Hey Folks, In this article we are going to see AWS Academy Machine Learning Module 4 Answers. This Module Provides knowledge check answers for 4th module 4. if you need previous quiz answers i will share the link below.
Also Read:Â AWS Academy Machine Learning Module 3 Answers
AWS Academy Machine Learning Module 4Â
Q1. Which Patterns are common in the time series data ?
A. Trends
B. Seasonal
C. Exponential
D. Star Shaped
E. None of the above
Answer: [ A ], [ B ] Trends, Seasonal.
Q2. Which use cases are apply to forecasting ?Â
A. Predicting the inventory that's required for items in a warehouseÂ
B. Predicting if an X- ray image contains an abnormalityÂ
C. Predicting the energy consumption of an officeÂ
D. Predicting the Sentiment of a review
E. Determining if two images are of the same personÂ
Answer: [ A ] , [ C ]Â Â
Q3. Which datasets could be used as a time series data set? (Answer Two)
A. Sales data that contains items, purchase dates and quantities
B. Web logs that contain IP addresses, pages and timestamps
C. Chemical Composition of food additives
D. Membership data that contains personally identifiable information ( PII ) and a donate flag.Â
E. Results from a one-time survey.Â
Answer: [ A ] [ B ]Â
        Sales data that contains items, purchase dates and quantities
        Web logs that contain IP addresses, pages and timestamps
Q4. You have a dataset of temperature readings from a weather station. Temperature readings are logged for every 5 minutes. You notice that there are several missing values each day. Which approach do you follow.Â
A. Replace the missing values with zeroÂ
B. Forward fill the missing values.Â
C. Backward fill the missing values.Â
D. Use the sum of the temperatures for the day to fill the missing values.Â
E. Remove the records that have missing data.Â
Answer: [ B ] , [ C ]Â
Q5. Which scenarios are the examples of appropriate down sampling?Â
A. Using mean to convert temperature readings every minute to an hourly value.Â
B. Using mean to convert sales information during the day to a daily total.Â
C. Using sum to convert sales order information during the day to daily total.Â
D. Using the sum to convert temperature readings every minute to tan hourly value.Â
Answer: [ A ] [ C ]Â
Q6. what examples of seasonality that might you observe in the time series data ?Â
A. Quarterly, yearly.
B. Spring, summer, fall, winter
C. Every two years
D. One time sales events
E. Hourly.
Answer: [ A ] [ B ]Â
Q7. Amazon forecast generates production for P10, P50 and P90. If the forecast predicts the shoe sales, what do the P10, P50 and P90 tell you.Â
A. P10 indicates 10% of the time, fewer than the predicted value will be ordered.Â
B. P50 indicates 50% of the time, the exact number of the predicted value will be ordered.Â
C. P90 indicates 90% of the time, more than the predicted value will be ordered.
D. All the above.Â
Answer: [ A ]Â P10 indicates 10% of the time, fewer than the predicted value will be ordered.Â
Q8. Which datasets are required for generating a retail forecast with Amazon Forecast?Â
A. Item data that includes an item and category.Â
B. Item stock information that includes a time stamp, item, and stock quantity.Â
C. Item pricing data including a timestamp item and priceÂ
D. Timeseries that include a timestamp , item and quantityÂ
Answer: [ D ]Â
Q9. You are going to use a dataset, to generate a forecast. Which steps would you take to use the data that's available to produce the best model?Â
A. Use the train_test_split_function from scikit-learn to create a training and testing dataset.Â
B. Use pandas to split the data by time into a training and testing dataset.Â
C. Use the training data set in Amazon forecast by specifying a backset window.Â
D. Using the testing dataset to compare the predicted values with actual values.Â
E. All the Above
Answer: [ B ] [ C ] [ D ]Â
In the next module, we are going to see the module 5 answers.Â
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