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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operations and Processes | 22% | - Data governance and quality management - Model deployment and monitoring - Data pipeline design and maintenance - Security and compliance in data operations - Version control and reproducibility |
| Topic 2: Modeling, Analysis, and Outcomes | 24% | - Model selection and evaluation metrics - Data preparation and exploratory data analysis - Predictive and prescriptive analytics - Result interpretation and business communication - Feature engineering and selection |
| Topic 3: Mathematics and Statistics | 17% | - Statistical inference and hypothesis testing - Bayesian reasoning and modeling - Calculus and optimization concepts - Linear algebra fundamentals - Probability theory and distributions |
| Topic 4: Machine Learning | 24% | - Hyperparameter tuning and optimization - Deep learning fundamentals - Supervised, unsupervised, and reinforcement learning - Algorithm selection and implementation - Ethics and bias in machine learning |
| Topic 5: Specialized Applications of Data Science | 13% | - Computer Vision - Natural Language Processing (NLP) - Industry-specific analytics use cases - Anomaly detection and signal processing - Time-series analysis |
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NEW QUESTION # 52
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: C
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 # 53
A data scientist is standardizing a large data set that contains website addresses. A specific string inside some of the web addresses needs to be extracted. Which of the following is the best method for extracting the desired string from the text data?
Answer: B
NEW QUESTION # 54
A data scientist is building a model to predict customer credit scores based on information collected from reporting agencies. The model needs to automatically adjust its parameters to adapt to recent changes in the information collected. Which of the following is the best model to use?
Answer: B
Explanation:
XGBoost supports "warm-start" incremental training, continuing to refine the existing ensemble with new data, so it can automatically update its parameters as new agency information arrives. The other methods require full retraining to incorporate recent changes.
NEW QUESTION # 55
Which of the following JOINS would generate the largest amount of data?
Answer: B
Explanation:
A CROSS JOIN produces the Cartesian product of the two tables (every row from the first paired with every row from the second), yielding far more rows than any of the other join types.
NEW QUESTION # 56
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: B
Explanation:
Compression and decompression are CPU‐intensive operations; on large data sets, the extra processing load can significantly spike CPU utilization. Memory, OS support, or library dependencies are far less likely to be the primary bottleneck in a standard compression workflow.
NEW QUESTION # 57
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