# Latest Lead4pass C1000-059 Dumps For IBM C1000-059 Training Materials Updated Jan 2023

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## Free C1000-059 Dumps With Exam Questions Training Materials

Free sample questions of C1000-059 free dumps are provided here.

##### Question 1:

A new test to diagnose a disease is evaluated on 1152 people, 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?

A. 33 false positives and 81 false negatives

B. 81 false positives and 73 false negatives

C. 73 false positives and 81 false negatives

D. 81 false positives and 33 false negatives

##### Question 2:

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

Reference: https://www.sciencedirect.com/topics/computer-science/backpropagation

##### Question 3:

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

Reference: https://www.investopedia.com/terms/p/prescriptive-analytics.asp

##### Question 4:

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

##### Question 5:

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

A. A random classifier has an AUC (the area under the ROC curve) of 0.5

B. Using only one evaluation metric is sufficient

C. The F-score is always equal to the precision

D. Recall of 1 (100%) is always a good result

##### Question 6:

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

A. for skewed data

B. for real numbers

C. for normally distributed data

D. for large data sets

##### Question 7:

Given the following matrix multiplication: What is the value of P?

A.?

B. 17

C. 12

D.?

Reference: https://www.mathsisfun.com/algebra/matrix-multiplying.html

##### Question 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.)

A. y = affine1(ReLU(affine2(x)))

B. y = max(affine1(x), affine2(x))

C. y = affine2(ReLU(affine1(x)))

D. y = affine2(max(affine1(x), 0))

E. y = ReLU(affine1(x), affine2(x))

##### Question 9:

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

What is the recall for this example? A. 0.2

B. 0.25

C. 0.5

D. 0.33

Reference: https://machinelearningmastery.com/precision-recall-and-f-measure-for-imbalanced- classification/

##### Question 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?

A. CSV files cannot be used as data sources in Watson Studio.

B. The CSV file was converted to a binary blob and must be converted in the notebook code.

C. The CSV file is stored in a Cloud Object Storage.

D. The CSV file is stored in a Watson Machine Learning instance and is only accessible via REST API.

##### Question 11:

Determine the number of bigrams and trigrams in the sentence. “Data is the new oil”.

A. 3 bigrams, 3 trigrams

B. 4 bigrams, 4 trigrams

C. 3 bigrams, 4 trigrams

D. 4 bigrams, 3 trigrams

##### Question 12:

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

A. Implement data transformation, feature extraction, feature engineering, and imputation algorithms in one single pipeline.

B. Do not apply any data transformation or feature extraction or feature engineering steps.

C. Leverage only deep learning algorithms.

D. Apply a limited number of data transformation steps from a pre-defined catalog of possible operations independent of the machine learning use case.

##### Question 13:

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

A. binary encoding

B. decoding

C. autoencoding

D. one-hot encoding

Reference: https://hub.packtpub.com/how-to-handle-categorical-data-for-machine-learning-algorithms/

##### Question 14:

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

A. complex pattern-based

B. regex

C. statistical

D. dictionary

Reference: https://www.researchgate.net/ publication/318093829_Developing_an_innovative_entity_extraction_method_for_unstructured_data

##### Question 15:

What statement is true about UTF-8?

A. It is encoding for Latin script.

B. It is rarely used today.

C. It is encoding for Unicode characters.

D. It is equal to ASCII.