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

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Question # 65

You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor= ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A.

Option A

B.

Option B.

C.

Option C

D.

Option D

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Question # 66

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

A.

Rewrite the job in Pig.

B.

Rewrite the job in Apache Spark.

C.

Increase the size of the Hadoop cluster.

D.

Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

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Question # 67

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?

A.

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

B.

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

C.

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

D.

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

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Question # 68

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?

A.

Store the common data in BigQuery as partitioned tables.

B.

Store the common data in BigQuery and expose authorized views.

C.

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

D.

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

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Question # 69

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?

A.

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

B.

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

C.

Use the NOW () function in BigQuery to record the event’s time.

D.

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

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Question # 70

Flowlogistic’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 they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all the data 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?

A.

Export the data into a Google Sheet for virtualization.

B.

Create an additional table with only the necessary columns.

C.

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

D.

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

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