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Preparing for the exam requires a combination of hands-on experience with Google Cloud Platform, as well as studying relevant documentation and training materials. Google offers a variety of resources for exam preparation, including online courses, hands-on labs, and practice exams.

Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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Google Certified Professional Data Engineer Exam Sample Questions (Q284-Q289):

NEW QUESTION # 284
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

A. Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
B. Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
C. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
D. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Answer: B

NEW QUESTION # 285
Your company is using WILDCARD 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: D

NEW QUESTION # 286
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 the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as
80% warm and 20% active.
B. Store and process the entire dataset in BigQuery.
C. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
D. Store and process the entire dataset in Cloud Bigtable.

Answer: A

NEW QUESTION # 287
You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

A. BigQuery Data Transfer Service for the migration, Pub/Sub and Dataproc for the real-time updates
B. Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates
C. gsutil for both the migration and the real-time updates
D. gsutil for the migration; Pub/Sub and Dataflow for the real-time updates

Answer: B

NEW QUESTION # 288
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
B. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
C. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
D. They have not assigned the timestamp, which causes the job to fail

Answer: A

NEW QUESTION # 289
......

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