- (Topic 5)
Your company has a Google Cloud project that uses BlgQuery for data warehousing There are some tables that contain personally identifiable information (PI!) Only the compliance team may access the PH. The other information in the tables must be available to the data science team. You want to minimize cost and the time it takes to assign appropriate access to the tables What should you do?

1. A. * 1 From the dataset where you have the source data, create views of tables that you want to share, excluding Pll* 2 Assign an appropriate project-level IAM role to the members of the data science team 3 Assign access controls to the dataset that contains the view
2. B. * 1 From the dataset where you have the source data, create materialized views of tables that you want to share excluding Pll* 2 Assign an appropriate project-level IAM role to the members of the data science team 3. Assign access controls to the dataset that contains the view.
3. C. * 1 Create a dataset for the data science team* 2 Create views of tables that you want to share excluding Pll* 3 Assign an appropriate project-level IAM role to the members of the data science team 4 Assign access controls to the dataset that contains the view* 5 Authorize the view to access the source dataset
4. D. * 1. Create a dataset for the data science team.* 2. Create materialized views of tables that you want to share, excluding Pll* 3. Assign an appropriate project-level IAM role to the members of the data science team * 4 Assign access controls to the dataset that contains the view* 5 Authorize the view to access the source dataset

Correct Answer: C
This option can help minimize cost and time by using views and authorized datasets. Views are virtual tables defined by a SQL query that can exclude PII columns from the source tables. Views do not incur storage costs and do not duplicate data. Authorized datasets are datasets that have access to another dataset’s data without granting direct access to individual users or groups. By creating a dataset for the data science team and creating views of tables that exclude PII, you can share only the relevant information with the team. By assigning an appropriate project-level IAM role to the members of the data science team, you can grant them access to the BigQuery service and resources. By assigning access controls to the dataset that contains the view, you can grant them access to query the views. By authorizing the view to access the source dataset, you can enable the view to read data from the source tables without exposing PII. The other options are not optimal for this scenario, because they either use materialized views instead of views, which incur storage costs and duplicate data (B, D), or do not create a separate dataset for the data science team, which makes it harder to manage access controls (A). References:
✑ https://cloud.google.com/bigquery/docs/views
✑ https://cloud.google.com/bigquery/docs/authorized-datasets

- (Topic 5)
You are designing a mobile chat application. You want to ensure people cannot spoof chat messages, by
providing a message were sent by a specific user. What should you do

1. A. Tag messages client side with the originating user identifier and the destination user.
2. B. Encrypt the message client side using block-based encryption with a shared key.
3. C. Use public key infrastructure (PKI) to encrypt the message client side using the originating user's privatekey.
4. D. Use a trusted certificate authority to enable SSL connectivity between the client application and the server.

Correct Answer: C

- (Topic 7)
For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

1. A. Use BigQuery as the data warehous
2. B. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflo
3. C. Use Google Data Studio for analysis and reporting.
4. D. Use BigQuery as the data warehous
5. E. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gclou
6. F. Use Google Data Studio for analysis and reporting.
7. G. Use Cloud Dataproc Hive as the data warehous
8. H. Upload gzip files to a MultiRegional Cloud Storagebucke
9. I. Upload this data into BigQuery using gclou
10. J. Use Google data Studio for analysis and reporting.
11. K. Use Cloud Dataproc Hive as the data warehous
12. L. Directly stream data into prtitioned Hive table
13. M. Use Pig scripts to analyze data.

Correct Answer: A

- (Topic 7)
TerramEarth has about 1 petabyte (PB) of vehicle testing data in a private data center. You want to move the data to Cloud Storage for your machine learning team. Currently, a 1- Gbps interconnect link is available for you. The machine learning team wants to start using
the data in a month. What should you do?

1. A. Request Transfer Appliances from Google Cloud, export the data to appliances, and return the appliances to Google Cloud.
2. B. Configure the Storage Transfer service from Google Cloud to send the data from your data center to Cloud Storage
3. C. Make sure there are no other users consuming the 1 Gbps link, and use multi-thread transfer to upload the data to Cloud Storage.
4. D. Export files to an encrypted USB device, send the device to Google Cloud, and request an import of the data to Cloud Storage

Correct Answer: A

- (Topic 2)
For this question, refer to the TerramEarth case study.
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

1. A. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
2. B. Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.
3. C. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.
4. D. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the jo

Correct Answer: D
Storageguarantees 2 replicates which are geo diverse (100 miles apart) which can get better remote latency and availability.
More importantly, is that multiregional heavily leverages Edge caching and CDNs to provide the content to the end users.
All this redundancy and caching means that Multiregional comes with overhead to sync and ensure consistency between geo-diverse areas. As such, it’s much better for write-once- read-many scenarios. This means frequently accessed (e.g. “hot” objects) around the world, such as website content, streaming videos, gaming or mobile applications.
References: https://medium.com/google-cloud/google-cloud-storage-what-bucket-class-for-the-best-performance-5c847ac8f9f2