Prepare Important Exam with Professional-Data-Engineer Exam Dumps(2025) [Q36-Q60]

Posted On November 26, 2025

Prepare Important Exam with Professional-Data-Engineer Exam Dumps(2025)

Pass Exam Questions Efficiently With Professional-Data-Engineer Questions

Google Professional-Data-Engineer certification exam is designed to assess the skills and knowledge of candidates in various areas related to data engineering. Professional-Data-Engineer exam covers topics such as data processing architecture, data modeling, data ingestion, data transformation, and data storage. Candidates are also expected to have a strong understanding of Google Cloud technologies, including BigQuery, Cloud Storage, and Dataflow.

QUESTION 36
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Use the NOW () function in BigQuery to record the event’s time.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

QUESTION 37
You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery. use the connection to the Cloud Data Loss Prevention {Cloud DLP} API to de-identify the necessary data.

Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming.batch processing, and Cloud DLP Select BigQuery as your data sink.

Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the datainto BigQuery.

Set up Datastream to replicate your on-premise data on BigQuery.

QUESTION 38
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.

Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

QUESTION 39
You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet.
What should you do?

Update your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the Simba. googlebigqueryodbc. ini tile to point to your data center’s NAT gateway.

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream. Add those IP addresses to the firewall allowlist of your on-premises data center.
Use IP Allovlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward- SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on- premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.

Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your on- premises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.

QUESTION 40
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

QUESTION 41
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

gcloud ml-engine local train

gcloud ml-engine jobs submit training

gcloud ml-engine jobs submit training local

You can’t run a TensorFlow program on your own computer using Cloud ML Engine .

QUESTION 42
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named(“ReadLogData”)
.from(“clouddataflow-readonly:samples.log_data”)
You want to improve the performance of this data read. What should you do?

Specify the TableReferenceobject in the code.

Use .fromQueryoperation to read specific fields from the table.

Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.

Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.

QUESTION 43
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Use the NOW () function in BigQuery to record the event’s time.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

QUESTION 44
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

Create a cron schedule in Cloud Dataprep.

Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

QUESTION 45
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub
streaming data, one of the important business requirements is to be able to periodically identify the inputs
and their timings during their campaign. Engineers have decided to use windowing and transformation in
Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud
Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

They have not assigned the timestamp, which causes the job to fail

They have not set the triggers to accommodate the data coming in late, which causes the job to fail

They have not applied a global windowing function, which causes the job to fail when the pipeline is
created

They have not applied a non-global windowing function, which causes the job to fail when the pipeline
is created

QUESTION 46
You are designing a cloud-native historical data processing system to meet the following conditions:
The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis

tools including Cloud Dataproc, BigQuery, and Compute Engine.
A streaming data pipeline stores new data daily.

Peformance is not a factor in the solution.

The solution design should maximize availability.

How should you design data storage for this solution?

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.

Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

QUESTION 47
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

Assign global unique identifiers (GUID) to each data entry.

Compute the hash value of each data entry, and compare it with all historical data.

Store each data entry as the primary key in a separate database and apply an index.

Maintain a database table to store the hash value and other metadata for each data entry.

QUESTION 48
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
____.

application node

conditional node

master node

worker node

QUESTION 49
You work for a farming company. You have one BigQuery table named sensors, which is about 500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp. which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

1. Create a retries column in the sensor? table.
2. Set record type and repeated mode for the metrics column.
3. Use an UPDATE statement every 30 seconds to add new metrics.

1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an INSERT statement every 30 seconds to add new metrics.

1. Create a metrics table partitioned by timestamp.
2. Create a sensorld column in the metrics table, that points to the id column in the sensors table.
3. Use an IHSEW statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.

1. Create a metrics table partitioned by timestamp.
2. Create a sensor Id column in the metrics table, that points to the _d column in the sensors table.
3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.

QUESTION 50
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

Create an API using App Engine to receive and send messages to the applications

Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

Create a table on Cloud SQL, and insert and delete rows with the job information

Create a table on Cloud Spanner, and insert and delete rows with the job information

QUESTION 51
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage.
The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

1. Load the data to BigQuery tables.2. Create a taxonomy of policy tags in Data Catalog.3. Add policy tags to columns.4. Process with the Spark-BigQuery connector or BigQuery SQL.

1. Deploy a long-living Dataproc cluster with Apache Hive and Ranger enabled.2. Configure Ranger for column level security.3. Process with Dataproc Spark or Hive SQL.

1. Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage.2. Define a BigQuery external table for SQL processing.3. Use Dataproc Spark to process the Cloud Storage files.

1. Define a BigLake table.2. Create a taxonomy of policy tags in Data Catalog.3. Add policy tags to columns.4. Process with the Spark-BigQuery connector or BigQuery SQL.

QUESTION 52
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

QUESTION 53
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

BETWEEN

WHERE

SELECT

LIMIT

QUESTION 54
Which of these statements about exporting data from BigQuery is false?

To export more than 1 GB of data, you need to put a wildcard in the destination filename.

The only supported export destination is Google Cloud Storage.

Data can only be exported in JSON or Avro format.

The only compression option available is GZIP.

QUESTION 55
You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified You want to cleanse the data n near-reel time before running it through Al models What should you do?

Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasets

Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sink

Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery

QUESTION 56
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings.
Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

Re-write the application to load accumulated data every 2 minutes.

Convert the streaming insert code to batch load for individual messages.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

QUESTION 57
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’the property ‘actors’and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

C: Set the following in your entity options: exclude_from_indexes = ‘actors, tags’ D: Set the following in your entity options: exclude_from_indexes = ‘date_published’

Option A

Option B.

Option C

Option D

QUESTION 58
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Trigger based on element size in bytes

Trigger that is a combination of other triggers

Trigger based on element count

Trigger based on time

QUESTION 59
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

Use Transfer Appliance to copy the data to Cloud Storage

Use gsutil cp -Jto compress the content being uploaded to Cloud Storage

Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic

QUESTION 60
Which TensorFlow function can you use to configure a categorical column if you don’t know all of the possible values for that column?

categorical_column_with_vocabulary_list

categorical_column_with_hash_bucket

categorical_column_with_unknown_values

sparse_column_with_keys

Google Professional-Data-Engineer certification is a valuable credential that can help professionals stand out in the competitive field of data engineering. It demonstrates that the certified individual has the expertise and skills required to design, build, and manage data processing systems effectively. As data becomes increasingly important in organizations of all sizes and industries, the demand for certified data engineers is expected to grow, making this certification a worthwhile investment for individuals looking to advance their career in the field.

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