You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Load the data every 30 minutes into a new partitioned table in BigQuery.

B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

D. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

A. Cloud Dataflow connector

B. DataFlow SDK

C. BiqQuery API

D. BigQuery Data Transfer Service

Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

A. dataflow.worker

B. dataflow.compute

C. dataflow.developer

D. dataflow.viewer

Cloud Dataproc charges you only for what you really use with _____ billing.

A. month-by-month

B. minute-by-minute

C. week-by-week

D. hour-by-hour

You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on

Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

A. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.

B. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.

C. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.

D. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

You need (o give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline There will be tens of thousands of messages per second and that can be multithreaded, and you worry about the backpressure on the system How should you design your pipeline to minimize that backpressure?

A. Call out to the service via HTTP

B. Create the pipeline statically in the class definition

C. Create a new object in the startBundle method of DoFn

D. Batch the job into ten-second increments

You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app You have reviewed old chat logs and lagged each conversation for intent based on each customer's stated intention for contacting customer service About 70% of customer requests are simple requests that are solved within 10 intents The remaining 30% of inquiries require much longer, more complicated requests

Which intents should you automate first?

A. Automate the 10 intents that cover 70% of the requests so that live agents can handle more complicated requests

B. Automate the more complicated requests first because those require more of the agents' time

C. Automate a blend of the shortest and longest intents to be representative of all intents

D. Automate intents in places where common words such as "payment" appear only once so the software isn't confused

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert.

What is the most likely cause of this problem?

A. They have not assigned the timestamp, which causes the job to fail

B. They have not set the triggers to accommodate the data coming in late, which causes the job to fail

C. They have not applied a global windowing function, which causes the job to fail when the pipeline is created

D. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name

B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name

C. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code

D. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified

You want to cleanse the data n near-reel time before running it through Al models

What should you do?

A. Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

B. Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasets

C. Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sink

D. Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery