Pass Your Google Professional-Data-Engineer Exam with Correct 433 Questions and Answers [Q70-Q91]

Pass Your Google Professional-Data-Engineer Exam with Correct 433 Questions and Answers

Latest [Sep 24, 2026] 2026 Realistic Verified Professional-Data-Engineer Dumps

Google Professional-Data-Engineer exam is designed to test the skills and knowledge of professionals who work with Google Cloud Platform. It is designed for those who have experience in designing, building, and managing data processing systems, and for those who work with machine learning models and data analysis tools. Professional-Data-Engineer exam is designed to test the ability of professionals to use Google Cloud Platform tools to develop and implement solutions that meet the needs of businesses.

Google Professional-Data-Engineer Certification Exam is a highly prestigious certification program offered by Google for individuals who want to establish themselves as professional data engineers. Google Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design, build, operationalize, secure, and monitor data processing systems. It is designed for individuals who have experience working with data processing systems, data warehousing, and data analysis technologies.

 

NEW QUESTION # 70
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud.
You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• A. Deploy a Cloud Dataproc cluster.
  Use an SSD persistent disk and 50% preemptible workers.
  Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• B. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.
  Store data in HDFS.Change references in scripts from hdfs:// to gs://
• C. Deploy a Cloud Dataproc cluster.
  Use a standard persistent disk and 50% preemptible workers.
  Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• D. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances.
  Install the Cloud Storage connector, and store the data in Cloud Storage.
  Change references in scripts from hdfs:// to gs://

Answer: C

Explanation:
Ask for cost effective so persistent disk are HDD which are cheaper in comparison to SSD.

NEW QUESTION # 71
You have enabled the free integration between Firebase Analytics and Google BigQuery.
Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL.
What should you do?

• A. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
• B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
• C. Use the WHERE_PARTITIONTIME pseudo column
• D. Use the TABLE_DATE_RANGE function

Answer: D

Explanation:
Legacy sql uses table date range whereas standard sql uses table_sufix for wildcard.

NEW QUESTION # 72
When you design a Google Cloud Bigtable schema it is recommended that you _________.

• A. Create schema designs that require atomicity across rows
• B. Avoid schema designs that require atomicity across rows
• C. Create schema designs that are based on a relational database design
• D. Avoid schema designs that are based on NoSQL concepts

Answer: B

Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.

NEW QUESTION # 73
You are a BigQuery admin supporting a team of data consumers who run ad hoc queries and downstream reporting in tools such as Looker. All data and users are combined under a single organizational project. You recently noticed some slowness in query results and want to troubleshoot where the slowdowns are occurring.
You think that there might be some job queuing or slot contention occurring as users run jobs, which slows down access to results. You need to investigate the query job information and determine where performance is being affected. What should you do?

• A. Use Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.
• B. Use available administrative resource charts to determine how slots are being used and how jobs are performing over time. Run a query on the INFORMATION_SCHEMA to review query performance.
• C. Use slot reservations for your project to ensure that you have enough query processing capacity and are able to allocate available slots to the slower queries.
• D. Use Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.

Answer: B

Explanation:
To troubleshoot query performance issues related to job queuing or slot contention in BigQuery, using administrative resource charts along with querying the INFORMATION_SCHEMA is the best approach.
Here's why option D is the best choice:
* Administrative Resource Charts:
* BigQuery provides detailed resource charts that show slot usage and job performance over time.
These charts help identify patterns of slot contention and peak usage times.
* INFORMATION_SCHEMA Queries:
* The INFORMATION_SCHEMA tables in BigQuery provide detailed metadata about query jobs, including execution times, slots consumed, and other performance metrics.
* Running queries on INFORMATION_SCHEMA allows you to pinpoint specific jobs causing contention and analyze their performance characteristics.
* Comprehensive Analysis:
* Combining administrative resource charts with detailed queries on INFORMATION_SCHEMA provides a holistic view of the system's performance.
* This approach enables you to identify and address the root causes of performance issues, whether they are due to slot contention, inefficient queries, or other factors.
Steps to Implement:
* Access Administrative Resource Charts:
* Use the Google Cloud Console to view BigQuery's administrative resource charts. These charts provide insights into slot utilization and job performance metrics over time.
* Run INFORMATION_SCHEMA Queries:
* Execute queries on BigQuery's INFORMATION_SCHEMA to gather detailed information about job performance. For example:
SELECT
creation_time,
job_id,
user_email,
query,
total_slot_ms / 1000 AS slot_seconds,
total_bytes_processed / (1024 * 1024 * 1024) AS processed_gb,
total_bytes_billed / (1024 * 1024 * 1024) AS billed_gb
FROM
`region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE
creation_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY)
AND state = 'DONE'
ORDER BY
slot_seconds DESC
LIMIT 100;
* Analyze and Optimize:
* Use the information gathered to identify bottlenecks, optimize queries, and adjust resource allocations as needed to improve performance.
Reference Links:
* Monitoring BigQuery Slots
* BigQuery INFORMATION_SCHEMA
* BigQuery Performance Best Practices

NEW QUESTION # 74
You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets, and storing the final curated data product in BigQuery datasets. You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

• A. 1. Create a single Dataplex virtual lake and create a single zone to contain landing, raw, and curated data.
  2. Provide each data engineering team access to the virtual lake.
• B. 1. Create a single Dataplex virtual lake and create a single zone to contain landing, raw, and curated data.
  2. Build separate assets for each data product within the zone.
  3. Assign permissions to the data engineering teams at the zone level.
• C. 1. Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.
  2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
• D. 1. Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw, and curated data.
  2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

Answer: D

NEW QUESTION # 75
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?

