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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 24% | - Algorithm selection and implementation - Hyperparameter tuning and optimization - Ethics and bias in machine learning - Deep learning fundamentals - Supervised, unsupervised, and reinforcement learning |
| Operations and Processes | 22% | - Data pipeline design and maintenance - Data governance and quality management - Model deployment and monitoring - Security and compliance in data operations - Version control and reproducibility |
| Mathematics and Statistics | 17% | - Statistical inference and hypothesis testing - Linear algebra fundamentals - Calculus and optimization concepts - Bayesian reasoning and modeling - Probability theory and distributions |
| Modeling, Analysis, and Outcomes | 24% | - Result interpretation and business communication - Predictive and prescriptive analytics - Data preparation and exploratory data analysis - Model selection and evaluation metrics - Feature engineering and selection |
| Specialized Applications of Data Science | 13% | - Time-series analysis - Anomaly detection and signal processing - Computer Vision - Natural Language Processing (NLP) - Industry-specific analytics use cases |
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問題 #44
A data scientist receives an update on a business case about a machine that has thousands of error codes. The data scientist creates the following summary statistics profile while reviewing the logs for each machine:
| Number of machines observed | 3,000,000
| Number of unique error codes observed | 19,000
| Median number of unique codes per machine | 7
| Median number of error transactions | 45
Which of the following is the most likely concern with respect to data design for model ingestion?
答案：C
解題說明：
# With 19,000 unique error codes and only 7 codes per machine (on median), the data structure will likely consist of a very large number of binary features (e.g., one-hot encoded error codes), most of which will be 0 for any given machine. This leads to a sparse matrix-where the majority of elements are zero-which poses computational and modeling challenges.
Why the other options are incorrect:
* B: Granularity misalignment would mean mismatched levels (e.g., mixing daily and hourly data), which is not the issue here.
* C: There are many features (error codes), not too few.
* D: Multivariate outliers involve unusual combinations across features, not sparsity.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"High-cardinality categorical features can result in sparse matrices, especially when one-hot encoded for models."
問題 #45
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?
答案：D
解題說明：
# 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."
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問題 #46
Which of the following problem-solving approaches is a set of guidelines to handle highly variable and not fully apparent situations?
答案：C
解題說明：
Heuristics are rule-of-thumb strategies that guide problem solving in complex, uncertain situations where a fixed algorithm or plan isn't feasible.
問題 #47
Which of the following distributions would be best to use for hypothesis testing on a data set with 20 observations?
答案：D
解題說明：
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.
問題 #48
A data scientist would like to model a complex phenomenon using a large data set composed of categorical, discrete, and continuous variables. After completing exploratory data analysis, the data scientist is reasonably certain that no linear relationship exists between the predictors and the target. Although the phenomenon is complex, the data scientist still wants to maintain the highest possible degree of interpretability in the final model. Which of the following algorithms best meets this objective?
答案：C
解題說明：
# Decision trees offer excellent interpretability while handling complex, non-linear relationships and multiple variable types (categorical, discrete, continuous). They provide easy-to-understand visualizations and logic- based rules, making them ideal when transparency and insight are priorities.
Why other options are incorrect:
* A: Neural networks are powerful but are considered "black box" models, with low interpretability.
* C: Linear regression assumes a linear relationship, which contradicts the scenario.
* D: Random forests are ensembles of trees - more accurate, but less interpretable.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.2:"Decision trees are interpretable models that support non-linear, multi-type data with logical branching."
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問題 #49
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