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Launching into Machine Learning Coursera Quiz Answers

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Launching into Machine Learning Coursera Quiz Answers


 

Get to know your data: Improve data through Exploratory Data Analysis Quiz Answers

Question 1)
Which of the following are categories of data quality tools?

  • Cleaning tools
  • Monitoring tools
  • Both โ€˜Cleaning toolsโ€™ and โ€˜Monitoring toolsโ€™
  • None of the options

Question 2)
What are the features of low data quality?

  • Unreliable info
  • Incomplete data
  • Duplicated data
  • All of the options

Question 3)
What are the objectives of exploratory data analysis?

  • Check for missing data and other mistakes.
  • Gain maximum insight into the data set and its underlying structure.
  • Uncover a parsimonious model, one which explains the data with a minimum number of predictor variables.
  • All of the options

Question 4)
Exploratory Data Analysis is majorly performed using the following methods:

  • Univariate
  • Bivariate
  • Both Univariate and Bivariate
  • None of the options

Question 5)
Which of the following is not a component of Exploratory Data Analysis?

  • Accounting and Summarizing
  • Anomaly Detection
  • Statistical Analysis and Clustering
  • Hyperparameter tuning

 

Machine Learning in Practice Quiz Answers

Question 1)
Which of the following machine learning models have labels, or in other words, the correct answers to whatever it is that we want to learn to predict?

  • Unsupervised Model
  • Supervised Model
  • Reinforcement Model
  • None of the options

Question 2)
Which model would you use if your problem required a discrete number of values or classes?

  • Regression Model
  • Unsupervised Model
  • Supervised Model
  • Classification Model

Question 3)
To predict the continuous value of our label, which of the following algorithms is used?

  • Classification
  • Regression
  • Unsupervised
  • None of the options

Question 4)
What is the most essential metric a regression model uses?

  • Mean squared error as their loss function
  • Cross entropy
  • Both โ€˜Mean squared error as their loss functionโ€™ & โ€˜Cross entropyโ€™
  • None of the options

Question 5)
Why is regularization important in logistic regression?

  • Avoids overfitting
  • Keeps training time down by regulating the time allowed
  • Finds errors in the algorithm
  • Encourages the use of large weights

 

Training AutoML Models Using Vertex AI Quiz Answers

Question 1)
What is the main benefit of using an automated Machine Learning workflow?

  • It makes the model perform better.
  • It makes the model run faster.
  • It deploys the model into production.
  • It reduces the time it takes to develop trained models and assess their performance.

Question 2)
For a user who can use SQL, but has little Machine Learning experience and wants a โ€˜Low-Codeโ€™ solution, which Machine Learning framework should they use?

  • Scikit-Learn
  • BigQuery ML
  • AutoML
  • Python

Question 3)
If a dataset is presented in a Comma Separated Values (CSV) file, which is the correct data type to choose in Vertex AI?

  • Image
  • Tabular
  • Text
  • Video

Question 4)
If the business case is to predict fraud detection, which is the correct Objective to choose in Vertex AI?

  • Clustering
  • Forecasting
  • Regression/Classification
  • Segmentation

Question 5)
Which of the following are stages of the Machine Learning workflow that can be managed with Vertex AI?

  • Create a dataset and upload data.
  • Train an ML model on your data.
  • Deploy your trained model to an endpoint for serving predictions.
  • All of the options.

Question 6)
What is the default setting in AutoML Tables for the data split in model evaluation?

  • 70% Training, 20% Validation, 10% Testing
  • 80% Training, 15% Validation, 5% Testing
  • 80% Training, 5% Validation, 15% Testing
  • 80% Training 10% Validation, 10% Testing

Question 7)
MAE, MAPE, RMSE, RMSLE and R2 are all available as test examples in the Evaluate section of Vertex AI and are common examples of what type of metric?

  • Forecasting Regression Metrics
  • Linear Regression Metrics
  • Clustering Regression Metrics
  • Decision Trees Progression Metrics

Question 8)
What does the Feature Importance attribution in Vertex AI display?

  • How much each feature impacts the model, expressed as a percentage
  • How much each feature impacts the model, expressed as a ratio
  • How much each feature impacts the model, expressed as a ranked list
  • How much each feature impacts the model, expressed as a decimal

Question 9)
Which of the following metrics can be used to find a suitable balance between precision and recall in a model?

