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  1. Home
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  3. DY0-001

CompTIA DY0-001 Exam (page: 1)
CompTIA DataX
Updated on: 12-Feb-2026

Viewing Page 1 of 12
DY0-001 PDF 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.



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.

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

Linear regression.

Of the four models, linear regression has the highest R² (0.8), indicating it explains the greatest proportion of variance in sales.



Part 2

Var 4 ­ Net operations cost.

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 (p0.90) nor initial investment (p0.23) reach significance.



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?

  1. A logistic regression
  2. An exponential regression
  3. A linear regression
  4. A probit regression

Answer(s): C



A data scientist wants to evaluate the performance of various nonlinear models.
Which of the following is best suited for this task?

  1. AIC
  2. Chi-squared test
  3. MCC
  4. ANOVA

Answer(s): A



Which of the following is the layer that is responsible for the depth in deep learning?

  1. Convolution
  2. Dropout
  3. Pooling
  4. Hidden

Answer(s): D



Which of the following modeling tools is appropriate for solving a scheduling problem?

  1. One-armed bandit
  2. Constrained optimization
  3. Decision tree
  4. Gradient descent

Answer(s): B

Explanation:

Scheduling problems require finding the best allocation of resources subject to constraints (e.g., time slots, resource availability), which is precisely what constrained optimization algorithms are designed to handle.



Which of the following environmental changes is most likely to resolve a memory constraint error when running a complex model using distributed computing?

  1. Converting an on-premises deployment to a containerized deployment
  2. Migrating to a cloud deployment
  3. Moving model processing to an edge deployment
  4. Adding nodes to a cluster deployment

Answer(s): D

Explanation:

Increasing the number of nodes in your cluster directly expands the total available memory across the distributed system, alleviating memory-constraint errors without changing your code or deployment paradigm. Containerization or edge deployments don't inherently provide more memory, and migrating to the cloud alone doesn't guarantee additional nodes unless you explicitly scale out.



A data analyst wants to save a newly analyzed data set to a local storage option. The data set must meet the following requirements:

Be minimal in size

Have the ability to be ingested quickly

Have the associated schema, including data types, stored with it

Which of the following file types is the best to use?

  1. JSON
  2. Parquet
  3. XML
  4. CSV

Answer(s): B

Explanation:

Parquet is a columnar storage format that automatically includes schema (data types), uses efficient compression to minimize file size, and enables very fast reads for analytic workloads.



Viewing Page 1 of 12



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