Machine Learning for Business Professionals course is offered by “Coursera”. In this post you will get Machine Learning Business Profession Quiz Answer | 100% Correct Answers.

 

 

Machine Learning for Business Professionals

Offered By ”Google Cloud”

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All the Qwiklabs Answer Of week- 1 Are Given In The Below Video

 

 

Week- 1

Use Cases

 

 

1. For each of the following 5 scenarios, determine the difficulty or specificity of each suggested use case.

Scenario 1: Discrete manufacturing e.g. Caterpillar
Say that you are a discrete manufacturing company and you are looking to monitor and track the health of your shipping fleet. You have 50 large container ships that deliver goods.

Use Case: Predict the optimal number of months between ship maintenance cycles to maintain good performance

 

  • Too Unspecific – we dont know the number of container ships
  • Too Specific – each ship is likely to have a different tracking device that gives different data about Lat/Long + speed
  • Too Easy – this is already a solved problem and the company should buy a commercial off the shelf solution
  • Too Difficult – predicting ship maintenance cannot be done
  • Just Right – building a proof-of-concept model, given enough high quality data, could be possible. Let’s try it!

2. Scenario 1: Discrete manufacturing e.g. Caterpillar
Say that you are a discrete manufacturing company and you are looking to monitor and track the health of your shipping fleet. You have 50 large container ships that deliver goods.

Use Case: Use data from on-board ship IoT devices to track device power consumption and predict cost savings

 

  • Too Unspecific – we don’t know what we’re measuring
  • Too Specific – our insights are not generalizable enough across all ships
  • Too Easy – why bother, this is a solved problem
  • Too Difficult – getting sensors on ships is too difficult
  • Just Right – putting sensors on ships to track speed and consumption could get us good data for modeling purposes.

3. Scenario 1: Discrete manufacturing e.g. Caterpillar
Say that you are a discrete manufacturing company and you are looking to monitor and track the health of your shipping fleet. You have 50 large container ships that deliver goods.

Use Case: Predict the total amount of revenue to be generated by a ship’s cargo once sold

 

  • Too Unspecific – we dont know what we’re calculating
  • Too Specific – the insights wouldn’t be useful
  • Too Easy – you wouldnt need a Machine Learning model to calculate revenue from goods sold (it would just be units * price)
  • Too Difficult – you can’t predict the revenue

Just Right

4. Scenario 1: Discrete manufacturing e.g. Caterpillar
Say that you are a discrete manufacturing company and you are looking to monitor and track the health of your shipping fleet. You have 50 large container ships that deliver goods.

Use Case: Predict whether or not to be in the shipping business

 

  • Too Unspecific – this question is too vague for Machine Learning. What are we actually trying to predict?
  • Too Specific
  • Too Easy – this has been solved before many times
  • Too Difficult – you can never predict where a company should be in a certain businesses or not (regardless of how good the questions you ask are)
  • Just Right

 

 

 

 

Module 2 Quiz

 

1. A model trained to predict life expectancy is trained on data collected from Norwegians. This model is successful in that market. However, when it is used in nearby Lithuania, performance suffers considerably. This is most likely an issue of:

 

  • Cleanliness
  • Coverage
  • Completeness

2. The success of expert systems demonstrates that:

 

  • Small sets of if-then statements are too brittle to work as models in most domains
  • It’s already possible to create explainable ML models for many domains

3. Moore’s Law describes

 

  • The rate at which the density, and therefore, the processing power, of computer chips doubles
  • The maximum amount of speedup we can expect when increasing the number of processors used to perform a task
  • The relationship between the size of computer chips and the amount of electric power they need to consume

4. The reliability of Moore’s Law meant that for a long time, one could expect the time it took to compute a workload to halve every two years. Even though Moore’s Law is no longer reliable, recent advances in this area or these areas have led to a similar sort of optimism.

 

  • ASICs
  • Cloud Computing
  • Storage technology
  • Distributed Computing

 

 

All the Qwiklabs Answer Of week- (2-4) Are Given In The Below Video

 

 

Week- 2

Module 3 Quiz

 

1. A reasonable percentage of the data to reserve for evaluation is:

 

  • 20%
  • 100%
  • 1%
  • 50%

2. A survey is administered over the internet via links in ads that appear on the front page of popular local newspapers. The survey asks sensitive questions about health and medical history. This could be an example of:

 

  • Automation bias
  • Selection bias
  • Confirmation bias
  • Reporting bias

3. Fill in the blanks with the appropriate fairness criteria. ________________ leads to models that use the same threshold, regardless of sub-group whereas _____________ leads to models that use different thresholds but which lead to similar distributions of predictions.

 

  • Group unaware, Demographic parity
  • Group unaware, Equal opportunity
  • Demographic parity, Equal opportunity

 

Week- 3

Module 4 Quiz

 

1. Unstructured data are

 

  • Datasets that aren’t well organized or which use inconsistent conventions
  • Data that can’t be easily or objectively compared

2. True or False: It can be a good idea to decompose large use cases into smaller ones, and align each smaller use case to its own model.

 

  • True
  • False

 

 

Week- 4

Module 5 Quiz

 

1. Traditional data storage solutions suffer from which of the following:

 

  • All of the above
  • Poor performance
  • Inseparable combination of compute and storage
  • Siloed access policies and technologies
  • Lack of centralized metadata stores

2. Data enrichment refers to the practice of:

 

  • Cleaning data by removing errors and inconsistencies
  • Using analytics and machine learning to compute values no originally present in the data but which have business value
  • None of these
  • Joining data with third party data
  • All of these

3. Which of the following are techniques used to protect sensitive data:

 

  • Coarsening it
  • Removing it
  • All of the above
  • Masking it

 

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