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DY0-001 Practice Exam Pdf, DY0-001 Latest Exam Forum

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CompTIA DataX Certification Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
A data scientist is merging two tables. Table 1 contains employee IDs and roles. Table 2 contains employee IDs and team assignments. Which of the following is the best technique to combine these data sets?

A. left join on Table 1 with Table 2
B. outer join between Table 1 and Table 2
C. inner join between Table 1 and Table 2
D. right join on Table 1 with Table 2

Answer: C

Explanation:
# An inner join returns only those records that have matching keys (employee IDs in this case) in both tables.
Since each table provides a different attribute for the same entity (employee), an inner join is the most efficient and accurate method when focusing on employees present in both tables.
Why the other options are less ideal:
* B & C: Left or right joins would include unmatched data, which may lead to nulls.
* D: An outer join brings in all records from both tables and fills nulls where no matches exist, which may introduce irrelevant or incomplete entries.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.2:"Inner joins are most appropriate when combining datasets with matching keys to retain only relevant, intersecting records."
* SQL for Data Analysts, Chapter 3:"Use inner joins when combining tables on a common key to include only matched data for analysis."
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NEW QUESTION # 17
Which of the following is the naive assumption in Bayes' rule?

A. Independence
B. Homoskedasticity
C. Uniform distribution
D. Normal distribution

Answer: A

Explanation:
# In the context of Naive Bayes classifiers, the "naive" assumption refers to the conditional independence of features given the class label. That is, the model assumes each feature contributes independently to the probability of the output class, which simplifies the computation of probabilities.
Why the other options are incorrect:
* A: Normal distribution is often assumed for continuous variables, but it's not the naive assumption in Bayes' rule.
* C: Uniform distribution refers to equal probability across outcomes, not used here.
* D: Homoskedasticity is related to constant variance in regression, not Bayesian classification.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.1:"Naive Bayes assumes all features are conditionally independent given the target class, which allows for efficient computation."
-

NEW QUESTION # 18
A data analyst wants to generate the most data using tables from a database. Which of the following is the best way to accomplish this objective?

A. INNER JOIN
B. RIGHT OUTER JOIN
C. LEFT OUTER JOIN
D. FULL OUTER JOIN

Answer: D

Explanation:
# FULL OUTER JOIN returns all rows from both tables, inserting NULLs where no match exists. This join includes the maximum possible number of records - all matches, plus all unmatched records from both sides.
Why the other options are incorrect:
* A: INNER JOIN returns only matching rows - less total data.
* B & C: LEFT/RIGHT JOIN include all rows from one table only.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.2:"A FULL OUTER JOIN maximizes data volume by including all matched and unmatched records from both tables."
* SQL for Data Science, Chapter 4:"Use FULL OUTER JOIN when the goal is to preserve every record from both datasets regardless of match."
-

NEW QUESTION # 19
A data scientist is building a proof of concept for a commercialized machine-learning model. Which of the following is the best starting point?

A. Model selection
B. Model performance evaluation
C. Literature review
D. Hyperparameter tuning

Answer: A

Explanation:
# In the proof-of-concept phase, the first practical step is model selection - identifying which modeling technique is most appropriate based on the nature of the problem, data, and business goal. Literature reviews are helpful but usually precede model experimentation.
Why the other options are incorrect:
* A: Literature review informs planning but isn't the first hands-on step.
* B: Performance evaluation comes after models are built.
* C: Hyperparameter tuning applies after a model is chosen.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.1:"Model selection is a critical step during early prototyping when evaluating different algorithms for feasibility."
* CRISP-DM Framework - Modeling Phase:"Selecting candidate models is the first step in model development after understanding the data."

NEW QUESTION # 20
Which of the following best describes the minimization of the residual term in a ridge linear regression?

A. e
B. e²
C. |e|
D. 0

Answer: B

Explanation:
# In ridge regression, the model minimizes the sum of squared residuals (errors), with an added penalty term on the magnitude of coefficients (L2 regularization). The residual component specifically is represented by:
# e² (squared error)
Thus, ridge regression minimizes:
Minimize: #(y# # ##)² + ##(#²)
Why the other options are incorrect:
* A: |e| corresponds to L1 loss (used in Lasso).
* B: e represents the error term itself, not its minimized quantity.
* D: Zero error is ideal but practically unachievable and not the actual loss function being minimized.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 1.4:"Ridge regression minimizes the squared error term with an L2 penalty."
* Introduction to Statistical Learning, Chapter 6:"Ridge regression uses squared error loss, which emphasizes larger deviations more heavily than linear loss."
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NEW QUESTION # 21
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