Free Access Google.Professional-Data-Engineer.v2025-03-18.q108 Practice Test (Page 20)

Question 91

Your company receives both batch- and stream-based event dat
a. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

A.Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

B.Set sliding windows to capture all the lagged data.

C.Use watermarks and timestamps to capture the lagged data.

D.Set a single global window to capture all the data.

Question 92

Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

A.categorical_column_with_vocabulary_list

B.categorical_column_with_hash_bucket

C.categorical_column_with_unknown_values

D.sparse_column_with_keys

Question 93

You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow.
The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines.
You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

A.Assign the compute. networkUser role to the Dataflow service agent.

B.Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.

C.Assign the dataflow, admin role to the Dataflow service agent.

D.Assign the dataflow, admin role to the service account that executes the Dataflow pipeline.

Question 94

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt dat
a. What should you do?

A.Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

B.Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

C.Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

D.Add a SideInput that returns a Boolean if the element is corrupt.

Question 95

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=<actorname>ordered by date_releasedor all movies with
tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the
number of indexes?

A.Manually configure the index in your index config as follows: