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IBM C1000-059 Exam Summary:-
- Exam Name: IBM Certified Specialist – AI Enterprise Workflow V1
- Exam Code: C1000-059
- Exam Price: $200 (USD)
- Duration: 90 mins
- Number of Questions: 62
- Passing Score: 44 / 62
IBM C1000-059 Exam Topics:-
01. Scientific, Mathematical, and technical essentials for Data Science and AI
02. Applications of Data Science and AI in Business
03. Data understanding techniques in Data Science and AI
04. Data preparation techniques in Data Science and AI
05. Application of Data Science and AI techniques and models
06. Evaluation of AI models
07. Deployment of AI models
08. Technology Stack for Data Science and AI
Improve Your Knowledge-Base:
Get access to some of the sample C1000-059 practice test questions to boost your knowledge base. Preparation is a continuous process for some time, and you must always try to keep improving via learning. Access the questions from anywhere and from any device. The easy availability of the questions would help you to get better with the C1000-059 exam topics. You can solve these questions easily or learn new things from them.
Here Are Some Useful Questions:
01. A client requests a general artificial intelligence (AI) tool that they can plug into their data warehouse. What is the best response to this request?
a) There is no general AI tool currently that works universally.
b) Apply neural networks to your data.
c) IBM Watson is the tool you are looking for.
d) AI can create value without any human-intervention.
Answer: a
02. What are two common ways to handle missing values when cleaning data?
a) delete records
b) replace with ‘1’
c) replace with mean
d) replace with ‘100’
e) replace with standard deviation
Answer: a, c
03. To reduce the overall time to complete a data ingestion job, what two actions should be taken?
a) Assemble the data pipeline into a series of immutable transformations, which can be combined after the processing.
b) Partition the data within each pipeline to take advantage of parallel processing (multiple server cores, processors, etc.).
c) Look for outliers in the data, missing values, and skewness of the data.
d) Build a dedicated pipeline for each dataset to ensure that all of them can be processed independently and concurrently.
e) Apply a chi-squared statistical test to rank the impact of each feature on the concept label and discard the less impactful features before model training.
Answer: b, d
04. What is used to update coefficients in logistic regression?
a) number of features
b) gradient descent
c) slope
d) kernel
Answer: b
05. What are three hyperparameters that are used when building a simple decision tree model?
a) kernel
b) learning rate
c) maximum depth
d) split criterion
e) number of nearest neighbors
f) minimum number of samples in a leaf node
Answer: c, d, f
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