A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available
Which model is MOST likely to provide the best results in Amazon SageMaker?

1. A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
2. B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
3. C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of regressor.
4. D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full yearof data with a predictor_type of classifier.

Correct Answer: C

A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

1. A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
2. B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims ineach category to expect from month to month.
3. C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect frommonth to month.
4. D. Classification with supervised learning of the categories for which partial information on claim contents isprovided, and forecasting using claim IDs and timestamps for all other categories.

Correct Answer: C

A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

1. A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth.Perform a manual review on those words before performing a business validation.
2. B. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
3. C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (AmazonA2I). Perform a manual review on those words before performing a business validation.
4. D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images.Use this information to process the loan applications.

Correct Answer: C

A large consumer goods manufacturer has the following products on sale
• 34 different toothpaste variants
• 48 different toothbrush variants
• 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched
Which solution should a Machine Learning Specialist apply?

1. A. Train a custom ARIMA model to forecast demand for the new product.
2. B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
3. C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
4. D. Train a custom XGBoost model to forecast demand for the new product

Correct Answer: B
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

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