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Perfect Google - Real Professional-Data-Engineer Exam Questions
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Exam Details
The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.
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Google Certified Professional Data Engineer Exam Sample Questions (Q128-Q133):
NEW QUESTION # 128
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour
Has added more than $30 worth of products to the basket Has not completed a
transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?
- A. Use a global window with a time based trigger with a delay of 60 minutes.
- B. Use a sliding time window with a duration of 60 minutes.
- C. Use a session window with a gap time duration of 60 minutes.
- D. Use a fixed-time window with a duration of 60 minutes.
Answer: A
NEW QUESTION # 129
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?
- A. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
- B. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key. Use gsutil cpto upload each encrypted file to the Cloud Storage bucket.
Manually destroy the key previously used for encryption, and rotate the key once. - C. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
- D. Use gcloud kms keys createto create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
Answer: B
NEW QUESTION # 130
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.
Tom,555 X street
Tim,553 Y street
Sam, 111 Z street
Which operation is best suited for the above data processing requirement?
- A. Source API
- B. Sink API
- C. Data extraction
- D. ParDo
Answer: D
Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each
element in your PCollection.
NEW QUESTION # 131
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat
a. What should you do?
- A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the models
using Cloud Dataproc. Call the models from your application. - B. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences. - C. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application. - D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
Answer: B
NEW QUESTION # 132
You recently deployed several data processing jobs into your Cloud Composer 2 environment. You notice that some tasks are failing in Apache Airflow. On the monitoring dashboard, you see an increase in the total workers' memory usage, and there were worker pod evictions. You need to resolve these errors. What should you do?
Choose 2 answers
- A. Increase the directed acyclic graph (DAG) file parsing interval.
- B. Increase the Cloud Composer 2 environment size from medium to large.
- C. Increase the memory available to the Airflow workers.
- D. Increase the maximum number of workers and reduce worker concurrency.
- E. Increase the memory available to the Airflow triggerer.
Answer: C,D
Explanation:
To resolve issues related to increased memory usage and worker pod evictions in your Cloud Composer 2 environment, the following steps are recommended:
* Increase Memory Available to Airflow Workers:
* By increasing the memory allocated to Airflow workers, you can handle more memory-intensive tasks, reducing the likelihood of pod evictions due to memory limits.
* Increase Maximum Number of Workers and Reduce Worker Concurrency:
* Increasing the number of workers allows the workload to be distributed across more pods, preventing any single pod from becoming overwhelmed.
* Reducing worker concurrency limits the number of tasks that each worker can handle simultaneously, thereby lowering the memory consumption per worker.
Steps to Implement:
* Increase Worker Memory:
* Modify the configuration settings in Cloud Composer to allocate more memory to Airflow workers. This can be done through the environment configuration settings.
* Adjust Worker and Concurrency Settings:
* Increase the maximum number of workers in the Cloud Composer environment settings.
* Reduce the concurrency setting for Airflow workers to ensure that each worker handles fewer tasks at a time, thus consuming less memory per worker.
Reference Links:
* Cloud Composer Worker Configuration
* Scaling Airflow Workers
NEW QUESTION # 133
......
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