DY0-001 · Question #40
SIMULATION 1 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 correct answer is D. The data set not being representative of the population. Option D is correct because mixing temperature scales (°F and °C) without standardization means the dataset does not accurately reflect the true temperature distribution across regions - a 50°C reading represents extreme heat (~122°F), yet it would be treated as equivalent to…
Question
SIMULATION 1 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. Answer:
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.
Exhibit
Options
- AThe data set consuming too many resources
- BThe data set having insufficient features
- CThe data set having insufficient row observations
- DThe data set not being representative of the population
How the community answered
(51 responses)- A6% (3)
- B4% (2)
- C12% (6)
- D78% (40)
Explanation
Option D is correct because mixing temperature scales (°F and °C) without standardization means the dataset does not accurately reflect the true temperature distribution across regions - a 50°C reading represents extreme heat (~122°F), yet it would be treated as equivalent to 50°F (cool weather) if left uncorrected, skewing any comparison and making the data misleading relative to the actual population of temperatures.
Why the distractors fail:
- A (too many resources): Resource consumption is a performance/infrastructure concern, not a data quality issue arising from mixed measurement scales.
- B (insufficient features): The dataset already has the relevant variables (temperature, location/zip code); the problem is how temperature is measured, not what is measured.
- C (insufficient row observations): The issue is with measurement consistency, not sample size - adding more rows in mixed scales would only worsen the problem.
Memory tip: Think "apples and oranges" - mixing °F and °C is like comparing two different fruits as if they're the same. When a dataset mixes scales or units, the measurements can no longer reliably represent the true population, which is exactly what "not representative" means on the exam.
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