Your neural network model is taking days to train. You want to increase the training speed. What can you do?

A. Subsample your test dataset.

B. Subsample your training dataset.

C. Increase the number of input features to your model.

D. Increase the number of layers in your neural network.

You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

A. Create a new BigOuery dataset and use streaming inserts to land the data from multiple vendors. Configure your BigQuery ML model to use the "ingestion' dataset as the training data.

B. Use BigQuery streaming inserts to land the data from multiple vendors whore your BigQuery dataset ML model is deployed.

C. Create a Pub'Sub topic and send all vendor data to it Connect a Cloud Function to the topic to process the data and store it in BigQuery.

D. Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.

You need to choose a database for a new project that has the following requirements:

1.

Fully managed

2.

Able to automatically scale up

3.

Transactionally consistent

4.

Able to scale up to 6 TB

5.

Able to be queried using SQL

Which database do you choose?

A. Cloud SQL

B. Cloud Bigtable

C. Cloud Spanner

D. Cloud Datastore

What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and streaming inserts

B. Storage, queries, and loading data from a file

C. Storage, queries, and exporting data

D. Queries and streaming inserts

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Include multiple time series values within the row key

B. Keep the row keep as an 8 bit integer

C. Keep your row key reasonably short

D. Keep your row key as long as the field permits

What is the HBase Shell for Cloud Bigtable?

A. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

B. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

C. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.

D. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.

Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

A. Field promotion

B. Randomization

C. Salting

D. Hashing

You are testing a Dataflow pipeline to ingest and transform text files. The files are compressed gzip, errors are written to a dead-letter queue, and you are using Sidelnputs to join data You noticed that the pipeline is taking longer to complete than expected, what should you do to expedite the Dataflow job?

A. Switch to compressed Avro files

B. Reduce the batch size

C. Retry records that throw an error

D. Use CoGroupByKey instead of the Sidelnput

You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A. Cloud SQL

B. Cloud Bigtable

C. Cloud Spanner

D. Cloud Datastore

Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

A. 1 Create a project for storage of the data for your organization. 2 Create a central Cloud Storage bucket with three folders to store the files for each department.

3. Create a central BigQuery dataset with tables prefixed with the department name.

4 Give viewer rights for the storage project for the users of your departments.

B. 1Create a project for storage of the data for each of your departments. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets.

3. Create user groups for authorized readers for each bucket and dataset.

4 Enable the IT team to administer the user groups to add or remove users as the departments' request.

C. 1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets.

3. Publish the data that each department shared in Analytics Hub.

4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.

D. 1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones. 4 Enable each department to own and share the data of their data lakes.