• A. Masters, workers, and parameter servers
• B. Workers and parameter servers
• C. Parameter servers
• D. Workers

Answer: B

Explanation:
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node. You may set TrainingInput.workerCount to specify the number of workers to use. You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use. You can specify the type of machine for the master node, but you can't specify more than one master node.
Reference: https://cloud.google.com/ml-engine/docs/training-overview#job_configuration_parameters

NEW QUESTION # 76
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?

• A. cos(X)
• B. X2+Y2
• C. X2
• D. Y2

Answer: B

NEW QUESTION # 77
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids.
Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

• A. Create a file on a shared file and have the application servers write all bid events to that file.
  Process the file with Apache Hadoop to identify which user bid first.
• B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
• C. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
• D. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Answer: C

Explanation:
From Cloud SQL we can fetch the record on timestamp basis using where clause and it satisfies near real time.

NEW QUESTION # 78
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?

• A. Create stratified random samples using the OVER() function and compare equivalent samples from each table.
• B. 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.
• C. Select random samples from the tables using the HASH() function and compare the samples.
• D. Select random samples from the tables using the RAND() function and compare the samples.

Answer: C

NEW QUESTION # 79
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

• A. Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.
• B. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
• C. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.
• D. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.

Answer: C

NEW QUESTION # 80
An organization maintains a Google BigQuery dataset that contains tables with user-level data. 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?

• A. Create and share an authorized view that provides the aggregate results.
• B. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
• C. Create and share a new dataset and view that provides the aggregate results.
• D. Create and share a new dataset and table that contains the aggregate results.

Answer: B

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/bigquery/docs/access-control

NEW QUESTION # 81
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.)

• A. Restrict BigQuery API access to approved users.
• B. Segregate data across multiple tables or databases.
• C. Restrict access to tables by role.
• D. Ensure that the data is encrypted at all times.
• E. Use Google Stackdriver Audit Logging to determine policy violations.
• F. Disable writes to certain tables.

Answer: A,C,E

Explanation:
Explanation/Reference:

NEW QUESTION # 82
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.)

• A. Restrict BigQuery API access to approved users.
• B. Segregate data across multiple tables or databases.
• C. Restrict access to tables by role.
• D. Ensure that the data is encrypted at all times.
• E. Use Google Stackdriver Audit Logging to determine policy violations.
• F. Disable writes to certain tables.

Answer: A,C,E

NEW QUESTION # 83
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

• A. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
• B. The [myproject:mydataset.mytable] table has too many partitions
• C. Users are running too many concurrent queries in the system
• D. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

Answer: A

NEW QUESTION # 84
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

• A. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
• B. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
• C. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
• D. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage.Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Answer: A

Explanation:
Timestamp at starting of rowkey causes bottleneck issues.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#prefer_rows_to_column_versions

NEW QUESTION # 85
Which is not a valid reason for poor Cloud Bigtable performance?

• A. The table's schema is not designed correctly.
• B. The Cloud Bigtable cluster has too many nodes.
• C. The workload isn't appropriate for Cloud Bigtable.
• D. There are issues with the network connection.

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.

NEW QUESTION # 86
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

• A. Streaming job, PubSubIO, BigQueryIO, side-outputs
• B. Batch job, PubSubIO, side-inputs
• C. Streaming job, PubSubIO, BigQueryIO, side-inputs
• D. Streaming job, PubSubIO, JdbcIO, side-outputs

Answer: C

NEW QUESTION # 87
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

• A. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
• B. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
• C. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.
• D. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

Answer: C

NEW QUESTION # 88
You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?

• A. Install the StackDriver Agent and configure the MySQL plugin.
• B. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
• C. Place the MariaDB instances in an Instance Group with a Health Check.
• D. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

Answer: D

Explanation:
The GitHub repository named google-fluentd-catch-all-config which includes the configuration files for the Logging agent for ingesting the logs from various third-party software packages.

NEW QUESTION # 89
Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

• A. Cloud Composer
• B. Cloud Dataflow
• C. Cloud Dataproc
• D. Cloud Dataprep

Answer: C

NEW QUESTION # 90
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority
of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the
cost of queries, your organization created a view called events, which queries only the last 14 days of
data. The view is described in legacy SQL. Next month, existing applications will be connecting to
BigQuery to read the eventsdata via an ODBC connection. You need to ensure the applications can
connect. Which two actions should you take? (Choose two.)

• A. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection
  and shared "events"
• B. Create a service account for the ODBC connection to use for authentication
• C. Create a new view over events_partitioned using standard SQL
• D. Create a new partitioned table using a standard SQL query
• E. Create a new view over events using standard SQL

Answer: A,E

NEW QUESTION # 91
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