| Question.6 Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do? (A) Create a Google Cloud Dataflow job to process the data. (B) Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS. (C) Create a Hadoop cluster on Google Compute Engine that uses persistent disks. (D) Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector. (E) Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks. |
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Answer is (D) Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
Dataproc is used to migrate Hadoop and Spark jobs on GCP. Dataproc with GCS connected through Google Cloud Storage connector helps store data after the life of the cluster. When the job is high I/O intensive, then we need to create a small persistent disk.
| Question.7 Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do? (A) Put the data into Google Cloud Storage. (B) Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster. (C) Tune the Cloud Dataproc cluster so that there is just enough disk for all data. (D) Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk. |
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Answer is (A) Put the data into Google Cloud Storage.
A is correct because Google recommends using Cloud Storage instead of HDFS as it is much more cost effective especially when jobs aren’t running.
B is not correct because this will decrease the compute cost but not the storage cost.
C is not correct because while this will reduce cost somewhat, it will not be as cost effective as using Cloud Storage.
D is not correct because while this will reduce cost somewhat, it will not be as cost effective as using Cloud Storage.
| Question.8 You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do? (A) Send the data to Google Cloud Datastore and then export to BigQuery. (B) Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery. (C) Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required. (D) Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed. |
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Answer is (B) Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
You can use cloud data flow for both batch and streaming pipelines. Bigquery for analytics. Pub sub will be used to stream data into cloud data flow.
| Question.9 You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users’ privacy? (A) Grant the consultant the Viewer role on the project. (B) Grant the consultant the Cloud Dataflow Developer role on the project. (C) Create a service account and allow the consultant to log on with it. (D) Create an anonymized sample of the data for the consultant to work with in a different project. |
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Answer is (B) Grant the consultant the Cloud Dataflow Developer role on the project.
The Dataflow developer role will not provide access to the underlying data.
Reference:
https://cloud.google.com/dataflow/docs/concepts/access-control#example_role_assignment
| Question.10 You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do? (A) Load the data every 30 minutes into a new partitioned table in BigQuery. (B) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery (C) Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore (D) Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage. |
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Answer is (B) Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
Use cases for external data sources include:
Loading and cleaning your data in one pass by querying the data from an external data source (a location external to BigQuery) and writing the cleaned result into BigQuery storage.
Having a small amount of frequently changing data that you join with other tables. As an external data source, the frequently changing data does not need to be reloaded every time it is updated.
Reference:
https://cloud.google.com/bigquery/external-data-sources