B.

Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.

D.

There is no charge for a query that retrieves its results from cache.

When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table

A.

Rewrite the job in Pig.

C.

master node

The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m
suffix—for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".

B.

To make sure your model is generalized for more than just the training data

The flaw with evaluating a predictive model on training data is that it does not inform you on how well the
model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset
rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test
dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training
dataset. This is called overfitting.

D.

Maintain a database table to store the hash value and other metadata for each data
entry.

D.

Train on the new data while using the existing data as your test set.

A.

The wide model is used for memorization, while the deep model is used for generalization.

B.

A good use for the wide and deep model is a recommender system.

Explanation
Can we teach computers to learn like humans do, by combining the power of memorization and
generalization? It's not an easy question to answer, but by jointly training a wide linear model (for
memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to
bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale
regression and classification problems with sparse inputs (categorical features with a large number of possible
feature values), such as recommender systems, search, and ranking problems.