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IBM C1000-154 exam consists of multiple-choice questions and is designed to be completed within a specific time frame. Candidates are required to score a certain percentage to pass the exam. C1000-154 exam is challenging, but with the right preparation and practice, candidates can pass the exam and earn their IBM Watson Data Scientist v1 certification.

IBM C1000-154 certification exam is designed for data scientists who are looking to validate their skills and knowledge in working with IBM Watson technologies. C1000-154 Exam is designed to test the candidate's ability to design, develop, and deploy data-driven solutions using IBM Watson tools and technologies. The IBM Watson Data Scientist v1 certification is an excellent way to demonstrate your expertise in this field and differentiate yourself from other professionals in the industry.

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IBM C1000-154 Exam consists of 60 multiple-choice questions that must be answered within a time limit of 90 minutes. The questions are designed to test the candidate's knowledge and understanding of various data science concepts and technologies. To pass the exam, candidates must achieve a minimum score of 65%.

IBM Watson Data Scientist v1 Sample Questions (Q25-Q30):

NEW QUESTION # 25
When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?

A. The popularity of the algorithm in recent academic papers.
B. The algorithm that requires the least amount of data preprocessing.
C. Choosing algorithms that are only based on supervised learning.
D. Compatibility of the algorithm with the data characteristics and the predictive task.

Answer: D

NEW QUESTION # 26
Which of the following is a common issue identified during the preprocessing of data?

A. Presence of missing values
B. Overly detailed documentation
C. Aesthetically unpleasing charts
D. Excessively large file names

Answer: A

NEW QUESTION # 27
The first step in performing exploratory data analysis (EDA) typically involves:

A. Choosing a color palette for data visualization
B. Determining the hypothesis for the analysis
C. Selecting a random sample of data to analyze
D. Connecting to as many data sources as possible

Answer: B

NEW QUESTION # 28
In the context of model selection, explainability refers to:

A. How colorful and visually appealing the model's output is.
B. The complexity of the algorithm used to build the model.
C. The ease with which humans can understand how the model makes decisions.
D. The model's ability to operate without any data.

Answer: C

NEW QUESTION # 29
Given the Confusion matrix below, which is the formula for specificity?

A. TN/(TN + FP)
B. (TP + TN)/(FN + FP + TN + TP)
C. TP/(FN + TP)
D. TP/(FP + TP)

Answer: A

NEW QUESTION # 30
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New C1000-154 Real Exam & C1000-154 Valid Exam Sims

Pass Guaranteed Quiz Efficient C1000-154 - New IBM Watson Data Scientist v1 Real Exam

IBM Watson Data Scientist v1 Sample Questions (Q25-Q30):

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