CompTIA DataAI v1 Exam (DY0-001) - CompTIA

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Question #1 Verified Q&A
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
Explanation:
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below.<br />
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Explanation:<br />
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Explanation: Part 1<br />
Select Table 2. Table 2 contains mixed temperature scales (°F and °C) that must be standardized<br />
before visualization. Variable: Temperature/scale Action: Correct Value to correct: 50 °C<br />
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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.<br />
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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.<br />
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Question #2 Verified Q&A
SIMULATION A data scientist needs to determine whether product sales are impacted by other contributing factors. The client has provided the data scientist with sales and other variables in the data set. The data scientist decides to test potential models that include other information. INSTRUCTIONS Part 1 Use the information provided in the table to select the appropriate regression model. Part 2 Review the summary output and variable table to determine which variable is statistically significant.<br />
If at any time you would like to bring back the initial state of the simulation, please click the Reset All<br />
button. Part 1 Part 2 24/90<br />
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Time Given the R2 values, which of the following regression models best fits the relat variables? Quantile regression Linear regression Ridge regression R2 0.5 R2 0.6 R2 0.8<br />
Var 1 Sales (in millions) Var 2 ROI % of overall)<br />
1 2 3 5 9<br />
3.118026935 6%<br />
4.823728572 11%<br />
4<br />
7.149131157 18%<br />
2.173859679 5%<br />
3.519662597 9%<br />
5.98246748 12%<br />
8.495414141 14%<br />
3.678906129 7%<br />
3.539605808 6%<br />
6<br />
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Ridge regression R2 0.5 2 6 8 10 log(Lambda)<br />
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Quantile regression R2 0,6 0.16 0.12 0.08<br />
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Linear regression R? 0.8 8 70 - 8 110 130 150 170<br />
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Lasso regression R? 0.62<br />
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-1<br />
2 3<br />
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Part 1 Part 2<br />
Var 1 Sales<br />
Var 2 ROI Var 3 Var<br />
Time (in millions) (% of overal Inventory cos oper<br />
16% 58<br />
1 326.311584 2 507.9584031<br />
8% 57<br />
3 232.5685962 4 117.3342091<br />
7% 50<br />
7% 60<br />
5 242.866515<br />
14% 50<br />
6 359.6300247<br />
19% 56<br />
7 119.384542<br />
5% 53<br />
5% 56<br />
8 372.064584<br />
18% 51<br />
9 320.0212452 Which of the following additional variable include in the new model?<br />
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• Var 5 Initial investment O Var 4 Net opi<br />
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• Var 3 Inventory cost • None of the :<br />
View summary output<br />
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Explanation:
below.<br />
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Explanation: Part 1 Linear regression. Of the four models, linear regression has the highest R² (0.8), indicating it explains the greatest proportion of variance in sales.<br />
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Part 2<br />
Var 4 – Net operations cost.<br />
Net operations cost has a p-value of essentially 0 (far below 0.05), indicating it is the only additional predictor statistically significant in explaining sales. Neither inventory cost (p≈0.90) nor initial investment (p≈0.23) reach significance.<br />
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Question #3 Verified Q&A
A data scientist is building an inferential model with a single predictor variable. A scatter plot of the independent variable against the real-number dependent variable shows a strong relationship between them. The predictor variable is normally distributed with very few outliers. Which of the following algorithms is the best fit for this model, given the data scientist wants the model to be easily interpreted?
A A logistic regression
B A linear regression
C An exponential regression
D A probit regression
Correct Answer: A linear regression
Question #4 Verified Q&A
A data scientist wants to evaluate the performance of various nonlinear models. Which of the following is best suited for this task?
A AIC
B Chi-squared test
C MCC
D ANOVA
Correct Answer: AIC
Question #5 Verified Q&A
Which of the following is the layer that is responsible for the depth in deep learning?
A Convolution
B Dropout
C Pooling
D Hidden
Correct Answer: Hidden

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