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To be eligible for the exam, candidates should have a minimum of three years of experience in data engineering, as well as a thorough understanding of the Google Cloud Platform. They should also have hands-on experience in designing and implementing data processing systems using various Google Cloud tools and services, such as BigQuery, Cloud Dataflow, Cloud Storage, and Cloud Pub/Sub.

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Google Professional-Data-Engineer Certification Exam consists of multiple-choice questions and performance-based tasks that simulate real-world scenarios. Professional-Data-Engineer exam is divided into four main domains: Designing data processing systems, building and operationalizing data processing systems, integrating data sources, and managing data processing infrastructure.

Google Certified Professional Data Engineer Exam Sample Questions (Q253-Q258):

NEW QUESTION # 253
One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re-encrypt all of your CMEK-protected Cloud Storage data that used that key. and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK protection in the future. What should you do?

A. Create a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.
B. Create a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.
C. Create a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.
D. Rotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.

Answer: A

Explanation:
To re-encrypt all of your CMEK-protected Cloud Storage data after a key has been exposed, and to ensure future writes are protected with a new key, creating a new Cloud KMS key and a new Cloud Storage bucket is the best approach. Here's why option C is the best choice:
* Re-encryption of Data:
* By creating a new Cloud Storage bucket and copying all objects from the old bucket to the new bucket while specifying the new Cloud KMS key, you ensure that all data is re-encrypted with the new key.
* This process effectively re-encrypts the data, removing any dependency on the compromised key.
* Ensuring CMEK Protection:
* Creating a new bucket and setting the new CMEK as the default ensures that all future objects written to the bucket are automatically protected with the new key.
* This reduces the risk of objects being written without CMEK protection.
* Deletion of Compromised Key:
* Once the data has been copied and re-encrypted, the old key can be safely deleted from Cloud KMS, eliminating the risk associated with the compromised key.
Steps to Implement:
* Create a New Cloud KMS Key:
* Create a new encryption key in Cloud KMS to replace the compromised key.
* Create a New Cloud Storage Bucket:
* Create a new Cloud Storage bucket and set the default CMEK to the new key.
* Copy and Re-encrypt Data:
* Use the gsutil tool to copy data from the old bucket to the new bucket while specifying the new CMEK key:
gsutil -o "GSUtil:gs_json_api_version=2" cp -r gs://old-bucket/* gs://new-bucket/
* Delete the Old Key:
* After ensuring all data is copied and re-encrypted, delete the compromised key from Cloud KMS.
Reference Links:
* Cloud KMS Documentation
* Cloud Storage Encryption
* Re-encrypting Data in Cloud Storage

NEW QUESTION # 254
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

A. gcloud ml-engine jobs submit training
B. gcloud ml-engine local train
C. gcloud ml-engine jobs submit training local
D. You can't run a TensorFlow program on your own computer using Cloud ML Engine .

Answer: B

Explanation:
gcloud ml-engine local train - run a Cloud ML Engine training job locally This command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job.
This is especially useful in the case of testing distributed models, as it allows you to validate that you are properly interacting with the Cloud ML Engine cluster configuration.

NEW QUESTION # 255
Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

A. 1Create a project for storage of the data for your organization.2 Create a central Cloud Storage bucket with three folders to store the files for each department.3. Create a central BigQuery dataset with tables prefixed with the department name.4 Give viewer rights for the storage project for the users of your departments.
B. 1 Create multiple projects for storage of the data for each of your departments' applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Publish the data that each department shared in Analytics Hub.4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.
C. 1Create a project for storage of the data for each of your departments.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Create user groups for authorized readers for each bucket and dataset.4 Enable the IT team to administer the user groups to add or remove users as the departments' request.
D. 1 Create multiple projects for storage of the data for each of your departments' applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones.4 Enable each department to own and share the data of their data lakes.

Answer: B

Explanation:
Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.
References:
Architecture and functions in a data mesh - Google Cloud
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Build a Data Mesh with Dataplex | Google Cloud Skills Boost

NEW QUESTION # 256
You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?

A. Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.
B. Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.
C. Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.
D. Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.

Answer: D

Explanation:
A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired. Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. Reference:
Materialized views, ML models in data warehouse - Google Cloud
Data Engineering with Google Cloud Platform - Packt Subscription

NEW QUESTION # 257
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the dat
a. They should only see certain tables based on their team membership. How should you set user permissions?

A. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
B. Assign the users/groups data viewer access at the table level for each table
C. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
D. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside

Answer: B

NEW QUESTION # 258
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