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Google Certified Professional Data Engineer Exam Sample Questions (Q101-Q106):
NEW QUESTION # 101
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?
A. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
C. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
D. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
Answer: B
NEW QUESTION # 102
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
A. Add ", UNNEST(person)" before the WHERE clause.
B. Change "person" to "city.person".
C. Add ", UNNEST(city)" before the WHERE clause.
D. Change "person" to "person.city".
Answer: A
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results
NEW QUESTION # 103
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
A. Use Cloud ML Engine for training existing Spark ML models
B. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
C. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
D. Rewrite your models on TensorFlow, and start using Cloud ML Engine
Answer: C
Explanation:
cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml
NEW QUESTION # 104
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
A. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
B. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
C. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
D. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
Answer: A
Explanation:
cloud.google.com/compute/docs/disks/customer-managed-encryption
NEW QUESTION # 105
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 the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
C. Store and process the entire dataset in BigQuery.
D. Store and process the entire dataset in Cloud Bigtable.
Answer: A
NEW QUESTION # 106
......
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The Google Professional-Data-Engineer exam questions on the platform have been gathered by subject matter experts to ensure that they accurately reflect the format and difficulty level of the actual Google Professional-Data-Engineer exam. This makes these Google Certified Professional Data Engineer Exam PDF Questions ideal for individuals looking to pass the Google Professional-Data-Engineer Exam on their first try. You can evaluate the product with a free Professional-Data-Engineer demo.
>> Professional-Data-Engineer Sample Questions <<
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With the help of our Professional-Data-Engineer practice materials, you can successfully pass the actual exam with might redoubled. Our company owns the most popular reputation in this field by providing not only the best ever Professional-Data-Engineer study guide but also the most efficient customers’ servers. We can lead you the best and the fastest way to reach for the certification of Professional-Data-Engineer Exam Dumps and achieve your desired higher salary by getting a more important position in the company.
Google Certified Professional Data Engineer Exam Sample Questions (Q101-Q106):
NEW QUESTION # 101
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?
A. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
C. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
D. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
Answer: B
NEW QUESTION # 102
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
A. Add ", UNNEST(person)" before the WHERE clause.
B. Change "person" to "city.person".
C. Add ", UNNEST(city)" before the WHERE clause.
D. Change "person" to "person.city".
Answer: A
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results
NEW QUESTION # 103
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
A. Use Cloud ML Engine for training existing Spark ML models
B. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
C. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
D. Rewrite your models on TensorFlow, and start using Cloud ML Engine
Answer: C
Explanation:
cloud.google.com/dataproc/docs/tutorials/bigquery-sparkml
NEW QUESTION # 104
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
A. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
B. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
C. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
D. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
Answer: A
Explanation:
cloud.google.com/compute/docs/disks/customer-managed-encryption
NEW QUESTION # 105
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 the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
C. Store and process the entire dataset in BigQuery.
D. Store and process the entire dataset in Cloud Bigtable.
Answer: A
NEW QUESTION # 106
......
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