Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
A financial services firm providing products such as credit cards and bank loans receives thousands of online applications from clients applying for their products. Because it takes a lot of effort to scan and check all applications if they meet the minimum requirements for the products they are applying for, they want to build a machine learning model takes application fields like annual income, marital status, date of birth, occupation and other attributes as input and finds out if the applicant is qualified for the product the client applies for.
What is the machine learning technique will help build such model?
A. Regression
B. Classification
C. Clustering
D. Reinforcement learning
Answer: B.
A regression problem is a problem which its output variable is of continuous value. Problems which finds out about variables such as weights, prices or age are considered regression problems.
A classification problem is a problem which the output variable is a category. Examples of classification problems are finding a passenger’s nationality, detect if a patient is diagnosed with a disease or if an applicant is qualified for a job interview.
Regression and classification are supervised learning problems. It means, the machine learns from past experiences by training it on a labeled data set. A training set is a set of rows with input and output parameters. The machine then learns from the training set and improves its parameters for better detection.
Clustering is an unsupervised learning method. An unsupervised learning is a method to find references between input data without labeled output. The purpose is to find meaningful structure between the input sets with similar features and group them. Clustering is the method of grouping data points share similarities and separating dissimilar points to other groups. Examples of clustering applications are customer segmentation (new, frequent, loyal, ..), city land value and detecting anomalies in network traffic.
Reinforcement learning is a technique which a machine takes actions without training sets to reach the highest rewards possible. The agent learns from trial and decides what to do to perform a given task without supervision. The task punishes the agent for a wrong action and rewards it for achieving the task. Examples of reinforcement learning is asking an agent to play a maze game to reach the exit with traps along the way or making an agent play a video game and win a racing game.
From the explanation above, we can see the scenario problem which finding if a client is qualified for a product is a classification problem. So, answer B is correct.
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