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DASCA SDS Questions: Improve Your Exam Preparation [2026]
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DASCA Senior Data Scientist Sample Questions (Q84-Q89):
NEW QUESTION # 84
Which of the following is TRUE about Avro?
- A. Avro is a data serialization framework
- B. None of the above
- C. Both A and B
- D. Avro is based on Remote Procedure Call (RPC)
Answer: C
Explanation:
Apache Avro is a widely used framework within the Hadoop ecosystem for data serialization and data exchange.
Option A (Correct): Avro is a compact, fast, binary data serialization format. It allows efficient storage and exchange of structured data.
Option B (Correct): Avro supports Remote Procedure Call (RPC). It provides a framework for RPC communication, making it easier for distributed applications to exchange data across systems.
Option C: Correct, since both statements are true.
Option D: Incorrect because Avro is indeed both a serialization framework and RPC-based.
In data engineering workflows, Avro is valuable because it is schema-based (defined using JSON), highly interoperable, and ensures compatibility across different programming languages. This makes it essential in big data pipelines, Kafka messaging, and Hadoop ecosystem tools.
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem Tools & Data Serialization Techniques.
NEW QUESTION # 85
JSON takes hierarchical data structures and serializes them into:
- A. Plain text format
- B. Any desired format
- C. None of the above
- D. Both A and B
- E. Plain string format
Answer: D
Explanation:
JSON (JavaScript Object Notation) is a lightweight data-interchange format widely used for storing and exchanging structured or semi-structured data. JSON allows hierarchical (tree-like) structures, such as nested objects and arrays, to be serialized into a textual representation.
Option A (Plain text format): Correct. JSON files are stored as plain text, making them human-readable and language-independent.
Option B (Plain string format): Correct. JSON objects are transmitted as strings across networks (e.g., via APIs, RESTful services).
Option C: Incorrect. JSON does not serialize into "any format," but specifically into text/string-based formats.
Option D: Correct. Since JSON is both plain text and transmitted as string format, the right answer is both A and B.
Option E: Incorrect.
Thus, JSON serializes hierarchical data into plain text and string formats.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Tools: Data Serialization Formats (JSON, XML, Avro).
NEW QUESTION # 86
Which of the following is TRUE for Chief Data Monetization Officer (CDMO)?
i. CDMO should focus on driving and deriving value from the organization's data and analytic assets.
ii. The CDMO should own the organization's investment decisions with respect to data and analytics.
iii. CDMO should have revenue and margin responsibilities.
- A. i, ii
- B. All of the above
- C. ii, iii
Answer: B
Explanation:
The Chief Data Monetization Officer (CDMO) is a C-level executive role emerging in data-driven organizations. Their focus is on turning data into business value and revenue growth.
Statement i: Correct. The CDMO is responsible for value realization from data and analytics investments.
Statement ii: Correct. The CDMO owns decisions around investment prioritization for data initiatives.
Statement iii: Correct. Since monetization is directly linked to business performance, the CDMO must be accountable for revenue and margin impact.
Hence, all three statements are correct, making the right answer Option C (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Leadership & Monetization Roles.
NEW QUESTION # 87
Which of the following architectural techniques is used for parallel processing?
- A. The SuperVector Technique
- B. Both B and C
- C. Very Long Instruction Words (VLIW) Technique
- D. Both A and B
- E. The Superscalar Technique
Answer: D
Explanation:
Parallel processing architectures are designed to execute multiple instructions or operations simultaneously:
Superscalar Technique (Option A): Uses multiple execution units so that several instructions can be issued and executed in parallel within a single CPU cycle.
VLIW Technique (Option B): Uses very long instruction words, where multiple operations are encoded into a single instruction and executed in parallel.
SuperVector (Option C): Refers to vector processors, which process large arrays of data but is not classified as a mainstream architectural parallel technique in modern CPU design.
Therefore, the primary architectural techniques for parallel processing are Superscalar and VLIW, making Option D (Both A and B) correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Parallel and Distributed Processing.
NEW QUESTION # 88
Which of the following is a Python library for fitting Bayesian networks to real data?
- A. PyMC
- B. SciLib
- C. MCMC
- D. SCIMC
- E. MyLib
Answer: A
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 # 89
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