C.

SOCKS

When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy
routes data intended for the Cloud Dataproc cluster through an SSH tunnel

C.

Crossed feature columns

D.

Bucketization of a continuous feature

Explanation
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive
bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature)
depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences
between different feature combinations, we can add crossed feature columns to the model.

C.

Use Stackdriver Monitoring to see the usage of BigQuery query slots.

C.

Give a user access to view all datasets in a project, but not run queries on them.

Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to
separate data access permissions from job-running permissions

B.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

B.

Increases query speed, makes queries simpler

Denormalization increases query speed for tables with billions of rows because BigQuery's performance
degrades when doing JOINs on large tables, but with a denormalized data
structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization
also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates
redundant data.

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.

C.

Use the __PARTITIONTIME pseudo-column in the WHERE clause

Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp
for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a
clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02')