C1000-177 Foundations of Data Science using IBM watsonx Practice Questions
Prepare for C1000-177 with more than an answer.
- Exam fee
- $200 USD
- Time limit
- 90 minutes
- Questions on the exam
- 61
- Level
- Associate
- Valid for
- Not specified by IBM
Domains covered on the exam 5
- Evaluate the Business Problem16%
- Perform Exploratory Data Analysis21%
- Development Tools and Techniques13%
- Pre-Processing and Feature Engineering33%
- Model Selection, Training, Evaluation, and Presentation17%
- 1
You are analyzing a dataset with two continuous variables: 'YearsExperience' and 'Salary'. You calculate a Pearson correlation coefficient of 0.85. What does this indicate?
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Correct answer: A
Pearson correlation ranges from -1 to 1. A value of 0.85 indicates a strong positive linear relationship, meaning as YearsExperience increases, Salary also tends to increase significantly.
- 2
Which visualization technique is best suited for identifying multicollinearity between multiple numerical features in a dataset?
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Correct answer: B
A Correlation Heatmap visually displays the correlation coefficients between all pairs of numerical features. High values (close to 1 or -1) indicate multicollinearity, making it the most efficient tool for this purpose.
- 3
You are preparing a dataset for a loan default prediction model. You find a feature 'Transaction_ID' which has a unique value for every single row. Why should this feature be deselected?
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Correct answer: D
A feature where every value is unique (like an ID) has maximum cardinality. It may allow a model to 'memorize' the training data (overfitting) but provides no generalizable pattern for predicting new, unseen data.
- 4
A retail bank wants to implement a new fraud detection system using IBM watsonx.ai. The stakeholders require that the model not only predicts fraud accurately but also provides human-readable reasons for each flagged transaction to comply with regulatory explanation requirements. Which approach best satisfies these business objectives?
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Correct answer: C
While deep learning might offer slightly higher accuracy, the strict regulatory requirement for human-readable reasons favors intrinsically interpretable models like Decision Trees or Logistic Regression. These models provide direct feature importance and decision paths without relying on approximations like LIME.
- 5
A data scientist is defining the project lifecycle for a predictive maintenance solution in manufacturing. According to the CRISP-DM methodology, which phase immediately follows 'Data Understanding' and what is its primary focus?
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Correct answer: B
In the CRISP-DM lifecycle, Data Preparation follows Data Understanding. This phase involves all activities to construct the final dataset (data that will be fed into the modeling tool) from the initial raw data, including cleaning, feature engineering, and formatting.
flowchart LR BU[Business Understanding] --> DU[Data Understanding] DU --> DP[Data Preparation] DP --> M[Modeling] M --> E[Evaluation] E --> D[Deployment] - 6
A marketing team wants to test if a new email subject line results in a statistically significantly higher open rate than the current subject line. They plan to run an A/B test. What are the correct Null Hypothesis (H0) and Alternative Hypothesis (H1) for this scenario?
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Correct answer: C
The goal is to prove the new rate is higher. The Null Hypothesis (H0) typically represents the status quo or no effect (the new rate is less than or equal to the old). The Alternative Hypothesis (H1) represents the effect we wish to prove (the new rate is strictly greater).
