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C1000-059 Exam Overview - IBM AI Enterprise Workflow V1 Data Science Specialist

A Data Scientist Specialist is skilled in the use of IBM methods and technologies in solving business problems with machine learning solutions, within the design thinking lens and methodology. This includes the ability to connect machine learning solutions to enterprise requirements and business priorities and applying an understanding when implementing an enterprise AI workflow.

There are 62 questions covered in the real C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist exam, and you need to answer 44 questions correctly in 90 minutes to pass your exam,the language for this IBM C1000-059 exam is only English, passing this exam will help you achieve IBM Certified Specialist - AI Enterprise Workflow V1 certification.

C1000-059 Exam Sections

Section 1: Scientific, Mathematical, and technical essentials for Data Science and AI
Section 2: Applications of Data Science and AI in Business
Section 3: Data understanding techniques in Data Science and AI
Section 4: Data preparation techniques in Data Science and AI
Section 5: Application of Data Science and AI techniques and models
Section 6: Evaluation of AI models
Section 7: Deployment of AI models
Section 8: Technology Stack for Data Science and AI

View Online IBM AI Enterprise Workflow V1 Data Science Specialist C1000-059 Free Questions

What is meant by the curse of dimensionality?
A.The number of available algorithms for a given task is high.
B.The number of available data sources for a given task is high.
C.The data sparsity becomes more severe as the number of features is increased.
D.The data sparsity becomes more severe as the number of samples is increased.
Answer : B
 
The least squares optimization technique (The Method of Least Squares) is used in which algorithm?
A.Support Vector Machines
B.Naive Bayes classification
C.Logistic regression
D.Linear regression
Answer : D
 
What are three elements that are typically part of a machine learning pipeline in scikit-learn or pyspark? (Choose three.)
A.model building
B.data preprocessing
C.model prediction
D.business understanding
E.use case selection
F.data exploration
Answer : B, C, F

Which statement is true for naive Bayes?
A.Naive Bayes can be used for regression.
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 is a conditional probability model.
D.Naive Bayes doesn't require any assumptions about the distribution of values associated with each class.
Answer : C