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Artificial Intelligence Free Certificate | Free Quiz Certificate |The Digital Adda

Artificial Intelligence Free Certificate | Free Quiz Certificate |The Digital Adda

Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.

Get Artificial Intelligence certificate from The Digital Adda which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

Terms and Conditions:

  • There are 20 MCQ Questions in this test
  • You need to score atleast 50% to get a certificate
  • A certificate of achievement will be awarded .

Benefits :

Get Artificial Intelligence certificate from The Digital Adda which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

Here are the questions and answers :

Question 1: What is Artificial intelligence? *

  • Playing a game on Computer
  • Making a machine Intelligent
  • Programming on Machine with your Own Intelligence
  • Putting your intelligence in Machine

Question 2: Who is known as the -Father of AI”? *

  • Fisher Ada
  • Alan Turing
  • John McCarthy
  • Allen Newell

Question 3: How many types of Machine Learning are there? *

  • 1
  • 2
  • 3
  • 4

Question 4: Supervised learning deals with unlabeled data, while unsupervised learning deals with labelled data. *

  • True
  • False

Question 5: The “Regression” technique in Machine Learning is a group of algorithms that are used for: *

  • Predicting a continuous value; for example predicting the price of a house based on its characteristics.
  • Prediction of class/category of a case; for example a cell is benign or malignant, or a customer will churn or not.
  • Finding items/events that often co-occur; for example grocery items that are usually bought together by a customer.

Question 6: Which of the following is the meaning of “Out of Sample Accuracy” in the context of evaluation of models? *

  • “Out of Sample Accuracy” is the percentage of correct predictions that the model makes on data that the model has NOT been trained on.
  • “Out of Sample Accuracy” is the accuracy of an overly trained model (which may captured noise and produced a non-generalized model)

Question 7: When should we use Multiple Linear Regression? *

  • When there are multiple dependent variables
  • When we would like to predict impacts of changes in independent variables on a dependent variable.
  • When we would like to identify the strength of the effect that the independent variables have on a dependent variable.

Question 8: Which of the following examples is/are a sample application of Logistic Regression? (select all that apply) *

  • The probability that a person has a heart attack within a specified time period using person’s age and sex.
  • Customer’s propensity to purchase a product or halt a subscription in marketing applications.
  • Likelihood of a homeowner defaulting on a mortgage.
  • Estimating the blood pressure of a patient based on her symptoms and biographical data.

Question 9: Which one is TRUE about the kNN algorithm? *

  • kNN is a classification algorithm that takes a bunch of unlabelled points and uses them to learn how to label other points.
  • kNN algorithm can be used to estimate values for a continuous target.
  • None of the above

Question 10: What is “information gain” in decision trees? *

  • It is the information that can decrease the level of certainty after splitting in each node.
  • It is the entropy of a tree before split minus weighted entropy after split by an attribute.
  • It is the amount of information disorder, or the amount of randomness in each node.

Question 11: Which of the following is an application of clustering? *

  • Customer churn prediction
  • Price estimation
  • Customer segmentation
  • Sales prediction

Question 12: Which approach can be used to calculate dissimilarity of objects in clustering? *

  • Minkowski distance
  • Euclidian distance
  • Cosine similarity
  • All of the above

Question 13: How is a center point (centroid) picked for each cluster in k-means? *

  • We can randomly choose some observations out of the data set and use these observations as the initial means.
  • We can create some random points as centroids of the clusters.
  • We can select it through correlation analysis.

Question 14: What is a “Memory-based” recommender system? *

  • In memory based approach, a recommender system is created using machine learning techniques such as regression, clustering, classification, etc.
  • In memory based approach, we use the entire user-item dataset to generate a recommendation system.
  • In memory based approach, a model of users is developed in attempt to learn their preferences.
  • All of the above

Question 15: What is the shortcoming of content-based recommender systems? *

  • As it is based on similarity among items and users, it is not easy to find the neighbour users.
  • It needs to find similar group of users, so suffers from drops in performance, simply due to growth in the similarity computation.
  • Users will only get recommendations related to their preferences in their profile, and recommender engine may never recommend any item with other characteristics.

Question 16: Which of the following are applications of deep learning? *

  • Color Restoration in Greyscale Images
  • Self-Driving Cars
  • Automatic Machine Translation
  • Automatic Handwriting Generation
  • All of the Above

Question 17: An artificial neural network can be composed of which of the following types of layers? *

  • Output Layer
  • Hidden Layer
  • Sparse Layer
  • Input Layer
  • Intermediate Layer

Question 18: A artificial neuron is so powerful that it can perform complex tasks by simply performing a linear combination of its inputs. *

  • True
  • False

Question 19: The weights and biases in a neural network are optimized using: *

  • Activation Descent
  • Activation Function
  • Vanishing Gradient
  • Gradient Descent
  • Logistic Descent

Question 20: The popularity of self-driving cars has been rising at an exponential rate over the past decade. Based upon what you have learned, which of the following computer vision technique(s) is useful for self-driving cars? Select all relevant answers *

  • Object Detection
  • Motion Transfer
  • Image Classification
  • All of the above



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