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Professional-Data-Engineer Questions and Answers

Question # 1

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.

Question # 2

You work for a large ecommerce company. You are using Pub/Sub to ingest the clickstream data to Google Cloud for analytics. You observe that when a new subscriber connects to an existing topic to analyze data, they are unable to subscribe to older data for an upcoming yearly sale event in two months, you need a solution that, once implemented, will enable any new subscriber to read the last 30 days of data. What should you do?

A.

Create a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.

B.

Set the topic retention policy to 30 days.

C.

Set the subscriber retention policy to 30 days.

D.

Ask the source system to re-push the data to Pub/Sub, and subscribe to it.

Question # 3

You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

A.

1 Use a regional Cloud Storage bucket

2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs

3 Seek the subscription back in time by one day to recover the acknowledged messages

4 Start the Dataflow job in a secondary region and write in a bucket in the same region

B.

1 Use a multi-regional Cloud Storage bucket

2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs

3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages

4 Start the Dataflow job in a secondary region

C.

1. Use a dual-region Cloud Storage bucket.

2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs

3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages

4 Start the Dataflow job in a secondary region

D.

1. Use a dual-region Cloud Storage bucket with turbo replication enabled

2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs

3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages

4 Start the Dataflow job in a secondary region.

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