IBM c1000-059 practice test

IBM AI Enterprise Workflow V1 Data Science Specialist Exam

Last exam update: Jun 17 ,2024
Page 1 out of 5. Viewing questions 1-15 out of 62

Question 1

Which is the most important thing to ensure while collecting data?

  • A. samples collected are skewed with each other
  • B. samples collected are all strongly correlated with each other
  • C. samples collected adequately cover the space of all possible scenarios
  • D. samples collected focus only on the most common cases
Mark Question:
Answer:

A

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Question 2

What is the meaning of "deep" in deep learning?

  • A. To go deep into the loss function landscape.
  • B. The higher the number of machine learning algorithms that can be applied, the deeper is the learning.
  • C. A kind of deeper understanding achieved by any approach taken.
  • D. It indicates the many layers contributing to a model of the data.
Mark Question:
Answer:

D

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Reference: https://en.wikipedia.org/wiki/Deep_learning

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Question 3

Which algorithm is best suited if a client needs full explainability of the machine learning model?

  • A. decision tree
  • B. logistic regression
  • C. support vector machine (SVM)
  • D. recurrent neural network
Mark Question:
Answer:

A

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Question 4

Given the following sentence:
The dog jumps over a fence.
What would a vectorized version after common English stopword removal look like?

  • A. ['dog', 'fence', 'run']
  • B. ['fence', 'jumps']
  • C. ['dog', 'fence', 'jumps']
  • D. ['a', 'dog', 'fence', 'jumps', 'over', 'the']
Mark Question:
Answer:

C

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Reference:
https://towardsdatascience.com/text-pre-processing-stop-words-removal-using-
different-libraries- f20bac19929a

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Question 5

Which statement defines p-value?

  • A. It is the probability of accepting a null hypothesis when the hypothesis is proven true.
  • B. It is the probability of rejecting a null hypothesis when the hypothesis is proven false.
  • C. It is the probability of accepting a null hypothesis when the hypothesis is proven false.
  • D. It is the probability of rejecting a null hypothesis when the hypothesis is proven true.
Mark Question:
Answer:

C

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Reference: https://courses.lumenlearning.com/wmopen-concepts-statistics/chapter/introduction-to-
hypothesis- testing-5-of-5/

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Question 6

What is the primary role of a data steward?

  • A. they are a "blue sky thinker" who comes up with new approaches to use new data in innovative ways
  • B. they have a strong understanding of the enterprise's database architecture
  • C. they define data processes to meet compliance and regulatory obligations
  • D. the one who collects, processes, and performs statistical analysis on data
Mark Question:
Answer:

D

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Reference: https://analyticsindiamag.com/data-steward-roles-responsibilities/

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Question 7

Which is an example of a nominal scale data?

  • A. a variable industry with categorical values such as financial, engineering, and retail
  • B. a variable mood with a scale of values unhappy, ok, and happy
  • C. a variable bank account balance whose possible values are $5, $10, and $15
  • D. a variable temperature with a scale of values low, medium, and high
Mark Question:
Answer:

C

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Question 8

A data scientist is exploring transaction data from a chain of stores with several locations. The data
includes store number, date of sale, and purchase amount.
If the data scientist wants to compare total monthly sales between stores, which two options would
be good ways to aggregate the data? (Choose two.)

  • A. Find the sum of the transaction prices
  • B. Select the largest transaction amount by month and store
  • C. Write a GROUP BY query
  • D. Plot a time series plot of transaction amounts
  • E. Generate a pivot table
Mark Question:
Answer:

BD

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Question 9

A data analyst creates a term-document matrix for the following sentence: I saw a cat, a dog and
another cat.
Assuming they used a binary vectorizer, what is the resulting weight for the word cat?

  • A. 0
  • B. 1
  • C. 3
  • D. 2
Mark Question:
Answer:

B

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Question 10

In a hyperparameter search, whether a single model is trained or a lot of models are trained in
parallel is largely determined by?

  • A. The number of hyperparameters you have to tune.
  • B. The presence of local minima in your neural network.
  • C. The amount of computational power you can access.
  • D. Whether you use batch or mini-batch optimization.
Mark Question:
Answer:

C

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Reference:
https://github.com/Kulbear/deep-learning-
coursera/blob/master/Improving%20Deep%20Neural%
20Networks%20Hyperparameter%20tuning%2C%20Regularization%20and%20Optimization/Week%
203%
20Quiz%20-
%20Hyperparameter%20tuning%2C%20Batch%20Normalization%2C%20Programming%
20Frameworks.md

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Question 11

If the distribution of the height of American men is approximately normal, with a mean of 69 inches
and a standard deviation of 2.5 inches, then roughly 68 percent of American men have heights
between
and
.

  • A. 64 inches and 74 inches
  • B. 66.5 inches and 69 inches
  • C. 71.5 inches and 76.5 inches
  • D. 66.5 inches and 71.5 inches
Mark Question:
Answer:

B

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Question 12

Which two properties hold true for standardized variables (also known as z-score normalization)?
(Choose two.)
A. standard deviation = 0.5
B. expected value = 0
C. expected value = 0.5
D. expected value = 1
E. standard deviation = 1

Mark Question:
Answer:

CE
(none)
Explanation

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Question 13

What is the main difference between traditional programming and machine learning?

  • A. Machine learning models take less time to train.
  • B. Machine learning takes full advantage of SDKs and APIs.
  • C. Machine learning is optimized to run on parallel computing and cloud computing.
  • D. Machine learning does not require explicit coding of decision logic.
Mark Question:
Answer:

D

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Question 14

What is the name of the design thinking work product that contains a summary description of a
particular person or role?

  • A. persona
  • B. snapshot
  • C. My Sticky Note
  • D. user summary report
Mark Question:
Answer:

A

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Reference: https://www.interaction-design.org/literature/topics/design-thinking

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Question 15

What are two methods used to detect outliers in structured data? (Choose two.)

  • A. multi-label classification
  • B. isolation forest
  • C. gradient descent
  • D. one class Support Vector Machine (SVM)
  • E. Word2Vec
Mark Question:
Answer:

BD

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Reference:
https://www.researchgate.net/post/What-is-the-best-outliers-detection-algorithm-to-
used-for-big- data

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