Akamai Technologies Senior Data Scientist Interview Questions and Answers

Q1. Why did you choose infor
Ans. 
I chose Infor because of its reputation for innovative technology solutions and its commitment to employee development.
• Infor is known for its cutting-edge technology solutions in the industry.

• I was impressed by Infor's focus on employee growth and development opportunities.

• I believe Infor's values align with my own professional goals and aspirations.
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Q2. Explain how you work under stress
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I thrive under pressure by staying organized, prioritizing tasks, and maintaining a positive attitude.
• I stay organized by creating to-do lists and breaking down tasks into manageable steps.

• I prioritize tasks based on deadlines and importance to ensure that critical work is completed first.

• I maintain a positive attitude by taking short breaks to recharge, practicing deep breathing exercises, and seeking support from col
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Q1. Everything mentioned on CV
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Q2. Some coding questions
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Q1. Same questions as first interview but deeper approach
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Q2. Coding is involved again
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Q1. About projects and then questions related to ML and DL. Mostly focused on DL part
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Q2. What is the difference between Adam optimizer and Gradient Descent Optimizer?
Ans. 
Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
• Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.

• Adam optimizer uses adaptive learning rates for each parameter.

• Gradient Descent optimizer has a fixed learning rate for all parameters.

• Adam optimizer includes momentum to speed up convergence.

• Gradient Descent optimizer updates parameters b...read more
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Q3. When to use Relu and when not?
Ans. 
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
• ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.

• Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.

• Consider using Leaky ReLU or Sigmoid for output layers depending on the task.

• ReLU is computationally efficient and helps in preventing the vanishing gradient prob...read more
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Standard question from sql and python in hackerrank

Q1. Reverse a linked list
Ans. 
Reverse a linked list by changing the direction of pointers
• Start with three pointers: current, previous, and next

• Iterate through the linked list, updating pointers to reverse the direction

• Return the new head of the reversed linked list
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Q2. Question based on joins and subquery
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Q1. More question about project
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Q2. What do you know about genAI
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Q1. Why you joined as Data science team
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Q2. What is difference between AI and ML ops
Ans. 
AI Ops focuses on managing and deploying artificial intelligence models, while ML Ops focuses on managing and deploying machine learning models.
• AI Ops involves managing and deploying artificial intelligence models, which may include deep learning, natural language processing, and computer vision.

• ML Ops involves managing and deploying machine learning models, which typically involve statistical algorithms and data analy...read more
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Q3. What is your 3 yrs & 5 yrs goal if you joined this company
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Major focus on busniess output

Q1. Asked about some machine learning concepts like NLP, TensorFlow etc
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Q1. Tell me about your self
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180 mins of online test with camera ON. Major topics include Excel, Aptitude, Python, Statistics and Case Study

Q1. Explain Apriori Method
Ans. 
Apriori method is a popular algorithm for frequent itemset mining in data mining.
• Used for finding frequent itemsets in transactional databases

• Based on the concept of association rule mining

• Involves generating candidate itemsets and pruning based on support threshold

• Example: If {milk, bread} is a frequent itemset, then {milk} and {bread} are also frequent
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Q2. Explain train-test in Scikit learn
Ans. 
Train-test split is a method used to divide a dataset into training and testing sets for model evaluation in Scikit learn.
• Split the dataset into two subsets: training set and testing set

• Training set is used to train the model, while testing set is used to evaluate the model's performance

• Common split ratios are 70-30 or 80-20 for training and testing sets

• Example: X_train, X_test, y_train, y_test = train_test_split(X, y,
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Q1. Explain about projects in current company
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Q2. Why do you want to move to an individual contributor role from a managerial position
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Q1. Why do you want to join Wolters Kluwer?
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Q2. Discussion around analytics and managerment reporting deliverables in current org.
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