Google Professional Data Engineer Certification Practice Test

Google Certified Professional – Data Engineer certification requires you answer Professional Data Engineer exam. This exam objectively measures an individual’s ability to demonstrate the critical job skills for the role. Valid Google Professional Data Engineer Certification Practice Test has been released, which ensure your success in Professional Data Engineer certification.

We also share free Professional Data Engineer dumps online:

1. Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data.

However, when tested against new data, it performs poorly.

What method can you employ to address this?


2. 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?


3. You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources.

How should you adjust the database design?


4. You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.

What should you do?


5. An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted.

How should you build this pipeline?


6. Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users.

How should you design the frontend to respond to a database failure?


7. 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?


8. You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data.
Which query type should you use?


9. Your company is using WILDCARDtables to query data across multiple tables with similar names.

The SQL statement is currently failing with the following error:

# Syntax error : Expected end of statement but got “-“ at [4:11]





age != 99



age DESC

Which table name will make the SQL statement work correctly?


10. 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.)


11. You are designing a basket abandonment system for an ecommerce company.

The system will send a message to a user based on these rules:

– No interaction by the user on the site for 1 hour

– Has added more than $30 worth of products to the basket

– Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent.

How should you design the pipeline?


12. Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data.

Which three steps should you take? (Choose three.)


13. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.

Which Google database service should you use?


14. You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples.

Which two characteristic support this method? (Choose two.)


15. 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?


16. Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing.

What should you do first?


17. Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster.

What should you do?


18. Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data.

Which three machine learning applications can you use? (Choose three.)


19. 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?


20. 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?


21. 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?


22. Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks.

What should you do?


23. You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time.

What should you do?