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| Section | Weight | Objectives |
|---|---|---|
| Modeling, Analysis, and Outcomes | 24% | - Data preparation and exploratory data analysis - Result interpretation and business communication - Feature engineering and selection - Model selection and evaluation metrics - Predictive and prescriptive analytics |
| Operations and Processes | 22% | - Version control and reproducibility - Model deployment and monitoring - Data pipeline design and maintenance - Data governance and quality management - Security and compliance in data operations |
| Specialized Applications of Data Science | 13% | - Industry-specific analytics use cases - Natural Language Processing (NLP) - Anomaly detection and signal processing - Time-series analysis - Computer Vision |
| Machine Learning | 24% | - Supervised, unsupervised, and reinforcement learning - Ethics and bias in machine learning - Deep learning fundamentals - Hyperparameter tuning and optimization - Algorithm selection and implementation |
| Mathematics and Statistics | 17% | - Calculus and optimization concepts - Bayesian reasoning and modeling - Statistical inference and hypothesis testing - Probability theory and distributions - Linear algebra fundamentals |
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NEW QUESTION # 17
Which of the following layer sets includes the minimum three layers required to constitute an artificial neural network?
Answer: B
Explanation:
# A basic artificial neural network (ANN) consists of:
* An input layer to receive data
* At least one hidden layer to process the data
* An output layer to produce predictions
These three layers form the minimal architecture required for learning and transformation.
Why the other options are incorrect:
* A: Pooling layers are used in CNNs, not core ANN structure.
* B: Convolutional layers are specific to CNNs.
* D: Dropout is a regularization technique, not a required component.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.3:"ANNs must include an input layer, hidden layer(s), and an output layer to form a complete learning structure."
* Deep Learning Fundamentals, Chapter 3:"At a minimum, a neural network includes input, hidden, and output layers to process and propagate data."
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NEW QUESTION # 18
A company created a very popular collectible card set. Collectors attempt to collect the entire set, but the availability of each card varies, with because some cards have higher production volumes than others. The set contains a total of 12 cards. The attributes of the cards are below:
A data scientist is provided a historical record of cards purchased, which was acquired by a local collectors' association. The data scientist needs to design an initial model iteration to predict whether or not the animal on the card lives in the sea or on land given the provided attributes. Which of the following is the best way to accomplish this task?
Answer: B
Explanation:
You have categorical inputs (wrapper color, shape, animal) and a binary target (sea vs. land). A decision tree natively handles categorical features and yields clear, rule-based splits that predict habitat, making it the most appropriate choice.
NEW QUESTION # 19
A data scientist is building a forecasting model for the price of copper. The only input in this model is the daily price of copper for the last ten years. Which of the following forecasting techniques is the most appropriate for the data scientist to use?
Answer: D
Explanation:
# An Autoregressive (AR) model is ideal when past values of a time series are used to predict future values.
Since the only input is historical price data of copper, AR is the most appropriate technique.
Why the other options are incorrect:
* B: Moving average smooths noise but doesn't model the dependencies for prediction.
* C: Dynamic time warping is used for measuring similarity between time series, not forecasting.
* D: Relative strength is a financial metric used for comparing asset performance - not a forecasting technique.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.5:"Autoregressive models are used when the goal is to predict future values based solely on past values in a univariate time series."
* Time Series Analysis and Forecasting, Chapter 5:"AR models capture the temporal dependencies in time series data and are foundational in time-based prediction."
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NEW QUESTION # 20
Which of the following types of layers is used to downsample feature detection when using a convolutional neural network?
Answer: B
Explanation:
# Pooling layers are used in Convolutional Neural Networks (CNNs) to reduce the spatial dimensions (width and height) of the feature maps. This helps in downsampling, reducing computational complexity, and controlling overfitting by summarizing the features (e.g., max pooling or average pooling).
Why the other options are incorrect:
* B: Input layers receive raw data and do not perform downsampling.
* C: Output layers generate the final prediction.
* D: Hidden layers process data but do not specifically perform downsampling unless designed to do so (e.g., convolutional or pooling sublayers).
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.3:"Pooling layers are used to downsample feature maps and are critical in CNNs for reducing dimensions."
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NEW QUESTION # 21
A data scientist uses a large data set to build multiple linear regression models to predict the likely market value of a real estate property. The selected new model has an RMSE of 995 on the holdout set and an adjusted R² of 0.75. The benchmark model has an RMSE of 1,000 on the holdout set. Which of the following is the best business statement regarding the new model?
Answer: B
Explanation:
# The difference between the benchmark RMSE (1,000) and the new model RMSE (995) is minimal and may not justify replacing the existing model. Though the adjusted R² is decent, business decisions should be based on whether the improvement is statistically and practically significant.
Why the other options are incorrect:
* A: The RMSE improvement is marginal and may not be worth deployment effort.
* B: The adjusted R² of 0.75 is moderate, not necessarily "exceptionally strong."
* D: The claim about industry standards is unsupported and not universally true.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.2:"Model selection must consider both statistical improvement and practical significance."
* Data Science Best Practices, Chapter 8:"Small improvements in performance metrics must be evaluated in the context of deployment cost and business impact."
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NEW QUESTION # 22
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