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Google Data Engineer Sample Questions (Q72-Q77):
NEW QUESTION # 72
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud Spanner for storag
- B. Add secondary indexes to support query patterns.
- C. Use Cloud SQL for storag
- D. Use Cloud SQL for storag
- E. Use Cloud Dataflow to transform data to support query patterns.
- F. Use Cloud Spanner for storag
- G. Use Cloud Dataflow to transform data to support query patterns.
- H. Add secondary indexes to support query patterns.
Answer: E
Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 73
What are the minimum permissions needed for a service account used with Google Dataproc?
- A. Execute to Google Cloud Storage; write to Google Cloud Logging
- B. Execute to Google Cloud Storage; execute to Google Cloud Logging
- C. Read and write to Google Cloud Storage; write to Google Cloud Logging
- D. Write to Google Cloud Storage; read to Google Cloud Logging
Answer: C
Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes
NEW QUESTION # 74
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)
- A. Change the zone of your Cloud Dataflow pipeline to run in us-central1
- B. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
- C. Create a temporary table in Cloud Spanner that will act as a buffer for new data
- D. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
- E. Use a larger instance type for your Cloud Dataflow workers
- F. Increase the number of max workers
- G. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
Answer: E,G
NEW QUESTION # 75
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
- A. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- B. Use Cloud Deployment Manager to automate access provision.
- C. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
- D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- E. Introduce resource hierarchy to leverage access control policy inheritance.
- F. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
Answer: D,E
NEW QUESTION # 76
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
- A. Build and train a text classification model using TensorFlo
- B. Call the Cloud Natural Language API from your applicatio
- C. Process the generated Sentiment Analysis as labels.
- D. Call the Cloud Natural Language API from your applicatio
- E. Deploy the model using Cloud Machine Learning Engin
- F. Build and train a text classification model using TensorFlo
- G. Deploy the model using a KubernetesEngine cluste
- H. Process the generated Entity Analysis as labels.
- I. Call the model from your application and process the results as labels.
- J. Call the model from your application and process the results as labels.
Answer: H
NEW QUESTION # 77
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