IBM C1000-059 Actual Free Exam Questions & Community Discussion

Exam Code/Number: C1000-059
Exam Name/Title: IBM AI Enterprise Workflow V1 Data Science Specialist
Certification Provider: IBM
Corresponding Certification: IBM Data and AI: Data and AI

Exam Questions: 64
Updated On: May 28, 2026

Question #15

What is the main difference between traditional programming and machine learning?

A. Machine learning takes full advantage of SDKs and APIs.

B. Machine learning is optimized to run on parallel computing and cloud computing.

C. Machine learning does not require explicit coding of decision logic.

D. Machine learning models take less time to train.

Discussion 0

Correct Answer: C Vote an answer

Question #16

The least squares optimization technique (The Method of Least Squares) is used in which algorithm?

A. Logistic regression

B. Support Vector Machines

C. Naive Bayes classification

D. Linear regression

Discussion 0

Correct Answer: D Vote an answer

Question #17

When communicating technical results to business stakeholders, what are three appropriate topics to include?
(Choose three.)

A. realistic impact on the business measures

B. methods that failed

C. alternative methods to address the business problem

D. benefits of cognitive over business analytics

E. newest developments in AI methods

F. differences between cloud provider portfolios

Discussion 0

Correct Answer: A,C,D Vote an answer

Question #18

Given two multidimensional arrays of the same data type, A and B which two Python NumPy statements give the matrix product of the two matrices? (Choose two.)

A. np.dot(A,B)

B. A @ B

C. np.matprod(A,B)

D. A × B

E. A * B

Discussion 0

Correct Answer: C,E Vote an answer

Question #19

What are two methods used to detect outliers in structured data? (Choose two.)

A. gradient descent

B. Word2Vec

C. isolation forest

D. multi-label classification

E. one class Support Vector Machine (SVM)

Discussion 0

Correct Answer: C,E Vote an answer

Question #20

Which statement is true for naive Bayes?

A. Naive Bayes doesn't require any assumptions about the distribution of values associated with each class.

B. Let p(C1 | x) and p(C2 | x) be the conditional probabilities that x belongs to class C1 and C2 respectively, in a binary model, log p (C1 | x) - log p(C2 | x) > 0 results in predicting that x belongs to C2.

C. Naive Bayes can be used for regression.

D. Naive Bayes is a conditional probability model.

Discussion 0

Correct Answer: D Vote an answer

Question #21

Which statement is true in the context of evaluating metrics for machine learning algorithms?

A. Recall of 1 (100%) is always a good result

B. The F-score is always equal to precision

C. A random classifier has AUC (the area under ROC curve) of 0.5

D. Using only one evaluation metric is sufficient

Discussion 0

Correct Answer: D Vote an answer

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