Google Professional-Data-Engineer Free Certification Exam Questions Answer Dec 2025 update

Question # 51

Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

1. Create one lake for each team. Inside each lake, create one zone for each domain.2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.3. Have the central data platform team manage all zones' data assets.

1 Create one lake for each team. Inside each lake, create one zone for each domain.2. Attach each to the BigQuory datasets created by the individual teams as assets to the respective zone.3. Direct each domain to manage their own zone's data assets.

1 Create one lake for each domain. Inside each lake, create one zone for each team.2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.3. Direct each domain to manage their own lake's data assets.

1 Create one lake for each domain. Inside each lake, create one zone for each team.2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.3. Have the central data platform team manage all lakes' data assets.

Question # 52

Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.

Use a Cloud SQL for PostgreSQL highly available instance first, and 8»gtable with US. Europe, and Asiareplication alter securing funding

Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.

Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.

Question # 53

You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm.

To do this you need to add a synthetic feature. What should the value of that feature be?

X^2+Y^2

X^2

Y^2

cos(X)

Question # 54

You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.

Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.

Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

Question # 55

You are developing an application on Google Cloud that will automatically generate subject labels for users’ blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

Call the Cloud Natural Language API from your application. Process the generated Entity Analysis aslabels.

Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

Build and train a text classification model using TensorFlow. Deploy the model using Cloud MachineLearning Engine. Call the model from your application and process the results as labels.

Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

Question # 56

Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).

What should you do?

Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

Add a try… catch block to your DoFn that transforms the data, extract erroneous rows from logs.

Add a try… catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Add a try… catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

Question # 57

You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

Use Cloud ML Engine for training existing Spark ML models

Rewrite your models on TensorFlow, and start using Cloud ML Engine

Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery

Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

Question # 58

You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

Cloud Scheduler

Cloud Dataflow

Cloud Functions

Cloud Composer

Question # 59

You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?

Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.

Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.

Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.

Use customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage. Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.

Explanation:

The core requirements are to protect sensitive data elements (data privacy) while retainingalldata for potential future use, and then using this preprocessed data for consumer analyses.

Retaining All Data:This immediately makes option B (remove sensitive fields) unsuitable because it involves data loss.

Protecting Sensitive Data for Analysis & Future Use:Masking is a de-identification technique that redacts or replaces sensitive data with a substitute, allowing the data structure and usability for analysis to be maintained without exposing the original sensitive values. This aligns with protecting data while still making it usable.

Cloud Data Loss Prevention (DLP) API:This service is specifically designed to discover, classify, and protect sensitive data. It offers various de-identification techniques, including masking.

Dataflow:This is a serverless, fast, and cost-effective service for unified stream and batch data processing. It's well-suited for transforming large datasets, such as those read from Cloud Storage, and can integrate with the DLP API for de-identification.

Writing to BigQuery:BigQuery is an ideal destination for an organization-wide dataset for consumer analyses.

Therefore, using Dataflow to read the data from Cloud Storage, leveraging the Cloud DLP API tomask(a form of de-identification) the sensitive elements, and then writing the processed (masked) data to BigQuery is the most appropriate solution. This approach protects privacy for the consumer analyses dataset while the original, unaltered data can still be retained in the restricted Cloud Storage bucket for future use cases that might require access to the original sensitive information (under strict governance).

Let's analyze why other options are less suitable:

Option B:"Remove sensitive fields" means data loss, which contradicts the requirement to retain all data for potential future use cases.

Option C:Encrypting sensitive fields with Cloud KMS and writing them to BigQuery is a valid way to protect data. However, for "consumer analyses," masked data is generally more directly usable than encrypted data. Analysts would typically work with de-identified (e.g., masked) data rather than directly querying encrypted fields and managing decryption keys for analytical purposes. While decryption is possible, masking often provides a better balance of privacy and utility for broad analysis. The question also implies creating a datasetforanalysis, where masking makes the data ready-to-use for that purpose. The original data remains in Cloud Storage.

Option D:Using CMEK encrypts the entire object in Cloud Storage at rest. While this protects the data in Cloud Storage, federated queries from BigQuery would access the raw, unmasked data (assuming decryption occurs seamlessly). This doesn't address the preprocessing requirement of protectingcertain sensitive data elementswithin the data itself for theconsumer analysesdataset. The goal is to create a de-identified dataset for analysis, not just secure the raw data at rest.

[Reference:, Google Cloud Documentation: Cloud Data Loss Prevention > De-identification overview. "De-identification is the process of removing identifying information from data. Cloud DLP uses de-identification techniques such as masking, tokenization, pseudonymization, date shifting, and more to help you protect sensitive data.", Google Cloud Documentation: Cloud Data Loss Prevention > Basic de-identification > Masking. "Masking hides parts of data by replacing characters with a symbol, such as an asterisk (*) or hash (#).", Google Cloud Documentation: Dataflow > Overview. "Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.", Google Cloud Solution: Automating the de-identification of PII in large-scale datasets using Cloud DLP and Dataflow. This solution guide explicitly outlines using Dataflow and DLP API for de-identifying (including masking) data from Cloud Storage and loading it into BigQuery. "You can use Cloud DLP to scan data for sensitive elements andthen apply de-identification techniques such as redaction, masking, or tokenization." and "This tutorial uses Dataflow to orchestrate the de-identification process.", , , ]

Question # 60

You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.

What should you do?

Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read fron PubSub and write to GCS.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.

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Free Practice Questions for Google Professional-Data-Engineer Exam

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