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Free CompTIA DY0-001 Exam Braindumps (page: 2)

Page 2 of 18
PDF with 85 Questions

SIMULATION

A client has gathered weather data on which regions have high temperatures. The client would like a visualization to gain a better understanding of the data.

INSTRUCTIONS

Part 1

Review the charts provided and use the drop-down menu to select the most appropriate way to standardize the data.

Part 2

Answer the questions to determine how to create one data set.

Part 3

Select the most appropriate visualization based on the data set that represents what the client is looking for.

If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.

































  1. See Explanation for the Answer.

Answer(s): A

Explanation:

Part 1

Select Table 2. Table 2 contains mixed temperature scales (°F and °C) that must be standardized before visualization.

Variable: Temperature/scale

Action: Correct

Value to correct: 50 °C



Part 2

Method: Data matching

Join variable: Zip code

You need to merge the two tables by aligning matching records, which is a data-matching (join) operation, and ZIP code is the shared, uniquely identifying field linking each region's weather reading to its city.



Part 3

Choose the choropleth map (the first option).

A choropleth map best shows geographic variation in temperature by coloring each state (or region) according to its recorded value. This lets the client immediately see where the highest and lowest temperatures occur across the U.S. without distracting elements like bubble size or combined chart axes.



A data scientist is using the following confusion matrix to assess model performance:



The model is predicting whether a delivery truck will be able to make 200 scheduled delivery stops. Every time the model is correct, the company saves an hour in planning and scheduling of maintenance work. Every time the model is wrong, the company loses four hours of delivery time for the truck.
Which of the following is the net model impact for the company?

  1. 25 hours lost
  2. 25 hours saved
  3. 165 hours lost
  4. 165 hours saved

Answer(s): A

Explanation:

Treat each "predicted-to-fail" and "predicted-to-succeed" row as coming from 100 cases apiece (200 total).

Predicted-fail & actually-fail: 80 saves 80 hr

Predicted-succeed & actually-succeed: 85 saves 85 hr

Total saved = 80 + 85 = 165 hr



A team is building a spam detection system. The team wants a probability-based identification method without complex, in-depth training from the historical data set.
Which of the following methods would best serve this purpose?

  1. Logistic regression
  2. Random forest
  3. Naive Baves
  4. Linear regression

Answer(s): C

Explanation:

Naive Bayes directly computes class probabilities using simple frequency counts under the independence assumption, requiring minimal training complexity and no iterative optimization-- ideal for fast, probability-based spam detection.



A model's results show increasing explanatory value as additional independent variables are added to the model.
Which of the following is the most appropriate statistic?

  1. Adjusted R2
  2. p value
  3. x2
  4. R2

Answer(s): A

Explanation:

Adjusted R² accounts for the number of predictors in the model, only increasing when a new independent variable adds genuine explanatory power beyond what random chance would predict. In contrast, plain R² will always rise (or stay the same) as you add more variables, regardless of their true relevance.



A data scientist is standardizing a large data set that contains website addresses. A specific string inside some of the web addresses needs to be extracted.
Which of the following is the best method for extracting the desired string from the text data?

  1. Regular expressions
  2. Named-entity recognition
  3. Large language model
  4. Find and replace

Answer(s): A



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