[Nov-2021] C1000-059 Braindumps – C1000-059 Questions to Get Better Grades
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NEW QUESTION 28
Which statement is true for naive Bayes?
- A. Let p(C1 | x) and p(C2 | x) be the conditional probabilities that x belongs to class C1 and C2 respectively, in a binary model, log p (C1 | x) - log p(C2 | x) > 0 results in predicting that x belongs to C2.
- B. Naive Bayes can be used for regression.
- C. Naive Bayes doesn't require any assumptions about the distribution of values associated with each class.
- D. Naive Bayes is a conditional probability model.
Answer: D
NEW QUESTION 29
In machine vision, the algorithm for detecting objects or features in an image based on a target pattern is known as?
- A. OCR
- B. Fourier transform
- C. Hough transformation
- D. normalized correlation
Answer: D
NEW QUESTION 30
Which IBM Watson Machine Learning deployment method offers the ultimate flexibility in deploying a machine learning model?
- A. Watson Machine Learning FORTRAN client
- B. Watson Machine Learning REST API
- C. Watson Studio Project
- D. Watson Machine Learning Python client
Answer: B
NEW QUESTION 31
What is the primary role of a data steward?
- A. they have a strong understanding of the enterprise's database architecture
- B. they are a "blue sky thinker" who comes up with new approaches to use new data in innovative ways
- C. the one who collects, processes, and performs statistical analysis on data
- D. they define data processes to meet compliance and regulatory obligations
Answer: C
NEW QUESTION 32
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. user summary report
- D. My Sticky Note
Answer: A
NEW QUESTION 33
The least squares optimization technique (The Method of Least Squares) is used in which algorithm?
- A. Logistic regression
- B. Linear regression
- C. Naive Bayes classification
- D. Support Vector Machines
Answer: B
NEW QUESTION 34
What are three elements that are typically part of a machine learning pipeline in scikit-learn or pyspark?
(Choose three.)
- A. business understanding
- B. model prediction
- C. model building
- D. data preprocessing
- E. data exploration
- F. use case selection
Answer: B,D,E
NEW QUESTION 35
Which two statements are correct about deploying machine learning models? (Choose two.)
- A. It is only possible on the cloud because they require a large amount of compute resources.
- B. It is a necessary step in training and evaluating the performance of the models.
- C. It makes it possible to create reports for management dynamically using specific parameters from executives.
- D. It is critical for achieving high accuracy in training.
- E. It allows integration within business applications.
Answer: B,D
NEW QUESTION 36
Which fine-tuning technique does not optimize the hyperparameters of a machine learning model?
- A. random search
- B. grid search
- C. population based training
- D. hyperband
Answer: D
NEW QUESTION 37
Which distance is applied for multivariate outlier detection?
- A. Manhattan distance
- B. Euclidean distance
- C. Minkowski distance
- D. Mahalanobis distance
Answer: D
NEW QUESTION 38
What is meant by the curse of dimensionality?
- A. The data sparsity becomes more severe as the number of features is increased.
- B. The data sparsity becomes more severe as the number of samples is increased.
- C. The number of available algorithms for a given task is high.
- D. The number of available data sources for a given task is high.
Answer: D
NEW QUESTION 39
When should median value be used instead of mean value for imputing missing data?
- A. for real numbers
- B. for large data sets
- C. for skewed data
- D. for normally distributed data
Answer: B
NEW QUESTION 40
Which two properties hold true for standardized variables (also known as z-score normalization)? (Choose two.)
- A. standard deviation = 0.5
- B. expected value = 0.5
- C. expected value = 0
- D. expected value = 1
- E. standard deviation = 1
Answer: B,E
NEW QUESTION 41
What are two methods used to detect outliers in structured data? (Choose two.)
- A. isolation forest
- B. one class Support Vector Machine (SVM)
- C. multi-label classification
- D. Word2Vec
- E. gradient descent
Answer: A,B
NEW QUESTION 42
What are the various components that make up a time series data?
- A. trend, noise, covariance
- B. trend, noise, kurtosis
- C. trend, seasonality, causation
- D. trend, seasonality, noise
Answer: D
NEW QUESTION 43
With only limited labeled data available how might a neural network use case be realized?
- A. by creating random data
- B. by using a customized pre-trained model
- C. by increasing the depth of the neural network
- D. by assigning random labels
Answer: B
NEW QUESTION 44
Which situation would disqualify a machine learning system from being used for a particular use case?
- A. Training and testing data for the model contain outliers.
- B. The use case requires a 100% likelihood of making a correct/true prediction.
- C. Data for the machine learning model is available only as static CSV files.
- D. The neural network for the model requires significantly more computing power than a logistic regression model.
Answer: D
NEW QUESTION 45
What are three operators used by genetic programming? (Choose three.)
- A. crossover
- B. duel
- C. reciprocation
- D. mutation
- E. selection
- F. sheltering
Answer: A,B,E
NEW QUESTION 46
What is an example of a supervised machine learning algorithm that can be applied to a continuous numeric response variable?
- A. local outlier factor (LOF)
- B. linear regression
- C. k-means
- D. naive Bayes
Answer: B
NEW QUESTION 47
What are two hyperparameters used when building a k-means model? (Choose two.)
- A. number of neighbors
- B. number of clusters
- C. learning rate
- D. number of iterations
- E. kernel
Answer: B,D
NEW QUESTION 48
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 accepting a null hypothesis when the hypothesis is proven false.
- C. It is the probability of rejecting a null hypothesis when the hypothesis is proven true.
- D. It is the probability of rejecting a null hypothesis when the hypothesis is proven false.
Answer: B
NEW QUESTION 49
What is the best step by step order for machine learning pipeline?
Answer:
Explanation:
NEW QUESTION 50
A classification task has examples that are labeled as belonging to one of two classes:
*90% of the examples belong to class-1
*10% belong to class-2
Which two techniques are appropriate to deal with the class imbalance? (Choose two.)
- A. after training, divide the model accuracy of each class by the proportion that they represent in the dataset
- B. oversample the minority class and/or undersample the majority class
- C. lower the detection threshold of the minority class after training
- D. impose an additional cost on the model for making classification mistakes on the minority class during training
- E. apply dimensionality reduction to the features before training
Answer: A,D
NEW QUESTION 51
What is the meaning of "deep" in deep learning?
- A. It indicates the many layers contributing to a model of the data.
- B. To go deep into the loss function landscape.
- C. The higher the number of machine learning algorithms that can be applied, the deeper is the learning.
- D. A kind of deeper understanding achieved by any approach taken.
Answer: A
NEW QUESTION 52
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