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Google Google Cloud Certified Professional-Data-Engineer New Questions

Google Professional Data Engineer Exam Questions and Answers

Question 53

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

Options:

A.

Use a row key of the form .

B.

Use a row key of the form .

C.

Use a row key of the form #.

D.

Use a row key of the form >##.

Question 54

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

Options:

A.

Load data into different partitions.

B.

Load data into a different dataset for each client.

C.

Put each client’s BigQuery dataset into a different table.

D.

Restrict a client’s dataset to approved users.

E.

Only allow a service account to access the datasets.

F.

Use the appropriate identity and access management (IAM) roles for each client’s users.

Question 55

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

Options:

A.

Send the data to Google Cloud Datastore and then export to BigQuery.

B.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

C.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

D.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Question 56

Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Options:

A.

Threading

B.

Serialization

C.

Dropout Methods

D.

Dimensionality Reduction