IBM C1000-012 Online Practice Questions and Exam Preparation

IBM C1000-012 Online Questions & Answers

• Question 1:

  How is training data set constructed from user questions for the Natural Language Classifier?

  A. Use an XML formatted data set
  B. Upload documents with similar questions
  C. Group the questions and label as classes
  D. Create and intents index in a spreadsheet
  C. Group the questions and label as classes

• Question 2:

  Which IBM Watson Natural Language Understanding service APIs would be best suited for identifying the names of persons or organization in unstructured text documents?

  A. Keywords
  B. Taxonomy
  C. Concepts
  D. Entities
  D. Entities

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  Explanation Explanation/Reference:https://cloud.ibm.com/apidocs/natural-language-understanding

• Question 3:

  If you are looking to translate the language of text, but are uncertain of the original language which REST API endpoint from IBM Watson Language Translator service could be used?

  A. classify
  B. models
  C. identify
  D. translate
  B. models

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  Explanation Explanation/Reference:https://cloud.ibm.com/apidocs/language-translator

• Question 4:

  How can the threshold for the confidence level be set for the intent of a dialog node in the IBM Watson Assistant service?

  A. The confidence level cannot be set within the Dialog and should be done programmatically within the application code.
  B. On the drop-down of the Dialog node, the condition for confidence levels can be set for the intents defined in your Dialog node.
  C. The confidence level threshold is already set for each Dialog node at "0.2". It can be overridden by turning off the confidence level on the improve tab within the Watson Assistant workspace.
  D. It can be a condition within the Dialog node alongside the intent. For Example, intents [0].confidence
  D. It can be a condition within the Dialog node alongside the intent. For Example, intents [0].confidence

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  Reference https://developer.ibm.com/answers/questions/298862/accessing-the-intent-confidence-level-for-the-wats/

• Question 5:

  In order to minimize the error, which method helps to find the derivative of the error with respect to each weight and subtracting this value from the weight value?

  A. Answer Propagation
  B. Back Propagation
  C. Cognitive Propagation
  D. Forward Propagation
  B. Back Propagation

• Question 6:

  An AI FAQ widget on a company's support portal allows users to ask questions related to the company and its products.

  Why is it important to analyze the user's input for entities?

  A. Specific content such as the location (Colorado) could be used to further inform the program.
  B. There must be an intent for each and every entity detected by the program.
  C. The number of entities found in the utterance could improve the accuracy of the answer.
  D. They could indicate variations in the flow of the dialog node.
  A. Specific content such as the location (Colorado) could be used to further inform the program.

• Question 7:

  A journalist would like to analyze four short sentences captured and transcribed form verbal sound-bites from a political candidate. Is the IBM Watson Personality Insights service an appropriate technology to accomplish this goal?

  A. Yes, the Personality Insights services will provide details about the personality of this political candidate
  B. Yes, because the journalist will combine the Personality Insight with Speech to Test service to accomplish this goal
  C. No, there is not enough text for the Personality Insights service to analyze and provide reliable results
  D. No, the Personality Insights service provides only information about the emotional stat of the candidate
  C. No, there is not enough text for the Personality Insights service to analyze and provide reliable results

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  https://developer.ibm.com/watson/blog/2016/01/12/personality-meets-behavior-the-new-ibm-watson-personality-insights-demo/

• Question 8:

  An AI solution is implemented to detect what the user wants to do. The user mentions that he wants to buy a ski jacket for an upcoming vacation in Colorado. How can the entities be derived from the conversation using IBM Watson services?

  A. Upload training data to the Discovery service and use the Discovery API to query the results
  B. Use the Tone Analyzer service to retrieve the level of openness in the utterance
  C. Pass the utterance from the user to the Natural Language Understanding service Entities API
  D. Pass the utterance from the user through Speech to Text then apply Tradeoff Analytics
  C. Pass the utterance from the user to the Natural Language Understanding service Entities API

• Question 9:

  How would a developer build a AI system that recognize license plates in images?

  A. Train a classifier with examples of license plates.
  B. Write an algorithm to run a set of logical checks on the images.
  C. An AI system would already recognize different types of license plates, so simply show it the images.
  D. Build a tool that sends images to be annotated by teams of humans.
  A. Train a classifier with examples of license plates.

• Question 10:

  Which type of learning is K-means clustering?

  A. automatic learning
  B. supervised learning
  C. unsupervised learning
  D. reinforcement learning
  C. unsupervised learning

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  https://dataplatform.cloud.ibm.com/docs/content/wsj/spss-visualization/spss-viz-k-means-overview.html