All Coursera Quiz Answers

Feature Engineering Coursera Quiz Answers

by Niyander

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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 what’s important.
You can bring in your own domain expertise.
Once you understand the “vocabulary” of feature engineering, you can bring in other people’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?

.apply(TextIO.write().to(“gs://…”));

  • 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