IBM AI Enterprise Workflow V1 Data Science Specialist C1000-059 questions are available, which are the best material for you to study this IBM certification C1000-059 exam. We know that IBM C1000-059 exam is a required test for IBM Certified Specialist-AI Enterprise Workflow V1 certification, which 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. To pass IBM Certification C1000-059 exam easily, IBM AI Enterprise Workflow V1 Data Science Specialist C1000-059 questions are the best material for you to prepare the test well.
IBM C1000-059 Exam
Study the following IBM certification C1000-059 exam information to study the test well.
Number of questions: 62
Number of questions to pass: 44
Time allowed: 90 mins
Available Language: English
C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist Topics
C1000-059 IBM AI Enterprise Workflow V1 Data Science Specialist exam topics cover the following details.
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
Share IBM Certification C1000-059 Real Exam Questions
The new cracked IBM certification C1000-059 real exam questions can help you test all the above topics. Share some IBM AI Enterprise Workflow V1 Data Science Specialist C1000-059 real exam questions and answers below.
1.What is the goal of the backpropagation algorithm?
A. to randomize the trajectory of the neural network parameters during training
B. to smooth the gradient of the loss function in order to avoid getting trapped in small local minimas
C. to scale the gradient descent step in proportion to the gradient magnitude
D. to compute the gradient of the loss function with respect to the neural network parameters
Answer: B
2.With the help of AI algorithms, which type of analytics can help organizations make decisions based on facts and probability-weighted projections?
A. prescriptive analytics
B. cognitive analytics
C. predictive analytics
D. descriptive analytics
Answer: A
3.What is the technique called for vectorizing text data which matches the words in different sentences to determine if the sentences are similar?
A. Cup of Vectors
B. Box of Lexicon
C. Sack of Sentences
D. Bag of Words
Answer: D
4.Which statement is true in the context of evaluating metrics for machine learning algorithms?
A. A random classifier has AUC (the area under ROC curve) of 0.5
B. Using only one evaluation metric is sufficient
C. The F-score is always equal to precision
D. Recall of 1 (100%) is always a good result
Answer: B
5.Which situation would disqualify a machine learning system from being used for a particular use case?
A. The use case requires a 100% likelihood of making a correct/true prediction.
B. Data for the machine learning model is available only as static CSV files.
C. The neural network for the model requires significantly more computing power than a logistic regression model.
D. Training and testing data for the model contain outliers.
Answer: C
