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To become a Google Certified Professional Data Engineer, a candidate must pass the certification exam, which costs $200. Professional-Data-Engineer exam is available in English, Japanese, and Spanish and can be taken online or at a testing center. Professional-Data-Engineer exam is valid for two years, after which a candidate must recertify to maintain their certification.

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.

Google Professional-Data-Engineer Certification Exam is a highly recognized certification exam for professionals who want to validate their skills and expertise in data engineering. Professional-Data-Engineer exam is designed to test the candidates' knowledge of Google Cloud Platform (GCP) and its data engineering services. It requires a deep understanding of various data tooling, technologies, and services used to build and maintain data pipelines, data processing systems, and machine learning models.

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Google Certified Professional Data Engineer Exam Sample Questions (Q273-Q278):

NEW QUESTION # 273
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?

A. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
B. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
C. Add capacity (memory and disk space) to the database server by the order of 200.
D. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Answer: B

NEW QUESTION # 274
Your company is using WHILECARD tables 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] SELECT age FROM bigquery-public-data.noaa_gsod.gsod WHERE age != 99 AND_TABLE_SUFFIX = `1929' ORDER BY age DESC Which table name will make the SQL statement work correctly?

A. `bigquery-public-data.noaa_gsod.gsod*`
B. bigquery-public-data.noaa_gsod.gsod*
C. `bigquery-public-data.noaa_gsod.gsod'*
D. `bigquery-public-data.noaa_gsod.gsod`

Answer: B

NEW QUESTION # 275
Which of these is NOT a way to customize the software on Dataproc cluster instances?

A. Log into the master node and make changes from there
B. Configure the cluster using Cloud Deployment Manager
C. Modify configuration files using cluster properties
D. Set initialization actions

Answer: B

Explanation:
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster. When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/ docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties

NEW QUESTION # 276
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?

A. Create an API using App Engine to receive and send messages to the applications
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud Spanner, and insert and delete rows with the job information
D. Create a table on Cloud SQL, and insert and delete rows with the job information

Answer: B

Explanation:
Pubsub is used to transmit data in real time and scale automatically.

NEW QUESTION # 277
You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

A. Store and process the entire dataset in BigQuery.
B. Store and process the entire dataset in Cloud Bigtable.
C. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
D. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.

Answer: D

NEW QUESTION # 278
......

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