Free IBM C1000-154 Practice Test & Real Exam Questions

Exam Code/Number: C1000-154
Exam Name/Title: IBM Watson Data Scientist v1
Certification Provider: IBM
Corresponding Certification: IBM Certification

Exam Questions: 82
Updated On: Oct 04, 2026

Question #1

What is a critical consideration when selecting the right model class for a given problem?

A. The model's ability to produce results quickly, regardless of accuracy.

B. The nature of the problem (e.g., classification, regression) and the characteristics of the data.

C. The availability of high-performance computing resources.

D. The theoretical complexity of the model, with more complex models always being preferred.

Discussion 0

Correct Answer: B Vote an answer

Question #2

Given the Confusion matrix below, which is the formula for specificity?

A. TP/(FP + TP)

B. TP/(FN + TP)

C. (TP + TN)/(FN + FP + TN + TP)

D. TN/(TN + FP)

Discussion 0

Correct Answer: D Vote an answer

Question #3

Which metric is commonly used to evaluate the performance of a regression model?

A. Precision

B. Accuracy

C. Recall

D. Mean Squared Error (MSE)

Discussion 0

Correct Answer: D Vote an answer

Question #4

In the context of assessing data quality in Watson Knowledge Catalog (WKC) and Cloud Pak for Data (CPD), what is a primary focus?

A. Enhancing the graphical user interface

B. Increasing the volume of data collected

C. Analyzing completeness, consistency, and accuracy of data

D. Focusing on the data's color scheme

Discussion 0

Correct Answer: C Vote an answer

Question #5

Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?

A. Grid Search

B. Binary Search

C. Sequential Search

D. Random Search

Discussion 0

Correct Answer: A Vote an answer

Question #6

In classification models, which of the following metrics is NOT directly derived from the confusion matrix?

A. Precision

B. Mean Absolute Error (MAE)

C. Recall

D. F1-score

Discussion 0

Correct Answer: B Vote an answer

Question #7

How can data splits be made reproducible in a machine learning experiment?

A. By partitioning the data manually

B. By using a consistent random seed when splitting the data

C. By splitting the data in a sequential manner without randomization

D. By using a different random seed each time the data is split

Discussion 0

Correct Answer: B Vote an answer

Question #8

What does the term "complexity" in model comparison refer to?

A. The size of the dataset the model can handle

B. The amount of computational resources required for training and inference

C. The number of hyperparameters that need to be tuned

D. The aesthetic appeal of the model's graphical representations

Discussion 0

Correct Answer: B Vote an answer

Question #9

What is a key advantage of using supervised learning techniques over unsupervised learning techniques?

A. Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.

B. Supervised learning can work without any labeled data.

C. Supervised learning is more effective for discovering hidden patterns in data without prior labeling.

D. Supervised learning algorithms can automatically label data.

Discussion 0

Correct Answer: A Vote an answer

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