All Coursera Quiz Answers

Feature Engineering Coursera Quiz Answers

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Feature Engineering Coursera Quiz Answers


 

Introduction to Vertex AI Feature Store Quiz Answers

Question 1)
What is one definition of a feature in machine learning?

  • A value that you receive from a model as an output
  • A method of feature store
  • A value that is passed as input to a model
  • A place to store any data

Question 2)
Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. Using a central featurestore, enables an organization to efficiently share, discover, and re-use ML features at scale, which can increase the velocity of developing and deploying new ML applications. What are the key challenges that Vertex AI Feature Store solves?

  • Mitigate data storage silos, which occurs when you might have built and managed separate solutions for storage and the consumption of feature values.
  • Detect drift, as a result of significant changes to your feature data distribution over time.
  • Mitigate training-serving skew, which occurs when the feature data distribution that you use in production differs from the feature data distribution that was used to train your model.
  • All of the options are correct.

Question 3)
Where are the features registered?

  • Feature registry
  • Online Store
  • Offline Store
  • Feature Monitoring

Question 4)
Which of the following is an instance of an entity type?

  • Feature
  • Online Store
  • Entity
  • Featurestore

Question 5)
What are the two methods feature store offers for serving features?

  • Online serving and Offline serving
  • Batch serving and Online serving
  • Offline serving and Stream serving
  • Batch serving and Stream serving

Question 6)
Which of the following is the process of importing feature values computed by your feature engineering jobs into a featurestore?

  • Feature store
  • Feature Monitoring
  • Feature serving
  • Feature ingestion

 

Raw Data to Features Quiz Answers

Question 1)
In what form can raw data be used inside ML models?

  • After turning your raw data into a useful feature vectors
  • After turning your raw data into a useful feature matrix
  • After turning your raw data into multidimensional vectors
  • None of the options are correct.

Question 2)
A good feature has which of the following characteristics?

  • It should be related to the objective.
  • It should be known at prediction time.
  • It should be numeric with meaningful magnitude.
  • All of the options are correct.

Question 3)
Which of the following are the requirements to build an effective machine learning model?

  • It should scale to a large dataset.
  • It should find good features.
  • It should be able to preprocess with Vertex AI Platform.
  • All of the options are correct.

Question 4)
Which of the following statements is true about preprocessing?

  • Preprocessing within the context of Cloud ML allows you to do it at scale.
  • Preprocessing without the context of Cloud ML allows you to do it at scale.
  • Both options are correct.
  • None of the options are correct.

Question 5)
Which of the following statements is true?

  • Same problems in the same domain may need different features.
  • Different problems in the same domain may need different features.
  • Different problems in different domains may need the same features.
  • None of the options are correct.

 

Feature Engineering Quiz Answers

Question 1)
True or False: Feature Engineering is often one of the most valuable tasks a data scientist can do to improve model performance, for three main reasons:
You can isolate and highlight key information, which helps your algorithms “focus” on whatโ€™s important.
You can bring in your own domain expertise.
Once you understand the “vocabulary” of feature engineering, you can bring in other peopleโ€™s domain expertise.

  • True
  • False

Question 2)
What is one-hot encoding?

  • One-hot encoding is a process by which categorical variables are converted into a form that could be provided to neural networks to do a better job in prediction.
  • One-hot encoding is a process by which numeric variables are converted into a form that could be provided to neural networks to do a better job in prediction.
  • One-hot encoding is a process by which numeric variables are converted into a categorical form that could be provided to neural networks to do a better job in prediction.
  • One-hot encoding is a process by which only the hottest numeric variable is retained for use by the neural network.

Question 3)
What do you use the tf.feature_column.bucketized_column function for?

  • To compute the hash buckets needed to one-hot encode categorical values
  • To count the number of unique buckets the input values falls into
  • To discretize floating point values into a smaller number of categorical bins
  • None of the options are correct.

Question 4)
What is a feature cross?

  • A feature cross is a synthetic feature formed by adding (crossing) two or more features. Crossing combinations of features can provide predictive abilities beyond what those features can provide individually.
  • A feature cross is a synthetic feature formed by multiplying (crossing) two or more features. Crossing combinations of features can provide predictive abilities beyond what those features can provide individually.
  • A feature cross is a synthetic feature formed by dividing (crossing) two or more features. Crossing combinations of features can provide predictive abilities beyond what those features can provide individually.
  • None of the options are correct.

Question 5
Which of the following statements are true regarding the ML.EVALUATE function?

  • The ML.EVALUATE function can be used with linear regression, logistic regression, k-means, matrix factorization, and ARIMA-based time series models.
  • The ML.EVALUATE function evaluates the predicted values against the actual data.
  • You can use the ML.EVALUATE function to evaluate model metrics.
  • All of the options are correct.

Question 6
What is the significance of ML.FEATURE_CROSS?

  • ML.FEATURE_CROSS generates a STRUCT feature with all combinations of crossed categorical features except for 1-degree items.
  • ML.FEATURE_CROSS generates a STRUCT feature with few combinations of crossed categorical features except for 1-degree items.
  • ML.FEATURE_CROSS generates a STRUCT feature with all combinations of crossed categorical features including 1-degree items.
  • None of the options are correct.

Question 7
Which of the following statements are true regarding the ML.BUCKETIZE function?

