C1000-177 Sample Questions

C1000-177 Sample Questions & Answers

Pre-processing and feature engineering, including normalizing and rebalancing data, carries by far the most weight, alongside connecting business problems to data science solutions, exploratory analysis, choosing the right modeling tools, and evaluating models.

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Showing 6 of 12 free samples.

  1. Question 1Advanced

    Evaluate the Business Problem · Translate business objectives into Data Science/ML/AI solutions

    When defining success criteria for a machine learning project aimed at reducing customer churn, which metric provides the most business value when the cost of retaining a customer is low compared to the cost of losing them?

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    Correct answer: A

    When the cost of intervention (retention offer) is low and the cost of missing a churner (False Negative) is high, the business objective is to catch as many churners as possible. High Recall minimizes False Negatives.

  2. Question 2Beginner

    Evaluate the Business Problem · Identify appropriate tools for analysis

    A data scientist needs to rapidly prototype a regression model to predict housing prices using a dataset with 50 columns. The priority is to establish a performance baseline quickly without writing extensive code. Which IBM watsonx tool is most appropriate?

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    Correct answer: C

    IBM AutoAI is designed specifically for rapid experimentation and baseline establishment. It automates data preparation, model selection, and hyperparameter tuning, allowing the data scientist to get a baseline quickly without manual coding.

  3. Question 3Beginner

    Evaluate the Business Problem · Translate business objectives into Data Science/ML/AI solutions

    A retail company wants to group its customers into distinct segments based on purchasing behavior for targeted marketing. No predefined labels exist for these customers. Which type of machine learning approach should be proposed?

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    Correct answer: C

    Since there are no predefined labels (ground truth) and the goal is to group similar entities, Unsupervised Learning using Clustering algorithms (like K-Means) is the correct approach.

  4. Question 4Advanced

    Evaluate the Business Problem · Translate business objectives into Data Science/ML/AI solutions

    Case Study: A telecommunications firm is experiencing high customer churn.

    Context:

    • The marketing team has a budget to offer a 20% discount to 10,000 users.
    • Total customer base is 1 million.
    • The cost of the discount is $20 per user.
    • The revenue loss from a churned customer is $500.

    Objective:
    Identify which customers to target to maximize ROI.

    Which combination of actions defines the correct data science problem formulation?

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    Correct answer: B

    This is a resource-constrained maximization problem. By modeling churn probability (Classification), you can rank customers. Targeting the top 10,000 highest-risk customers focuses the limited budget on those most likely to cause the $500 loss, thereby maximizing the potential ROI of the retention campaign.

  5. Question 5Beginner

    Perform Exploratory Data Analysis · Visually examine the data for data understanding

    You are performing EDA on a dataset containing a 'Salary' feature. You generate a box plot and observe several points beyond the upper whisker. The whisker extends to Q3 + 1.5 * IQR. What do these points represent?

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    Correct answer: B

    In a box plot, points lying beyond the whiskers (typically defined as Q3 + 1.5IQR or Q1 - 1.5IQR) are statistically considered potential outliers that may require further investigation or handling.

  6. Question 6Intermediate

    Perform Exploratory Data Analysis · Assess data characteristics to guide future processing

    While analyzing a dataset using pandas in a Jupyter Notebook, you run df.describe(). You notice that the 'Age' column has a count of 500, a mean of 35, and a maximum value of 999. The dataset has 500 rows. What does this suggest?

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    Correct answer: B

    A maximum age of 999 is biologically impossible for humans and typically indicates a placeholder code for 'unknown' or a data entry error. This outlier will skew the mean and must be handled before modeling.

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