  • PR AUC
  • ROC AUC
  • Log Loss
  • F1 Score

 

BigQuery Machine Learning: Develop ML Models Where Your Data Lives Quiz Answers

Question 1)
Which of the following are advantages of BigQuery ML when compared to Python based ML frameworks?

  • BigQuery ML custom models can be created without the use of multiple tools
  • BigQuery ML automates multiple steps in the ML workflow
  • Moving and formatting large amounts of data takes longer with Python based models compared to model training in BigQuery
  • All of the options

Question 2)
Which of these BigQuery supported classification models is most relevant for predicting binary results, such as True/False?

  • XGBoost
  • AutoML Tables
  • Logistic Regression
  • DNN Classifier (TensorFlow)

Question 3)
For Classification or Regression problems with decision trees, which of the following models is most relevant?

  • Wide and Deep NNs
  • AutoML Tables
  • Linear Regression
  • XGBoost

Question 4)
Where labels are not available, for example where customer segmentation is required, which of the following BigQuery supported models is useful?

  • Time Series Forecasting
  • Recommendation – Matrix Factorization
  • K-Means Clustering
  • Time Series Anomaly Detection

Question 5)
What are the 3 key steps for creating a Recommendation System with BigQuery ML?

  • Prepare training data in BigQuery, train a recommendation system with BigQuery ML, use the predicted recommendations in production
  • Import training data to BigQuery, train a recommendation system with BigQuery ML, tune the hyperparameters
  • Prepare training data in BigQuery, select a recommendation system from BigQuery ML, deploy and test the model
  • Prepare training data in BigQuery, specify the model options in BigQuery ML, export the predictions to Google Analytics

 

Optimization Quiz Answers

Question 1)
For the formula used to model the relationship i.e. y = mx + b, what does โ€˜mโ€™ stand for?

  • It captures the amount of change we’ve observed in our label in response to a small change in our feature.
  • It refers to a bias term which can be used for regression.
  • It refers to a bias term which can be used for regression and it captures the amount of change we’ve observed in our label in response to a small change in our feature.
  • None of the options are correct.

Question 2)
What are the basic steps in an ML workflow (or process)?

  • Collect data
  • Check for anomalies, missing data and clean the data
  • Perform statistical analysis and initial visualization
  • All options are correct.

Question 3)
Which of the following loss functions is used for classification problems?

  • MSE
  • Cross entropy
  • Both MSE & Cross entropy
  • None of the options are correct.

Question 4)
Which of the following gradient descent methods is used to compute the entire dataset?

  • Batch gradient descent
  • Gradient descent
  • Mini-batch gradient descent
  • None of the options are correct.

Question 5)
Which of the following are benefits of Performance metrics over loss functions?

  • Performance metrics are easier to understand.
  • Performance metrics are directly connected to business goals.
  • Performance metrics are easier to understand and are directly connected to business goals.
  • None of the options are correct.

 

Generalization and Sampling Quiz Answers

Question 1)
Which is the best way to assess the quality of a model?

  • Observing how well a model performs against an existing known dataset.
  • None of the options are correct.
  • Observing how well a model performs against a new dataset that it hasn’t seen before.
  • Observing how well a model performs against a new dataset that it hasn’t seen before and observing how well a model performs against an existing known dataset.

Question 2)
How do you decide when to stop training a model?

  • When your loss metrics start to both increase and decrease
  • None of the options are correct
  • When your loss metrics start to increase
  • When your loss metrics start to decrease

Question 3)
Which of the following actions can you perform on your model when it is trained and validated?

  • You can write it multiple times against the independent test dataset.
  • You can write it once, and only once, against the independent test dataset.
  • You can write it once, and only once against the dependent test dataset.
  • You can write it multiple times against the dependent test dataset.

Question 4)
Which of the following allows you to create repeatable samples of your data?

  • Use the first few digits or the last few digits of a hash function on the field that you’re using to split or bucketize your data.
  • Use the last few digits of a hash function on the field that you’re using to split or bucketize your data.
  • None of the options are correct.
  • Use the first few digits of a hash function on the field that you’re using to split or bucketize your data.

Question 5)
Which of the following allows you to split the dataset based upon a field in your data?

  • BUCKETIZE, an open-source hashing algorithm that is implemented in BigQuery SQL.
  • None of the options are correct.
  • ML_FEATURE FINGERPRINT, an open-source hashing algorithm that is implemented in BigQuery SQL.
  • FARM_FINGERPRINT, an open-source hashing algorithm that is implemented in BigQuery SQL.