  • ML.BUCKETIZE is a pre-processing function that creates buckets by returning a STRING as the bucket name after numerical_expression is split into buckets by array_split_points..
  • It bucketizes a continuous numerical feature into a string feature with bucket names as the value.
  • Both options are correct.
  • None of the options are correct.

Question 8
Which of the following is true about Feature Cross?

  • It is a process of combining features into a single feature.
  • Feature Cross enables a model to learn separate weights for each combination of features.
  • Both options are correct.
  • None of the options are correct.

 

Preprocessing and Feature Creation Quiz Answers

Question 1)
Which of these accurately describes the relationship between Apache Beam and Dataflow?

  • Dataflow is the proprietary version of the Apache Beam API and the two are not compatible.
  • Apache Beam is the API for data pipeline building in Java or Python and Dataflow is the implementation and execution framework.
  • They are the same.

Question 2)
True or False: The Filter method can be carried out in parallel and autoscaled by the execution framework:

  • True: Anything in Map or FlatMap can be parallelized by the Beam execution framework.
  • False: Anything in Map or FlatMap can be parallelized by the Beam execution framework.

Question 3)
What is the purpose of a Cloud Dataflow connector?

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  • Connectors allow you to output the results of a pipeline to a specific data sink like Bigtable, Google Cloud Storage, flat file, BigQuery, and more.
  • Connectors allow you to chain multiple data-processing steps together automatically so they process in parallel.
  • Connectors allow you to authenticate your pipeline as specific users who may have greater access to datasets.

Question 4)
To run a pipeline you need something called a ________.

  • runner
  • Apache Beam
  • pipeline
  • executor

Question 5)
Your development team is about to execute this code block. What is your team about to do?

  • We are compiling our Cloud Dataflow pipeline written in Java and are submitting it to the cloud for execution.Notice that we are calling mvn compile and passing in –runner=DataflowRunner.
  • We are compiling our Cloud Dataflow pipeline written in Python and are loading the outputs of the executed pipeline inside of Google Cloud Storage (gs://)
  • We are preparing a staging area in Google Cloud Storage for the output of our Cloud Dataflow pipeline and will be submitting our BigQuery job with a later command.

Question 6)
True or False: A ParDo acts on all items at once (like a Map in MapReduce).

  • True
  • False.

Question 7)
What is one key advantage of preprocessing your features using Apache Beam?

  • Apache Beam code is often harder to maintain and run at scale than BigQuery preprocessing pipelines.
  • The same code you use to preprocess features in training and evaluation can also be used in serving.
  • Apache Beam transformations are written in Standard SQL which is scalable and easy to author.

 

Feature Crosses – TensorFlow Playground Coursera Quiz Answers

Question 1)
True or False: We can create many different kinds of feature crosses. For example:

[A X B]: a feature cross formed by multiplying the values of two features.
[A x B x C x D x E]: a feature cross formed by multiplying the values of five features.
[A x A]: a feature cross formed by squaring a single feature.

  • True
  • False

Question 2)
True or False: In TensorFlow Playground, orange and blue are used throughout the visualization in slightly different ways, but in general orange shows negative values while blue shows positive values.

  • True
  • False

Question 3)
True or False: In TensorFlow Playground, the data points (represented by small circles) are initially colored orange or blue, which correspond to zero and negative one.

  • True
  • False

Question 4)
Fill in the blanks: In the ____ layers, the lines are colored by the _____ of the connections between neurons. Blue shows a _____ weight, which means the network is using that ____ of the neuron as given. An orange line shows that the network is assigning a _____ weight.

  • Hidden, weights, negative, output, positive
  • Hidden, weights, positive, output, negative
  • Weights, hidden, negative, output, positive
  • Output, weights, negative, hidden, positive

Question 5)
True or False: In TensorFlow Playground, in the output layer, the dots are colored orange or blue depending on their original values. The background color shows what the network is predicting for a particular area. The intensity of the color shows how confident that prediction is.

  • True
  • False

Question 6)
Why might you create an embedding of a feature cross?

  • To create a lower-dimensional representation of the input space
  • To identify similar sets of inputs for clustering
  • To reuse weights learned in one problem in another problem
  • All of the options are correct.

 

Introduction to TensorFlow Transform Quiz Answers

Question 1)
What does tf.Transform do during the training and serving phase?

  • Provides a TensorFlow graph for preprocessing
  • Provides computation over the entire dataset, including on both internal and external data sources
  • None of the options are correct.
  • Provides a transformation polynomial to train the data

Question 2)
As an approach to feature engineering, which of the following most accurately describes what TensorFlow Transform is a hybrid of?

  • Dataflow and TensorFlow
  • AI Platform and TensorFlow
  • Apache Beam and TensorFlow
  • Apache Beam on Dataflow and TensorFlow

Question 3)
True or False: One of the goals of tf.Transform is to provide a TensorFlow graph for preprocessing that can be incorporated into the serving graph (and, optionally, the training graph).

  • False
  • True

Question 4)
Fill in the blank:
The ______________ _______________ is the most important concept of tf.Transform. The ______________ _______________ is a logical description of a transformation of the dataset. The ______________ _______________ accepts and returns a dictionary of tensors, where a tensor means Tensor or 2D SparseTensor.

  • Preprocessing variable
  • Preprocessing function
  • Preprocessing method