Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

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Question # 4

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

Which approach should you take?

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Use the NOW () function in BigQuery to record the eventâ€™s time.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

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Question # 5

Flowlogisticâ€™s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so theyâ€™ve purchased a visualization tool to simplify the creation of BigQuery reports. However, theyâ€™ve been overwhelmed by all thedata in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Export the data into a Google Sheet for virtualization.

Create an additional table with only the necessary columns.

Create a view on the table to present to the virtualization tool.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

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Question # 6

Flowlogisticâ€™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?

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

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Question # 7

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Store the common data in BigQuery as partitioned tables.

Store the common data in BigQuery and expose authorized views.

Store the common data encoded as Avro in Google Cloud Storage.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

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Question # 8

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

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