Updated Aug 28, 2026 Professional-Data-Engineer Exam Dumps – PDF Questions and Testing Engine [Q204-Q223]

Rate this post

Updated Aug 28, 2026 Professional-Data-Engineer Exam Dumps – PDF Questions and Testing Engine

New (2026) Google Professional-Data-Engineer Exam Dumps

Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	28%	– Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads – Security and governance 1. Data governance and compliance 2. IAM and access control in GCP
Building and operationalizing data processing systems	24%	– Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL – Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
Operationalizing machine learning models	26%	– ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment – Model deployment and monitoring 1. Online vs batch prediction 2. Model monitoring and drift detection
Designing data processing systems	22%	– Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures – Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)

NO.204 You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

NO.205 You need to modernize your existing on-premises data strategy. Your organization currently uses:
– Apache Hadoop clusters for processing multiple large data sets,
including on-premises Hadoop Distributed File System (HDFS) for data
replication.
– Apache Airflow to orchestrate hundreds of ETL pipelines with
thousands of job steps.
You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.

Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

NO.206 Case Study: 2 – MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments ?development/test, staging, and production ?
to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud’s machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You need to compose visualizations for operations teams with the following requirements:
Which approach meets the requirements?

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

NO.207 You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a
machine-learning process. You want to support a logistic regression model. You also need to monitor and
adjust for null values, which must remain real-valued and cannot be removed. What should you do?

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to ‘none’ using a Cloud
Dataproc job.

Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud
Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to ‘none’ using a Cloud
Dataprep job.

Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.

NO.208 An organization maintains a Google BigQuery dataset that contains tables with user-level dat A.
They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

Create and share an authorized view that provides the aggregate results.

Create and share a new dataset and view that provides the aggregate results.

Create and share a new dataset and table that contains the aggregate results.

Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

NO.209 Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

Increase the CPU size on your server.

Increase the size of the Google Persistent Disk on your server.

Increase your network bandwidth from your datacenter to GCP.

Increase your network bandwidth from Compute Engine to Cloud Storage.

NO.210 What are the minimum permissions needed for a service account used with Google Dataproc?

Execute to Google Cloud Storage; write to Google Cloud Logging

Write to Google Cloud Storage; read to Google Cloud Logging

Execute to Google Cloud Storage; execute to Google Cloud Logging

Read and write to Google Cloud Storage; write to Google Cloud Logging

NO.211 Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

1Create a project for storage of the data for your organization.2 Create a central Cloud Storage bucket with three folders to store the files for each department.3. Create a central BigQuery dataset with tables prefixed with the department name.4 Give viewer rights for the storage project for the users of your departments.

1Create a project for storage of the data for each of your departments.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Create user groups for authorized readers for each bucket and dataset.4 Enable the IT team to administer the user groups to add or remove users as the departments’ request.

1 Create multiple projects for storage of the data for each of your departments’ applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Publish the data that each department shared in Analytics Hub.4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.

1 Create multiple projects for storage of the data for each of your departments’ applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones.4 Enable each department to own and share the data of their data lakes.

NO.212 You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

Build and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.

Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences.
Deploy the models using Cloud Dataproc. Call the models from your application.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.

NO.213 You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

Run a scheduled query to pull the necessary data at specific intervals daily.

Create a materialized view based off of the query being run.

Use a cached query to accelerate time to results.

Limit the query columns being pulled in the final result.

NO.214 You need to choose a database to store time series CPU and memory usage for millions of computers.
You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

Create a table in BigQuery, and append the new samples for CPU and memory to the table

Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second

Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

NO.215 You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available.
You need to define a storage, backup, and recovery strategy of this data that minimizes cost.
How should you configure the BigQuery table?

Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

NO.216 You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow You want to improve the performance of these queries What should you do?

Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.

Change the storage class of the Hive partitioned data objects from Coldline to Standard.

Upgrade the external table to a BigLake table Enable metadata caching for the table.

NO.217 You are responsible for writing your company’s ETL pipelines to run on an Apache Hadoop cluster. The
pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the
pipelines?

PigLatin using Pig

HiveQL using Hive

Java using MapReduce

Python using MapReduce

NO.218 You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on- premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

NO.219 Your company is in a highly regulated industry. One of your requirements is to ensure individual users
have access only to the minimum amount of information required to do their jobs. You want to enforce this
requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

Disable writes to certain tables.

Restrict access to tables by role.

Ensure that the data is encrypted at all times.

Restrict BigQuery API access to approved users.

Segregate data across multiple tables or databases.

Use Google Stackdriver Audit Logging to determine policy violations.

NO.220 You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?

Exceptions in worker code

Job validation

Graph or pipeline construction

Insufficient permissions

NO.221 You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?

1 Create a BigQuery table clone.
2. Query the clone when you need to perform analytics.

1 Create a BigQuery table snapshot.
2 Restore the snapshot when you need to perform analytics.

1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Enable versionmg on the bucket.
3. Create a BigQuery external table on the exported files.

1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Set a locked retention policy on the bucket.
3. Create a BigQuery external table on the exported files.

NO.222 How would you query specific partitions in a BigQuery table?

Use the DAY column in the WHERE clause

Use the EXTRACT(DAY) clause

Use the __PARTITIONTIME pseudo-column in the WHERE clause

Use DATE BETWEEN in the WHERE clause

NO.223 You need to choose a database for a new project that has the following requirements:
* Fully managed
* Able to automatically scale up
* Transactionally consistent
* Able to scale up to 6 TB
* Able to be queried using SQL
Which database do you choose?

Cloud SQL

Cloud Bigtable

Cloud Spanner

Cloud Datastore