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DASCA Senior Data Scientist Sample Questions (Q13-Q18):
NEW QUESTION # 13
OCR (Optical Character Recognition) is an application used for:
Answer: B
Explanation:
Optical Character Recognition (OCR) is the process of automatically recognizing and converting different types of documents - such as scanned paper documents, PDFs, or images - into editable and searchable text.
OCR systems use Machine Learning (ML) and Computer Vision techniques to detect and classify patterns of characters in images.
Algorithms like Convolutional Neural Networks (CNNs) are commonly used for image-based OCR.
While OCR may indirectly contribute to data mining or big data workflows, the core application is based on machine learning, where models are trained to classify and recognize text patterns.
Thus, OCR is primarily a Machine Learning application, making Option B correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: OCR and Pattern Recognition.
NEW QUESTION # 14
Which of the following is a Python library for fitting Bayesian networks to real data?
Answer: C
Explanation:
The correct answer isPyMC(Option B).
PyMC is an open-source Python library widely used forBayesian statistical modelingandprobabilistic machine learning. It provides a robust framework for defining and fitting Bayesian networks to real data usingMarkov Chain Monte Carlo (MCMC)sampling techniques, as well asvariational inferencemethods.
This makes it a powerful tool for data scientists who want to work withuncertainty modeling,probabilistic inference, andcausal reasoningin complex datasets.
Let's clarify the other options to avoid confusion:
* Option A: SciLib- There is no standard Python library by this name that is related to Bayesian networks. (It may be confused withSciPyorSciKit-Learn, but those are not specialized for Bayesian inference.)
* Option C: MyLib- This is not a recognized Python package in the data science ecosystem.
* Option D: MCMC- While Markov Chain Monte Carlo is thetechniqueused in Bayesian estimation, it is not a standalone library. Instead, PyMC implements MCMC as part of its computational framework.
* Option E: SCIMC- No such Python library exists; it appears to be a distractor.
PyMC's primary strength is its ability to let data scientists define models in aprobabilistic programming style, making it easier to represent uncertainties and hidden variables in data. This aligns with DASCA's emphasis on ensuring data scientists understand bothstatistical foundationsand thetools required to implement them programmatically.
In practice, PyMC is often used in applications such as:
* Forecasting(e.g., time series with uncertainty bounds)
* Causal inference(estimating hidden relationships in data)
* Risk modeling(finance, healthcare, or supply chain domains)
* Machine learning with uncertainty quantification
Thus,PyMCis the correct library for fitting Bayesian networks in Python.
Reference:DASCA Data Scientist Knowledge Framework (DSKF) -Programming for Data Science & Probabilistic Modeling Tools, Official DASCA Study Guide.
NEW QUESTION # 15
A burn down chart shows:
Answer: B
Explanation:
A burn down chart is a graphical representation used in Agile project management (including data science projects) to track progress. It typically plots time on the x-axis and work remaining on the y-axis.
Option A: Incorrect. Burn down charts don't measure team "energy" or motivation levels.
Option B: Correct. The chart illustrates how much work remains versus how much has been completed, helping teams visualize progress toward goals. It helps identify whether the project is on track to finish within the sprint or deadline.
Option C: Incorrect. Hours worked after dark is irrelevant.
Option D: Incorrect. Budget reduction is not tracked in burn down charts.
Thus, the purpose of a burn down chart is to show the remaining work (tasks, story points, or features) decreasing over time. This provides transparency, supports stakeholder communication, and helps teams manage pace and velocity.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Project Management & Agile Tools in Data Science.
NEW QUESTION # 16
Which of the following is TRUE for data lake?
Answer: A,D,E
Explanation:
But per MCQ single-choice format # answer: A (though ideally A, B, C are correct).
A data lake is a centralized repository designed to store raw, structured, semi-structured, and unstructured data at scale. It provides:
Agility and productivity (Option A): Data lakes support flexible ingestion and faster access, making BI and data science environments more efficient.
Data integration (Option B): They handle multiple types of data, enabling advanced analytics and machine learning use cases.
Data as an asset (Option C): They shift perspective, treating data as a strategic resource, not just a storage cost.
Option D: Incorrect. Data lakes improve agility, not reduce it.
Option E: Incorrect, since multiple true statements exist.
Thus, the correct choice per DASCA context is Option A (with B and C also being true).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering: Data Lakes vs Warehouses.
NEW QUESTION # 17
Which of the following is a "thinking like a data scientist" decomposition process?
Answer: A
Explanation:
The "Thinking Like a Data Scientist" (TLADS) decomposition process is a structured approach to align data science projects with business goals. It breaks complex business problems into smaller, analyzable parts:
Business Initiative (Option A): Defines the overarching organizational challenge or objective (e.g., reduce churn, increase revenue).
Business Stakeholder (Option B): Identifies decision-makers and end users whose requirements shape the use cases.
Strategic Nouns (Option C): Focuses on the entities (e.g., customer, product, supplier) that generate and consume data, serving as anchors for analytics design.
Since all three are valid elements of the TLADS decomposition, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Science Fundamentals: Thinking Like a Data Scientist Process.
NEW QUESTION # 18
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