Free PDF Quiz 2026 CompTIA Useful Valid Braindumps DY0-001 Free
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Exam DY0-001 Tutorial, Latest DY0-001 Study Guide
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CompTIA DataAI Certification Exam Sample Questions (Q19-Q24):
NEW QUESTION # 19
The term "greedy algorithms" refers to machine-learning algorithms that:
- A. update priors as more data is seen.
- B. make the locally optimal decision.
- C. apply a theoretical model to the distribution of the data.
- D. examine even/ node of a tree before making a decision.
Answer: B
Explanation:
Greedy algorithms build the solution iteratively by choosing at each step the option that appears best at that moment, without reconsidering earlier choices.
NEW QUESTION # 20
A data scientist is creating a responsive model that will update a product's daily pricing based on the previous day's sales volume. Which of the following resource constraints is the data scientist's greatest concern?
- A. Training time
- B. Deployment time
- C. Development time
- D. Data collection time
Answer: A
Explanation:
# Since the model must update daily based on new data, retraining must be fast enough to meet daily deadlines. Therefore, training time is the critical constraint - it determines whether pricing updates can be executed promptly.
Why the other options are incorrect:
* A: Deployment time is a one-time or infrequent process.
* C: Development time is less critical once the model is built.
* D: Data is already collected daily - assumed to be available.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.4:"Time-sensitive applications such as daily pricing require fast model retraining, making training time a critical factor."
* Real-Time ML Deployment Handbook, Chapter 6:"Retraining time is the bottleneck in time- constrained systems that adapt to fresh inputs regularly."
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NEW QUESTION # 21
Given the equation:
Xt = # + #1Xt#1 + #t, where #t # N(0, ##²)
Which of the following time series models best represents this process?
- A. ARMA(1,1)
- B. AR(1)
- C. ARIMA(1,1,1)
- D. SARIMA(1,1,1) × (1,1,1)1
Answer: B
Explanation:
# The provided equation represents an autoregressive model of order 1 (AR(1)). It describes Xt as a function of its immediately prior value (Xt#1) plus white noise.
Key identifiers:
* No differencing (so not ARIMA).
* No moving average term (so not ARMA).
* No seasonal component (so not SARIMA).
Why the other options are incorrect:
* A: ARIMA(1,1,1) includes integration and MA terms, which are absent here.
* B: ARMA(1,1) includes both AR and MA terms, but only AR is present.
* C: SARIMA involves seasonal and differencing components - not applicable here.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.5:"AR(p) models describe a variable as dependent on its previous values with no differencing or moving average."
* Time Series Analysis Textbook, Chapter 4:"Xt = #Xt-1 + #t describes an AR(1) process when #t is white noise."
-
NEW QUESTION # 22
Which of the following is a classic example of a constrained optimization problem?
- A. The cold start problem
- B. Calculating gradient descent
- C. Calculating local maximum
- D. The traveling salesman
Answer: D
Explanation:
The traveling-salesman problem seeks the shortest possible route that visits each city exactly once and returns to the start, making it a textbook example of optimization under explicit constraints.
NEW QUESTION # 23
Which of the following distributions would be best to use for hypothesis testing on a data set with 20 observations?
- A. Student's t-
- B. Normal
- C. Uniform
- D. Power law
Answer: A
Explanation:
With only 20 observations and an unknown population variance, the t-distribution (with ???? - 1 degrees of freedom) properly accounts for the extra uncertainty in the standard error when performing hypothesis tests.
NEW QUESTION # 24
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