1. What is MLOps?
a. An abbreviation for "Machine Learning Operations"
b. An abbreviation for "Model Learning Operations"
c. An abbreviation for "Managed Learning Operations"
d. An abbreviation for "Machine Language Operations"
Answer: a. An abbreviation for "Machine Learning Operations"
2. What is the main goal of MLOps?
a. To streamline the deployment and management of machine learning models
b. To improve the accuracy of machine learning models
c. To increase the speed of machine learning development
d. All of the above
Answer: a. To streamline the deployment and management of machine learning models
3. Which of the following is NOT a component of MLOps?
a. Data governance
b. Continuous integration/continuous deployment (CI/CD)
c. Model training
d. Data visualization
Answer: d. Data visualization
4. What is the purpose of data preparation in MLOps?
a. To collect and store data for training and testing machine learning models
b. To clean and preprocess data for training and testing machine learning models
c. To evaluate the performance of machine learning models
d. To visualize data for stakeholders
Answer: b. To clean and preprocess data for training and testing machine learning models
5. What is the role of version control in MLOps?
a. To track changes to machine learning models and data
b. To automate the deployment of machine learning models
c. To manage access to machine learning models and data
d. To optimize machine learning algorithms
Answer: a. To track changes to machine learning models and data
6. Which of the following is NOT a challenge in implementing MLOps?
a. Limited availability of machine learning libraries
b. Lack of skilled resources
c. Data privacy and security concerns
d. Difficulty in version controlling machine learning models
Answer: a. Limited availability of machine learning libraries
7. What is the purpose of hyperparameter tuning in MLOps?
a. To select the optimal machine learning algorithm for a given problem
b. To optimize the performance of machine learning models by adjusting their parameters
c. To analyze the results of machine learning models
d. To visualize the data used for training and testing machine learning models
Answer: b. To optimize the performance of machine learning models by adjusting their parameters
8. What is the role of A/B testing in MLOps?
a. To compare the performance of different machine learning models
b. To evaluate the impact of machine learning models on user behavior
c. To optimize the deployment of machine learning models
d. All of the above
Answer: d. All of the above
9. Which of the following is an example of a deployment environment for machine learning models?
a. Cloud
b. On-premise data center
c. Edge devices
d. All of the above
Answer: d. All of the above
10. What is the purpose of monitoring and logging in MLOps?
a. To track the performance of machine learning models in production
b. To optimize the deployment of machine learning models
c. To automate the testing of machine learning models
d. To visualize the data used for training and testing machine learning models
Answer: a. To track the performance of machine learning models in production
11. What is the role of explainability in MLOps?
a. To increase the transparency of machine learning models
b. To optimize the deployment of machine learning models
c. To improve the accuracy of machine learning models
d. To visualize the data used for training and testing machine learning models
Answer: a. To increase the transparency of machine learning models
12. Among the following option identify the one which is not a type of learning
a) Semi unsupervised learning
b) Reinforcement learning
c) supervised learning
d) unsupervised learning
Answer: a
13. Identify the kind of learning algorithm for “facial identities for facial expressions”.
a) prediction
b) Recognizing anomalies
c) Prediction
d) Recognition patterns
Answer: d
14. What is the application of machine learning methods to a large database called?
a) Data mining
b) Artificial intelligence
c) internet of things
d) Big data computing
Answer: a
15. Choose a disadvantage of decision trees among the following.
a) Decision trees are robust to outliers
b) Factor analysis
c) Decision trees are prone to overfit
d) All of above
Answer: c
16. Machine learning is a subset of which of the following.
a) Deep learning
b) Data learning
c) Artificial intelligence
d) None of the Above
Answer: c
17. The father of machine learning is _____________
a) Geoffrey Everest Hinton
b) Geoffrey Hill
c) Geoffrey chaucer
d) None of the Above
Answer: a
18.The most significant phase in genetic algorithm is _________
a) Crossover
b) Fitness Function
c) Selection
d) Mutation
Answer: a
19.FIND-S algorithm ignores?
a) Negative
b) Positive
c) Both
d) None
Answer: a
20. Choose whether the following statement is true or false: The backpropagation law is also known as the generalized Delta rule.
a) True
b) False
Answer: a
21.Full form of PAC is _____________
a) Probably Approx Cost
b) Probability Approx Communication
c) Probably Approximate Correct
d) Probably Approximate Computation
Answer: c
22.Which of the following is not machine learning?
a) Artificial Intelligence
b) Rule-based inference
c) Both A&B
d) None of the Above
Answer: B
23.Among the following option identify the one which is used to create the most common graph types.
a) quickplot
b) qplot
c) plot
d) All of the above
Answer: B
24.Which of the following is not a supervised learning
a) Naive Bayesian
b) PCA
c) Linear Regression
d) Decision Tree
Answer: B
25.The total types of the layer in radial basis function neural networks is ______
a) 1
b) 2
c) 3
d) 4
Answer: c
26.Which of the following is an application of CBR?
a) Diagnosis
b) Planning
c) Design
d) All of the above
Answer: c
27.Choose the correct advantages of CBR.
a)Fast to train
b) A local Approx is found for each test case
c) Knowledge is in a form understandable by human
d) All of the above
Answer: d
28.Machine learning as various Search and Optimisation algorithms. Identify among the following which is not evolutionary computation.
a) Perceptron
b) Neuroevolution
c) Genetic Programming
d) Genetic Algorithm
Answer: a
29.What does K stand for in K mean algorithm?
a) Number of attributes
b) Number of Clusters
c) Number of Data
d) Number of iterations
Answer: d
30.Identify the method which is used for trainControl resampling.
a) Bag32
b) repeatedcv
c) svm
d) None of the Above
Answer: b
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