Online Access Free Google.Professional-Data-Engineer.v2026-04-15.q159 Practice Test (Page 11)

Professional-Data-Engineer Exam Question 46

You are building a teal-lime prediction engine that streams files, which may contain Pll (personal identifiable information) data, into Cloud Storage and eventually into BigQuery You want to ensure that the sensitive data is masked but still maintains referential Integrity, because names and emails are often used as join keys How should you use the Cloud Data Loss Prevention API (DLP API) to ensure that the Pll data is not accessible by unauthorized individuals?

A.Redact all Pll data, and store a version of the unredacted data in a locked-down bucket

B.Create a pseudonym by replacing Pll data with a cryptographic format-preserving token

C.Scan every table in BigQuery, and mask the data it finds that has Pll

D.Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the non-tokenized data in a locked-down button.

Professional-Data-Engineer Exam Question 47

You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily Your customers' information, such as their preferences, is hosted on a Cloud SQL for MySQL database Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers' information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time you want to keep the load on the Cloud SQL databases to a minimum. What should you do?

A.Create BigQuery connections to both Cloud SQL databases Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.

B.Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.

C.Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.

D.Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.

Correct Answer: A

Datastream is a serverless Change Data Capture (CDC) and replication service that allows you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud Storage, Cloud SQL, and Pub/Sub. Datastream captures and delivers database changes in real-time, with minimal impact on the source database performance. Datastream also preserves the schema and data types of the source database, and automatically creates and updates the corresponding tables in BigQuery.
By using Datastream, you can replicate the required tables from both Cloud SQL databases to BigQuery, and keep them in sync with the source databases. This way, you can reduce the load on the Cloud SQL databases, as the marketing team can run their queries on the BigQuery tables instead of the Cloud SQL tables. You can also leverage the scalability and performance of BigQuery to query the customer behavioral data from Google Analytics and the customer information from the replicated tables. You can run the queries as frequently as needed, without worrying about the impact on the Cloud SQL databases.
Option A is not a good solution, as BigQuery federated queries allow you to query external data sources such as Cloud SQL databases, but they do not reduce the load on the source databases. In fact, federated queries may increase the load on the source databases, as they need to execute the query statements on the external data sources and return the results to BigQuery. Federated queries also have some limitations, such as data type mappings, quotas, and performance issues.
Option C is not a good solution, as creating a Dataproc cluster with Trino would require more resources and management overhead than using Datastream. Trino is a distributed SQL query engine that can connect to multiple data sources, such as Cloud SQL and BigQuery, and execute queries across them. However, Trino requires a Dataproc cluster to run, which means you need to provision, configure, and monitor the cluster nodes. You also need to install and configure the Trino connector for Cloud SQL and BigQuery, and write the queries in Trino SQL dialect. Moreover, Trino does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high.
Option D is not a good solution, as creating a job on Apache Spark with Dataproc Serverless would require more coding and processing power than using Datastream. Apache Spark is a distributed data processing framework that can read and write data from various sources, such as Cloud SQL and BigQuery, and perform complex transformations and analytics on them. Dataproc Serverless is a serverless Spark service that allows you to run Spark jobs without managing clusters. However, Spark requires you to write code in Python, Scala, Java, or R, and use the Spark connector for Cloud SQL and BigQuery to access the data sources. Spark also does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high. References: Datastream overview | Datastream | Google Cloud, Datastream concepts | Datastream | Google Cloud, Datastreamquickstart | Datastream | Google Cloud, Introduction to federated queries | BigQuery | Google Cloud, Trino overview | Dataproc Documentation | Google Cloud, Dataproc Serverless overview | Dataproc Documentation | Google Cloud, Apache Spark overview | Dataproc Documentation | Google Cloud.

Professional-Data-Engineer Exam Question 48

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?

A.Use a row key of the form <timestamp>.

B.Use a row key of the form <timestamp>#<sensorid>.

C.Use a row key of the form >#<sensorid>#<timestamp>.

D.Use a row key of the form <sensorid>.

Professional-Data-Engineer Exam Question 49

You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use. and cost-effective. What should you do?

A.Create a normalized model with tables for each entity. Use snapshots before updates to track historical data

B.Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data

C.Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data

D.Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.

Professional-Data-Engineer Exam Question 50

You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

A.Create authorized views for each team in the same dataset in which the data resides, and assign the users
/groups data viewer access to the authorized views

B.Assign the users/groups data viewer access at the table level for each table

C.Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside

D.Create SQL views for each team in the same dataset in which the data resides, and assign the users
/groups data viewer access to the SQL views

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Professional-Data-Engineer Exam Question 46

Professional-Data-Engineer Exam Question 47

Professional-Data-Engineer Exam Question 48

Professional-Data-Engineer Exam Question 49

Professional-Data-Engineer Exam Question 50

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