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CIW AI Data Science Specialist Free Sample Questions

Description

Covers data science fundamentals, ethics and privacy, exploratory data analysis, modeling, visualization and statistics, database querying and data preparation, and programming and consulting skills.

12 free sample questions150 in the full practice test

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1D0-184 Sample Questions

  1. Question 1

    Intermediate

    Data Science Overview · 1.1: Fundamentals

    A data science consultant is analyzing a dataset containing employee satisfaction scores (0-10) and annual attrition rates. The calculated Pearson correlation coefficient (r) between these two variables is -0.85.

    Which statement accurately interprets this statistical result?

    Answer and explanation

    Correct answer: A

    A Pearson correlation coefficient (r) of -0.85 indicates a strong negative linear relationship. The negative sign implies an inverse relationship (as one variable goes up, the other goes down), and the magnitude (0.85) is close to 1, indicating strength.

  2. Question 2

    Beginner

    Data Science Overview · 1.1: Fundamentals

    A retail company wants to implement a system that automatically identifies and flags fraudulent transactions in real-time without human intervention. The system must learn from new fraud patterns as they emerge daily.

    Which terminology BEST describes the core technology required for this solution?

    Answer and explanation

    Correct answer: D

    Machine Learning is the specific subset of AI defined by the ability of a system to learn from data and improve from experience (new fraud patterns) without being explicitly programmed for every specific rule.

  3. Question 3

    Intermediate

    Data Science Overview · 1.2: Legal, Ethics and Privacy Considerations

    A financial institution is building a credit scoring model. During testing, the team discovers the model consistently assigns lower credit scores to applicants from a specific zip code, despite income levels being equal to other regions.

    Which ethical issue is demonstrated in this scenario?

    Answer and explanation

    Correct answer: C

    This is a classic example of algorithmic bias, where the model has learned a prejudice (redlining) based on geographic features that proxy for protected characteristics, resulting in unfair outcomes for a specific group.

  4. Question 4

    Beginner

    Data Science Overview · 1.2: Legal, Ethics and Privacy Considerations

    True or False: Pseudonymization is a data protection method that renders data completely anonymous and irreversible, such that the data subject can never be re-identified.

    Answer and explanation

    Correct answer: B

    Pseudonymization replaces identifying fields with artificial identifiers (pseudonyms) but retains a key that allows re-identification. Anonymization is the process that makes re-identification impossible. Therefore, the statement is False.

  5. Question 5

    IntermediateMultiple answers

    Data Science Overview · 1.2: Legal, Ethics and Privacy Considerations

    A data scientist needs to secure a web-based survey application collecting sensitive health information.

    Which TWO web security standards are most critical to implement to ensure data integrity and privacy during transmission? (Select TWO)

    Answer and explanation

    Correct answers: A, C

    OAuth 2.0 and OIDC handle secure authorization and authentication, ensuring only authorized users access the survey system.

    TLS (Transport Layer Security) encrypts data in transit, preventing interception.

  6. Question 6

    Beginner

    Data Science Overview · 1.3: Career

    Which source would be considered the most reliable for a data scientist to identify the latest scientifically validated trends in deep learning architectures?

    Answer and explanation

    Correct answer: C

    Peer-reviewed conferences like NeurIPS and ICML are the primary venues where cutting-edge, validated research in AI and deep learning is published and scrutinized by experts.

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