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| Section | Weight | Objectives |
|---|---|---|
| Modeling, Analysis, and Outcomes | 24% | - Feature engineering and selection - Result interpretation and business communication - Model selection and evaluation metrics - Predictive and prescriptive analytics - Data preparation and exploratory data analysis |
| Specialized Applications of Data Science | 13% | - Computer Vision - Industry-specific analytics use cases - Time-series analysis - Anomaly detection and signal processing - Natural Language Processing (NLP) |
| Mathematics and Statistics | 17% | - Linear algebra fundamentals - Probability theory and distributions - Calculus and optimization concepts - Bayesian reasoning and modeling - Statistical inference and hypothesis testing |
| Machine Learning | 24% | - Deep learning fundamentals - Supervised, unsupervised, and reinforcement learning - Hyperparameter tuning and optimization - Algorithm selection and implementation - Ethics and bias in machine learning |
| Operations and Processes | 22% | - Data governance and quality management - Version control and reproducibility - Security and compliance in data operations - Model deployment and monitoring - Data pipeline design and maintenance |
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NEW QUESTION # 68
A data scientist wants to digitize historical hard copies of documents. Which of the following is the best method for this task?
Answer: A
Explanation:
OCR converts scanned images of text into machine‐readable characters, making it the appropriate tool for digitizing printed or handwritten historical documents.
NEW QUESTION # 69
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: C,F
NEW QUESTION # 70
Which of the following explains back propagation?
Answer: B
Explanation:
Back propagation computes the gradient of the loss (error) with respect to each weight by propagating the error signal backward through the network, then uses those gradients to adjust weights and biases.
NEW QUESTION # 71
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 # 72
A data scientist needs to analyze a company's chemical businesses and is using the master database of the conglomerate company. Nothing in the data differentiates the data observations for the different businesses.
Which of the following is the most efficient way to identify the chemical businesses' observations?
Answer: A
Explanation:
# The most efficient and practical approach is to consult the business stakeholders to understand which sites or data partitions relate to chemical operations. This avoids unnecessary processing of irrelevant data and aligns with the data science best practice of combining domain knowledge with technical methods.
Why the other options are incorrect:
* A: Ingesting all data without guidance is time- and resource-intensive.
* B: Analyzing all data indiscriminately can dilute the focus on chemical business specifics.
* D: Using the largest data set arbitrarily may not reflect chemical operations and lacks targeted relevance.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.1:"Collaboration with domain experts and stakeholders ensures the data scientist focuses on relevant sources and minimizes inefficiency in data preparation."
* CRISP-DM Model - Business Understanding Phase:"Clarifying project objectives with business input is key to aligning data selection with analytical goals."
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NEW QUESTION # 73
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