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| Section | Weight | Objectives |
|---|---|---|
| Specialized Applications of Data Science | 13% | - Industry-specific analytics use cases - Time-series analysis - Natural Language Processing (NLP) - Computer Vision - Anomaly detection and signal processing |
| Operations and Processes | 22% | - Model deployment and monitoring - Data pipeline design and maintenance - Security and compliance in data operations - Version control and reproducibility - Data governance and quality management |
| Mathematics and Statistics | 17% | - Bayesian reasoning and modeling - Linear algebra fundamentals - Statistical inference and hypothesis testing - Probability theory and distributions - Calculus and optimization concepts |
| Machine Learning | 24% | - Hyperparameter tuning and optimization - Supervised, unsupervised, and reinforcement learning - Ethics and bias in machine learning - Deep learning fundamentals - Algorithm selection and implementation |
| Modeling, Analysis, and Outcomes | 24% | - Result interpretation and business communication - Feature engineering and selection - Model selection and evaluation metrics - Data preparation and exploratory data analysis - Predictive and prescriptive analytics |
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NEW QUESTION # 58
A data analyst wants to use compression on an analyzed data set and send it to a new destination for further processing. Which of the following issues will most likely occur?
Answer: D
Explanation:
# Compression is a CPU-intensive process because it requires encoding data into a smaller format, often involving complex algorithms. While memory use is usually moderate, CPU usage can spike significantly, especially during real-time compression or large dataset processing.
Why the other options are incorrect:
* A: Library issues are possible but not the most likely issue in compression.
* C: Most operating systems support common compression formats (e.g., .zip, .gz).
* D: Memory usage is generally lower than CPU usage during compression.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.4:"Compression is compute-intensive and may result in increased CPU utilization, particularly on shared servers or during large batch processes."
* Cloud Data Engineering Guide, Chapter 9:"High CPU usage is a common bottleneck in data compression and decompression processes, especially at scale."
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NEW QUESTION # 59
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: C
Explanation:
# Occam's razor is a principle that suggests selecting the simplest solution that sufficiently explains the data.
In data science, this translates to favoring simpler models (fewer features) when performance is similar.
Therefore, the model with the fewest features and the highest performance is preferred - balancing simplicity and effectiveness.
Why the other options are incorrect:
* B: Poor performance undermines utility.
* C & D: More features add complexity and risk overfitting, making them less desirable when simpler models suffice.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 3.2:"Simplicity in models improves interpretability and robustness. When models perform similarly, the simpler model should be preferred."
* Data Science Principles, Chapter 5:"Occam's razor encourages the use of fewer features to minimize complexity while preserving accuracy."
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NEW QUESTION # 60
A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)
Answer: E,F
NEW QUESTION # 61
Which of the following is best solved with graph theory?
Answer: C
Explanation:
The traveling-salesman problem is a prototypical graph theory challenge, finding the shortest tour through a graph's nodes, whereas the other tasks rely on different domains (OCR on image processing, fraud detection often on statistical/anomaly methods, bandit problems on sequential decision theory).
NEW QUESTION # 62
Which of the following methods should a data scientist use just before switching to a potential replacement model?
Answer: D
Explanation:
A/B testing lets you compare the current model against the candidate in parallel, measuring performance on live data, before fully switching to the new model.
NEW QUESTION # 63
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