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

It is your chance to pass the IBM AI Enterprise Workflow V1 Data Science Specialist exam and become a IBM Data and AI: Data and AI certified. After preparing for the C1000-059 exam with PassQuestion C1000-059 Exam Questions, you are fully ready to take the IBM C1000-056 exam with confidence. You will surely pass your IBM AI Enterprise Workflow V1 Data Science Specialist C1000-059 exam with excellent marks in your first attempt only.

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1. A new test to diagnose a disease is evaluated on 1152 people, and 106 people have the disease, and 1046 people do not have the disease.

The test results are summarized below:

In this sample, how many cases are false positives and false negatives?


2. What is the goal of the backpropagation algorithm?


3. With the help of AI algorithms, which type of analytics can help organizations make decisions based on facts and probability-weighted projections?


4. What is the technique called for vectorizing text data which matches the words in different sentences to determine if the sentences are similar?


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


6. When should median value be used instead of mean value for imputing missing data?


7. Given the following matrix multiplication:

What is the value of P?


8. A neural network is composed of a first affine transformation (affine1) followed by a ReLU non-linearity, followed by a second affine transformation (affine2).

Which two explicit functions are implemented by this neural network? (Choose two.)


9. The formula for recall is given by (True Positives) / (True Positives + False Negatives).

What is the recall for this example?


10. After importing a Jupyter notebook and CSV data file into IBM Watson Studio in the IBM Public Cloud project, it is discovered that the notebook code can no longer access the CSV file.

What is the most likely reason for this problem?


11. Determine the number of bigrams and trigrams in the sentence.

"Data is the new oil".


12. Which is a preferred approach for simplifying the data transformation steps in machine learning model management and maintenance?


13. Which is a technique that automates the handling of categorical variables?


14. Which two statements are correct about deploying machine learning models? (Choose two.)


15. Which of the following entity extraction techniques would be best for the extraction of telephone numbers from a text document?


16. What statement is true about UTF-8?


17. Which test is applied to determine the relationship between two categorical variables?


18. With only limited labeled data available how might a neural network use case be realized?


19. What is the first step in creating a custom model in Watson Visual Recognition service?


20. What is used to scale large positive values during data cleaning?


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