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Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

ACloud Pub/Sub, Cloud Dataflow, and Cloud Storage

BCloud Pub/Sub, Cloud Dataflow, and Local SSD

CCloud Pub/Sub, Cloud SQL, and Cloud Storage

DCloud Load Balancing, Cloud Dataflow, and Cloud Storage

Question 2

Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.

Which product should they use to store the data?

ACloud Bigtable

BGoogle BigQuery

CGoogle Cloud Storage

DGoogle Cloud Datastore

Question 3

You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests dat

a. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

ACreate a BigQuery reservation for the job.

BCreate a BigQuery reservation for the service account running the job.

CCreate a BigQuery reservation for the dataset.

DCreate a BigQuery reservation for the project.

Answer : D

https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas.

Here's why creating a BigQuery reservation for the project is the most suitable solution:

Project-Level Reservation: BigQuery reservations are applied at the project level. This means that the reserved slots (processing capacity) are shared across all jobs and queries running within that project. Since your CDC process is a significant contributor to your BigQuery usage, reserving slots for the entire project ensures that your CDC process always has access to the necessary resources, regardless of other activities in the project.

Predictable Cost Model: Reservations provide a fixed, predictable cost model. Instead of paying the on-demand price for each query, you pay a fixed monthly fee for the reserved slots. This eliminates the variability of costs associated with on-demand billing, making it easier to budget and forecast your BigQuery expenses.

BigQuery Monitoring: You can use BigQuery Monitoring to analyze the historical usage patterns of your CDC process and other queries within your project. This information helps you determine the appropriate amount of slots to reserve, ensuring that you have enough capacity to handle your workload while optimizing costs.

Why other options are not suitable:

A . Create a BigQuery reservation for the job: BigQuery does not support reservations at the individual job level. Reservations are applied at the project or assignment level.

B . Create a BigQuery reservation for the service account running the job: While you can create reservations for assignments (groups of users or service accounts), it's less efficient than a project-level reservation in this scenario. A project-level reservation covers all jobs within the project, regardless of the service account used.

C . Create a BigQuery reservation for the dataset: BigQuery does not support reservations at the dataset level.

By creating a BigQuery reservation for your project based on your utilization analysis, you can achieve a predictable cost model while ensuring that your CDC process and other queries have the necessary resources to run smoothly.

Question 4

You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

AInsert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.

BLog debug information in each ParDo function, and analyze the logs at execution time.

CInsert output sinks after each key processing step, and observe the writing throughput of each block.

DVerify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks

Question 5

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

ADisable writes to certain tables.

BRestrict access to tables by role.

CEnsure that the data is encrypted at all times.

DRestrict BigQuery API access to approved users.

ESegregate data across multiple tables or databases.

FUse Google Stackdriver Audit Logging to determine policy violations.

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