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The Google Professional-Data-Engineer Exam for the Google Professional-Data-Engineer certification is a comprehensive and challenging test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions, which require candidates to apply their knowledge to real-world scenarios. Candidates are required to demonstrate their expertise in areas such as data processing, data analysis, data integration, and data visualization.

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Google Professional-Data-Engineer Certification Exam is designed to test the knowledge and skills of candidates in the field of data engineering. Professional-Data-Engineer exam is intended for professionals who are responsible for designing, building, and maintaining data processing systems. Professional-Data-Engineer exam is designed to validate the candidate's ability to use Google Cloud Platform technologies to design and implement data processing systems, to build and maintain data structures and databases, and to analyze and optimize data processing workflows.

Google Professional-Data-Engineer exam is a rigorous test of an individual's skills and knowledge in data engineering on Google Cloud technologies. As demand for skilled data engineering professionals continues to grow, the certification can open up many lucrative job opportunities for those looking to make their mark in the industry. Google Certified Professional Data Engineer Exam certification process requires practical experience, extensive preparation, and dedication to acquiring skills that are highly valued in today's rapidly evolving technology ecosystem.

Google Certified Professional Data Engineer Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

A. Test and evaluate your model on your curated data to determine how well the model performs.
B. Monitor your model performance, and make any adjustments needed.
C. Use your model to run predictions on fresh customer input data.
D. Delineate what data will be used for testing and what will be used for training the model.

Answer: A

Explanation:
After processing your data, the next step in the model development lifecycle is to test and evaluate your model on the curated data. This is crucial to determine the performance of the model and to understand how well it can predict sales conversions for your customers. The evaluation phase involves using various metrics and techniques to assess the accuracy, precision, recall, and other relevant performance indicators of the model. It helps in identifying any issues or areas for improvement before deploying the model in a production environment. References: The information provided here is verified by the Google Professional Data Engineer Certification Exam Guide and related resources, which outline the steps and best practices in the model development lifecycle

NEW QUESTION # 125
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
B. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
C. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.
D. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the
default autoscaling setting for worker instances.

Answer: B

NEW QUESTION # 126
You have terabytes of customer behavioral data streaming from Google Analytics into BigQuery daily Your customers' information, such as their preferences, is hosted on a Cloud SQL for MySQL database Your CRM database is hosted on a Cloud SQL for PostgreSQL instance. The marketing team wants to use your customers' information from the two databases and the customer behavioral data to create marketing campaigns for yearly active customers. You need to ensure that the marketing team can run the campaigns over 100 times a day on typical days and up to 300 during sales. At the same time you want to keep the load on the Cloud SQL databases to a minimum. What should you do?

A. Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries.
B. Create BigQuery connections to both Cloud SQL databases Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.
C. Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.
D. Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries.

Answer: C

Explanation:
Datastream is a serverless Change Data Capture (CDC) and replication service that allows you to stream data changes from Oracle and MySQL databases to Google Cloud services such as BigQuery, Cloud Storage, Cloud SQL, and Pub/Sub. Datastream captures and delivers database changes in real-time, with minimal impact on the source database performance. Datastream also preserves the schema and data types of the source database, and automatically creates and updates the corresponding tables in BigQuery.
By using Datastream, you can replicate the required tables from both Cloud SQL databases to BigQuery, and keep them in sync with the source databases. This way, you can reduce the load on the Cloud SQL databases, as the marketing team can run their queries on the BigQuery tables instead of the Cloud SQL tables. You can also leverage the scalability and performance of BigQuery to query the customer behavioral data from Google Analytics and the customer information from the replicated tables. You can run the queries as frequently as needed, without worrying about the impact on the Cloud SQL databases.
Option A is not a good solution, as BigQuery federated queries allow you to query external data sources such as Cloud SQL databases, but they do not reduce the load on the source databases. In fact, federated queries may increase the load on the source databases, as they need to execute the query statements on the external data sources and return the results to BigQuery. Federated queries also have some limitations, such as data type mappings, quotas, and performance issues.
Option C is not a good solution, as creating a Dataproc cluster with Trino would require more resources and management overhead than using Datastream. Trino is a distributed SQL query engine that can connect to multiple data sources, such as Cloud SQL and BigQuery, and execute queries across them. However, Trino requires a Dataproc cluster to run, which means you need to provision, configure, and monitor the cluster nodes. You also need to install and configure the Trino connector for Cloud SQL and BigQuery, and write the queries in Trino SQL dialect. Moreover, Trino does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high.
Option D is not a good solution, as creating a job on Apache Spark with Dataproc Serverless would require more coding and processing power than using Datastream. Apache Spark is a distributed data processing framework that can read and write data from various sources, such as Cloud SQL and BigQuery, and perform complex transformations and analytics on them. Dataproc Serverless is a serverless Spark service that allows you to run Spark jobs without managing clusters. However, Spark requires you to write code in Python, Scala, Java, or R, and use the Spark connector for Cloud SQL and BigQuery to access the data sources. Spark also does not replicate or sync the data from Cloud SQL to BigQuery, so the load on the Cloud SQL databases would still be high. References: Datastream overview | Datastream | Google Cloud, Datastream concepts | Datastream | Google Cloud, Datastream quickstart | Datastream | Google Cloud, Introduction to federated queries | BigQuery | Google Cloud, Trino overview | Dataproc Documentation | Google Cloud, Dataproc Serverless overview | Dataproc Documentation | Google Cloud, Apache Spark overview | Dataproc Documentation | Google Cloud.

NEW QUESTION # 127
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

A. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
B. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
C. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
D. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Answer: A

NEW QUESTION # 128
You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards for example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the dat a. You want a quick solution that requires no coding What should you do?

A. Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
B. Create a Spark job and submit it to Dataproc Serverless.
C. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
D. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring quenes in BigQuery.

Answer: C

Explanation:
Cloud Data Fusion is a fully managed, cloud-native data integration service that allows you to build and manage data pipelines with a graphical interface. Wrangler is a feature of Cloud Data Fusion that enables you to interactively explore, clean, and transform data using a spreadsheet-like UI. You can use Wrangler to normalize the data in BigQuery by applying various directives, such as parsing, formatting, replacing, and validating data. You can also preview the results and export the wrangled data to BigQuery or other destinations. You can then set up a recurring job in Cloud Data Fusion to run the Wrangler pipeline on a schedule, such as weekly or daily. This way, you can create a quick and code-free solution to normalize the data for your reports. Reference:
Cloud Data Fusion overview
Wrangler overview
Wrangle data from BigQuery
[Scheduling pipelines]

NEW QUESTION # 129
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