IBM Foundations of Data Science using watsonx (C1000-177) Certification Sample Questions

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IBM C1000-177 Sample Questions:

01. Results are being presented to an executive sponsor with no statistical background.

What should lead the presentation?

a) The evaluation metrics, with the modeling choices that produced them explained in sequence

b) The code used to train the model

c) The business outcome the model enables and its expected effect

d) The full list of features considered and discarded

02. Six months after deployment a model's accuracy has fallen steadily while the code is unchanged.

What is the most likely explanation?

a) The test set was too small

b) The evaluation metric was chosen incorrectly at the outset

c) The model has gradually overfitted to the production data it has been scoring

d) The incoming data has drifted away from what the model was trained on

03. Which practices support keeping a deployed model trustworthy over time?

(Choose two.)

a) Versioning the model together with the data and code that produced it

b) Reporting the original test score as the current performance figure

c) Monitoring input distributions and prediction quality on an ongoing basis

d) Retraining on a fixed schedule regardless of observed performance

04. A complex ensemble outperforms a logistic regression by 0.004 AUC, but the deployment context requires each decision to be justified to the affected customer.

What follows?

a) Deploy the ensemble and generate the explanations from feature importances

b) The simpler model can be the right choice despite the lower score

c) Deploy the ensemble, since predictive performance is the deciding criterion

d) Retrain the logistic regression until it matches the ensemble

05. On a heavily imbalanced problem, which curve gives a more informative picture of a classifier?

a) A residual plot

b) The ROC curve, since it summarizes performance across every possible decision threshold

c) A learning curve

d) The precision-recall curve

06. What is the purpose of a validation set that is separate from the test set?

a) To increase the amount of data available for training the final model

b) To make model and hyperparameter choices without consuming the final unbiased estimate

c) To balance the class distribution

d) To detect missing values before training

07. A binary feature is set in 0.2% of records and flags a rare regulatory condition that strongly predicts the outcome.

What follows for a near-zero-variance filter?

a) The column should be oversampled with synthetic records before filtering

b) The column should be scaled so the rare value carries more weight

c) Applying it here would discard a rare but highly predictive column

d) It should be applied, as any near-constant column carries no information

08. How does linear discriminant analysis differ from principal component analysis as a reduction technique?

a) It uses the class labels to find directions that separate the classes

b) It works only on a single feature at a time

c) It requires the features to be categorical

d) It finds the directions along which the data varies most, without reference to any labels

09. A stakeholder must be able to explain which original variables drive each prediction.

What does this imply about using principal components?

a) They have no effect on interpretability

b) They are required whenever the feature count is high, because a model cannot fit many predictors

c) They are a poor fit, with each component blending many original variables

d) They are acceptable, since the component loadings show how much each original variable contributed

10. What does the explained variance ratio tell you when choosing how many principal components to keep?

a) The share of the data's total variance each component accounts for

b) The proportion of records each component describes

c) How correlated the components are with each other

d) How accurately a model built on those components will predict the target variable

Answers:

Question: 01 Answer: c	Question: 02 Answer: d	Question: 03 Answer: a, c	Question: 04 Answer: b	Question: 05 Answer: d
Question: 06 Answer: b	Question: 07 Answer: c	Question: 08 Answer: a	Question: 09 Answer: c	Question: 10 Answer: a

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