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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Specialized Applications of Data Science | 13% | - Computer Vision - Natural Language Processing (NLP) - Anomaly detection and signal processing - Time-series analysis - Industry-specific analytics use cases |
| Topic 2: Modeling, Analysis, and Outcomes | 24% | - Predictive and prescriptive analytics - Result interpretation and business communication - Model selection and evaluation metrics - Feature engineering and selection - Data preparation and exploratory data analysis |
| Topic 3: Machine Learning | 24% | - Supervised, unsupervised, and reinforcement learning - Ethics and bias in machine learning - Deep learning fundamentals - Algorithm selection and implementation - Hyperparameter tuning and optimization |
| Topic 4: Mathematics and Statistics | 17% | - Probability theory and distributions - Calculus and optimization concepts - Bayesian reasoning and modeling - Linear algebra fundamentals - Statistical inference and hypothesis testing |
| Topic 5: Operations and Processes | 22% | - Security and compliance in data operations - Data governance and quality management - Version control and reproducibility - Data pipeline design and maintenance - Model deployment and monitoring |
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NEW QUESTION # 32
Which of the following belong in a presentation to the senior management team and/or C-suite executives? (Choose two.)
Answer: B
Explanation:
Senior leaders need actionable insights and the overarching outcomes, not the implementation details, so you present your key recommendations alongside a summary of results at a high level.
NEW QUESTION # 33
A data scientist has built an image recognition model that distinguishes cars from trucks. The data scientist now wants to measure the rate at which the model correctly identifies a car as a car versus when it misidentifies a truck as a car. Which of the following would best convey this information?
Answer: D
Explanation:
A confusion matrix directly shows true positives (cars correctly identified) and false positives (trucks misidentified as cars), giving you exactly the rates you're interested in.
NEW QUESTION # 34
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 # 35
Which of the following best describes the minimization of the residual term in a LASSO linear regression?
Answer: D
Explanation:
# LASSO (Least Absolute Shrinkage and Selection Operator) regression minimizes the squared residuals (e²), just like OLS, but adds an L1 penalty to encourage sparsity in the coefficients. Thus, the residual component minimized is still the sum of squared errors.
Why the other options are incorrect:
* A: |e| is absolute error, not used in standard LASSO objective.
* B: e is the error term, but minimization applies to its squared version.
* C: Minimizing to exactly 0 is idealistic but not realistic.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"LASSO minimizes squared errors with an additional L1 regularization term."
* Elements of Statistical Learning, Chapter 6:"LASSO regression uses the same residual sum of squares (e²) as OLS for error measurement, with an added constraint."
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NEW QUESTION # 36
Which of the following distributions would be best to use for hypothesis testing on a data set with 20 observations?
Answer: A
Explanation:
With only 20 observations and an unknown population variance, the t-distribution (with 𝑛 - 1 degrees of freedom) properly accounts for the extra uncertainty in the standard error when performing hypothesis tests.
NEW QUESTION # 37
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