Free Practice Questions for Google Professional-Data-Engineer Exam

An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.

When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.

When query results are retrieved from a cached results table, you are not charged for the query.

BigQuery caches query results for 24 hours, not 48 hours.

Query results are not cached if you specify a destination table.

A query's results are always cached except under certain conditions, such as if you specify a destination table.

Online prediction

Optimized to minimize the latency of serving predictions.

Predictions returned in the response message.

Batch prediction

Optimized to handle a high volume of instances in a job and to run more complex models.

Predictions written to output files in a Cloud Storage location that you specify.

You can load data with nested and repeated fields using the Web UI.

You cannot use the Web UI to:

- Upload a file greater than 10 MB in size

- Upload multiple files at the same time

- Upload a file in SQL format

All three of the above operations can be performed using the "bq" command.

Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.

To deploy a batch pipeline in Dataflow that adheres to the organizational constraint of using only internal IP addresses, ensuring Private Google Access is the most effective solution. Here’s why option D is the best choice:

Private Google Access:

Private Google Accessallows resources in a VPC network that do not have external IP addresses to access Google APIs and services through internal IP addresses.

This ensures compliance with the organizational constraint of using only internal IPs while allowing Dataflow to access Cloud Storage and BigQuery.

Dataflow with Internal IPs:

Dataflow can be configured to use only internal IP addresses for its worker nodes, ensuring that no external IP addresses are assigned.

This configuration ensures secure and compliant communication between Dataflow, Cloud Storage, and BigQuery.

Firewall and Network Configuration:

Enabling Private Google Access requires ensuring the correct firewall rules and network configurations to allow internal traffic to Google Cloud services.

Steps to Implement:

Enable Private Google Access:

Enable Private Google Access on the subnetwork used by the Dataflow pipeline

gcloud compute networks subnets update [SUBNET_NAME] \

--region [REGION] \

--enable-private-ip-google-access

Configure Dataflow:

Configure the Dataflow job to use only internal IP addresses

gcloud dataflow jobs run [JOB_NAME] \

--region [REGION] \

--network [VPC_NETWORK] \

--subnetwork [SUBNETWORK] \

--no-use-public-ips

Verify Access:

Ensure that firewall rules allow the necessary traffic from the Dataflow workers to Cloud Storage and BigQuery using internal IPs.

Reference Links:

Private Google Access Documentation

Configuring Dataflow to Use Internal IPs

VPC Firewall Rules