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Professional-Data-Engineer Google Professional Data Engineer Exam Free Practice Exam Questions (2025 Updated)

Prepare effectively for your Google Professional-Data-Engineer Google Professional Data Engineer Exam certification with our extensive collection of free, high-quality practice questions. Each question is designed to mirror the actual exam format and objectives, complete with comprehensive answers and detailed explanations. Our materials are regularly updated for 2025, ensuring you have the most current resources to build confidence and succeed on your first attempt.

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You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?

A.

The message body for the sensor event is too large.

B.

Your custom endpoint has an out-of-date SSL certificate.

C.

The Cloud Pub/Sub topic has too many messages published to it.

D.

Your custom endpoint is not acknowledging messages within the acknowledgement deadline.

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A.

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

B.

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

C.

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

D.

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

E.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

A.

Update the current pipeline and use the drain flag.

B.

Update the current pipeline and provide the transform mapping JSON object.

C.

Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.

D.

Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

A.

Linear regression

B.

Logistic classification

C.

Recurrent neural network

D.

Feedforward neural network

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

A.

Continuously retrain the model on just the new data.

B.

Continuously retrain the model on a combination of existing data and the new data.

C.

Train on the existing data while using the new data as your test set.

D.

Train on the new data while using the existing data as your test set.

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?

A.

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

B.

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

C.

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

D.

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

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

A.

Redefine the schema by evenly distributing reads and writes across the row space of the table.

B.

The performance issue should be resolved over time as the site of the BigDate cluster is increased.

C.

Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

D.

Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

A.

Put the data into Google Cloud Storage.

B.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

C.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

D.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

A.

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

B.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

C.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

D.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

A.

Implement clustering in BigQuery on the ingest date column.

B.

Implement clustering in BigQuery on the package-tracking ID column.

C.

Tier older data onto Cloud Storage files, and leverage extended tables.

D.

Re-create the table using data partitioning on the package delivery date.

You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

A.

Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

B.

Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

C.

Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.

D.

Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

A.

Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

B.

Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

C.

Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

D.

Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

A.

Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

B.

Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.

C.

Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

D.

Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

You are designing a pipeline that publishes application events to a Pub/Sub topic. You need to aggregate events across hourly intervals before loading the results to BigQuery for analysis. Your solution must be scalable so it can process and load large volumes of events to BigQuery. What should you do?

A.

Create a streaming Dataflow job to continually read from the Pub/Sub topic and perform the necessary aggregations using tumbling windows

B.

Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub-Sub topic and performing the necessary aggregations

C.

Schedule a Cloud Function to run hourly, pulling all avertable messages from the Pub/Sub topic and performing the necessary aggregations

D.

Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.

You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

A.

1 Use a regional Cloud Storage bucket2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by one day to recover the acknowledged messages4 Start the Dataflow job in a secondary region and write in a bucket in the same region

B.

1 Use a multi-regional Cloud Storage bucket2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages4 Start the Dataflow job in a secondary region

C.

1. Use a dual-region Cloud Storage bucket.2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages4 Start the Dataflow job in a secondary region

D.

1. Use a dual-region Cloud Storage bucket with turbo replication enabled2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages4 Start the Dataflow job in a secondary region.

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A.

Add a SideInput that returns a Boolean if the element is corrupt.

B.

Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

C.

Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

D.

Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:

Executing the transformations on a schedule

Enabling non-developer analysts to modify transformations

Providing a graphical tool for designing transformations

What should you do?

A.

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

B.

Load each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

C.

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data’s schema changes

D.

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

Your neural network model is taking days to train. You want to increase the training speed. What can you do?

A.

Subsample your test dataset.

B.

Subsample your training dataset.

C.

Increase the number of input features to your model.

D.

Increase the number of layers in your neural network.

As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A.

Use Cloud Deployment Manager to automate access provision.

B.

Introduce resource hierarchy to leverage access control policy inheritance.

C.

Create distinct groups for various teams, and specify groups in Cloud IAM policies.

D.

Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

E.

For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

A.

Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.

B.

Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.

C.

Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.

D.

Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.

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