A00-240

A00-240 practice questions, page 4 of 4, with answer review and explanations.

Question 16

An analyst has a sufficient volume of data to perform a 3-way partition of the data into training, validation, and test sets to perform honest assessment during the model building process. What is the purpose of the training data set?

  1. To provide an unbiased measure of assessment for the final model.
  2. To compare models and select and fine-tune the final model.
  3. To reduce total sample size to make computations more efficient.
  4. To build the predictive models.

Question 17

Refer to the confusion matrix: Calculate the sensitivity. (0 - negative outcome, 1 - positive outcome) Click the calculator button to display a calculator if needed.

  1. 25/48
  2. 58/102
  3. 25/B9
  4. 58/81

Question 18

The total modeling data has been split into training, validation, and test data. What is the best data to use for model assessment?

  1. Training data
  2. Total data
  3. Test data
  4. Validation data

Question 19

What is a drawback to performing data cleansing (imputation, transformations, etc.) on raw data prior to partitioning the data for honest assessment as opposed to performing the data cleansing after partitioning the data?

  1. It violates assumptions of the model.
  2. It requires extra computational effort and time.
  3. It omits the training (and test) data sets from the benefits of the cleansing methods.
  4. There is no ability to compare the effectiveness of different cleansing methods.

Question 20

A company has branch offices in eight regions. Customers within each region are classified as either "High Value" or "Medium Value" and are coded using the variable name VALUE. In the last year, the total amount of purchases per customer is used as the response variable. Suppose there is a significant interaction between REGION and VALUE. What can you conclude?

  1. More high value customers are found in some regions than others.
  2. The difference between average purchases for medium and high value customers depends on the region.
  3. Regions with higher average purchases have more high value customers.
  4. Regions with higher average purchases have more medium value customers.