Ford Motor Interview Questions, Process, and Tips

Q1. Define MOQ and EOQ
Ans. 
MOQ is Minimum Order Quantity, the smallest amount of a product a supplier is willing to sell. EOQ is Economic Order Quantity, the optimal order quantity to minimize total inventory costs.
• MOQ is set by suppliers to ensure profitability and efficiency in production.

• EOQ helps in determining the ideal order quantity to minimize ordering and holding costs.

• MOQ is usually higher than EOQ as it represents the minimum amount a...read more
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Q2. How are you good at Negotiation
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I am skilled in negotiation through my ability to understand the needs of both parties, communicate effectively, and find mutually beneficial solutions.
• I have strong communication skills which help me clearly articulate my points and understand the other party's perspective.

• I am able to research and gather relevant information to support my arguments during negotiations.

• I am skilled at finding creative solutions that b...read more
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Few MCQ's and 2 coding questions

Q1. The technical HR asked me for a self intro
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Q2. How will you optimize your academic project if you were provided with more funds
Ans. 
I would enhance the project by investing in better technology, hiring more skilled team members, and conducting more extensive research.
• Invest in better technology to improve performance and efficiency

• Hire more skilled team members to bring in fresh perspectives and expertise

• Conduct more extensive research to explore new possibilities and innovations
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Q1. What are 4 golden signals of SRE?
Ans. 
The 4 golden signals of SRE are latency, traffic, errors, and saturation.
• Latency: Measures the time taken for a request to be processed.

• Traffic: Indicates the amount of network traffic or requests being handled.

• Errors: Tracks the rate of errors occurring in the system.

• Saturation: Measures the utilization of resources such as CPU, memory, or disk.
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Q2. Explain the process on the Incident management?
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Incident management is the process of identifying, analyzing, and resolving incidents to minimize impact on operations.
• Incidents are reported and categorized based on severity and impact.

• A response team is assigned to investigate and resolve the incident.

• Communication is key throughout the process to keep stakeholders informed.

• Post-incident analysis is conducted to identify root causes and prevent future incidents.

• Docu...read more
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Its on python mostly to do with string manipulation, acces data from list, dataframe

Q1. Wht is your last ctc
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Q2. How soon can you join
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Q1. Tell me about your projects
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Q2. Have you worked on playwright
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Q1. What is the difference between linear and classification?
Ans. 
Linear regression is used for predicting continuous values, while classification is used for predicting discrete values.
• Linear regression is used when the output variable is continuous, such as predicting house prices based on features like size and location.

• Classification is used when the output variable is categorical, such as predicting whether an email is spam or not based on its content.

• Linear regression aims to f...read more
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Q2. Explain example of outlier
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An outlier is a data point that differs significantly from other data points in a dataset.
• Outliers can skew statistical analyses and machine learning models.

• Examples of outliers include a person's weight being recorded as 1000 lbs, when the average weight is around 150 lbs.

• Outliers can be detected using statistical methods like Z-score or IQR.
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Q3. Explain k mean algorithm
Ans. 
K-means algorithm is a clustering technique that partitions data into k clusters based on similarity.
• Divides data points into k clusters based on centroids

• Iteratively assigns data points to the nearest centroid and updates centroids

• Continues until centroids no longer change significantly

• Example: Grouping customers based on purchasing behavior
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Q4. Explain the metrics for classification
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Classification metrics are used to evaluate the performance of a classification model.
• Accuracy: measures the proportion of correctly classified instances out of total instances

• Precision: measures the proportion of true positive predictions out of all positive predictions

• Recall: measures the proportion of true positive predictions out of all actual positive instances

• F1 Score: harmonic mean of precision and recall, balan...read more
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