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| Section | Weight | Objectives |
|---|---|---|
| Operations and Processes | 22% | - Security and compliance in data operations - Data governance and quality management - Data pipeline design and maintenance - Version control and reproducibility - Model deployment and monitoring |
| Mathematics and Statistics | 17% | - Calculus and optimization concepts - Statistical inference and hypothesis testing - Probability theory and distributions - Linear algebra fundamentals - Bayesian reasoning and modeling |
| Machine Learning | 24% | - Algorithm selection and implementation - Supervised, unsupervised, and reinforcement learning - Deep learning fundamentals - Ethics and bias in machine learning - Hyperparameter tuning and optimization |
| Modeling, Analysis, and Outcomes | 24% | - Feature engineering and selection - Model selection and evaluation metrics - Result interpretation and business communication - Predictive and prescriptive analytics - Data preparation and exploratory data analysis |
| 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 |
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NEW QUESTION # 35
Which of the following image data augmentation techniques allows a data scientist to increase the size of a data set?
Answer: D
Explanation:
# Cropping involves selecting portions of an image to create multiple training samples from one image. This technique helps increase dataset size and variability, which improves model generalization.
Why the other options are incorrect:
* A: Clipping typically refers to limiting pixel values, not augmentation.
* C: Masking hides or removes parts of an image - used more in object detection or inpainting, not to expand the dataset.
* D: Scaling changes the image size but doesn't create new samples.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 6.3:"Cropping is a data augmentation strategy that allows for synthetic expansion of the dataset by generating multiple views."
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NEW QUESTION # 36
Which of the following best describes the minimization of the residual term in a LASSO linear regression?
Answer: C
Explanation:
LASSO regression retains the ordinary least squares loss by minimizing the sum of squared residuals (eยฒ), with an added L1 penalty on the coefficients, but the residual term itself remains squared.
NEW QUESTION # 37
A data scientist is attempting to identify sentences that are conceptually similar to each other within a set of text files. Which of the following is the best way to prepare the data set to accomplish this task after data ingestion?
Answer: B
Explanation:
Generating embeddings transforms each sentence into a dense numerical vector in a semantic space, where conceptually similar sentences lie close together, enabling straightforward similarity calculations (e.g., cosine similarity) to group or identify related sentences.
NEW QUESTION # 38
Which of the following techniques enables automation and iteration of code releases?
Answer: C
Explanation:
Continuous Integration/Continuous Deployment pipelines automate the building, testing, and delivery of code, enabling rapid, repeatable, and iterative releases with minimal manual intervention.
NEW QUESTION # 39
A data scientist is merging two tables. Table 1 contains employee IDs and roles. Table 2 contains employee IDs and team assignments. Which of the following is the best technique to combine these data sets?
Answer: C
Explanation:
# An inner join returns only those records that have matching keys (employee IDs in this case) in both tables.
Since each table provides a different attribute for the same entity (employee), an inner join is the most efficient and accurate method when focusing on employees present in both tables.
Why the other options are less ideal:
* B & C: Left or right joins would include unmatched data, which may lead to nulls.
* D: An outer join brings in all records from both tables and fills nulls where no matches exist, which may introduce irrelevant or incomplete entries.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.2:"Inner joins are most appropriate when combining datasets with matching keys to retain only relevant, intersecting records."
* SQL for Data Analysts, Chapter 3:"Use inner joins when combining tables on a common key to include only matched data for analysis."
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NEW QUESTION # 40
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