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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning | 24% | - Supervised & Tree-based Learning
|
| Topic 2: Modeling, Analysis, and Outcomes | 24% | - Data Analysis Techniques
|
| Topic 3: Specialized Applications of Data Science | 13% | - Specialized Domains
|
| Topic 4: Operations and Processes | 22% | - Business & Data Lifecycle
|
| Topic 5: Mathematics and Statistics | 17% | - Statistical Methods and Concepts
|
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NEW QUESTION # 63
A data scientist has constructed a model that meets the minimum performance requirements specified in the proposal for a prediction project. The data scientist thinks the model's accuracy should be improved, but the proposed deadline is approaching. Which of the following actions should the data scientist take first?
Answer: B
Explanation:
# The model already meets the performance goals outlined in the project proposal. However, since the deadline is near and the data scientist is considering further improvements, the correct approach is to:
# Consult the key project stakeholder. This ensures transparency and aligns actions with stakeholder priorities
- whether to proceed with deployment or invest in further model tuning.
Why the other options are incorrect:
* A: Collecting more data requires time and may exceed project scope.
* B: Requesting funding is premature and not justified if performance goals are already met.
* D: Testing new models takes time and may delay delivery - stakeholder input is needed first.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.1:"Stakeholder engagement is critical in project decision-making, especially when trade-offs exist between quality and timelines."
* CRISP-DM Framework - Evaluation Phase:"Before modifying models that meet objectives, it is essential to consult business stakeholders to align with their expectations."
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NEW QUESTION # 64
A data scientist is attempting to identify sentences that are conceptually similar to each other within a set of text files. Which of the following is the best way to prepare the data set to accomplish this task after data ingestion?
Answer: D
Explanation:
# Embeddings (e.g., word2vec, sentence transformers) are vector representations of text that capture semantic similarity. They allow comparison of conceptual meaning between sentences in a high-dimensional space, which is essential for tasks like semantic similarity or clustering.
Why the other options are incorrect:
* B: Extrapolation predicts values beyond a dataset's range - not relevant here.
* C: Sampling reduces data volume but doesn't aid in similarity analysis.
* D: One-hot encoding captures presence of words but lacks semantic understanding.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Embeddings transform text into numeric vectors, enabling similarity computation and semantic analysis."
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NEW QUESTION # 65
A data analyst wants to find the latitude and longitude of a mailing address. Which of the following is the best method to use?
Answer: D
Explanation:
# Geocoding is the process of converting addresses (like "1600 Amphitheatre Parkway, Mountain View, CA") into geographic coordinates (latitude and longitude), which is essential for spatial data analysis and mapping.
Why other options are incorrect:
* A: One-hot encoding is for converting categorical variables into binary vectors.
* B: Binning is for grouping continuous variables into categories.
* D: Imputing fills in missing data values, unrelated to geographic location retrieval.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Geocoding is a technique to convert textual location data into coordinate-based data for geographic analysis."
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NEW QUESTION # 66
A data scientist built several models that perform about the same but vary in the number of features. Which of the following models should the data scientist recommend for production according to Occam's razor?
Answer: A
Explanation:
According to Occam's razor, when models perform equivalently, you choose the simplest one - in this case, the model that achieves the needed performance with the fewest features.
NEW QUESTION # 67
A data scientist is performing a linear regression and wants to construct a model that explains the most variation in the dat a. Which of the following should the data scientist maximize when evaluating the regression performance metrics?
Answer: A
NEW QUESTION # 68
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