PDE practice questions 91–100

1. Question 91 · 340 in bank

   You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery. In your current relational database, the author information is kept in a separate table and joined to the book information on a common key. Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

   • AKeep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today.
   • BCreate a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc.
   • CCreate a table that includes information about the books and authors, but nest the author fields inside the author column.
   • DKeep the schema the same, create a view that joins all of the tables, and always query the view.

   Show answer

   Answer: C

   Explanation

   C is the answer. https://cloud.google.com/bigquery/docs/best-practices-performance-nested Best practice: Use nested and repeated fields to denormalize data storage and increase query performance. Denormalization is a common strategy for increasing read performance for relational datasets that were previously normalized. The recommended way to denormalize data in BigQuery is to use nested and repeated fields. It's best to use this strategy when the relationships are hierarchical and frequently queried together, such as in parent-child relationships.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation

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2. Question 92 · 340 in bank

   You need to give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID. This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline. There will be tens of thousands of messages per second and that can be multi-threaded. and you worry about the backpressure on the system. How should you design your pipeline to minimize that backpressure?

   • ACall out to the service via HTTP.
   • BCreate the pipeline statically in the class definition.
   • CCreate a new object in the startBundle method of DoFn.
   • DBatch the job into ten-second increments.

   Show answer

   Answer: D

   Explanation

   D: I have insisted on this choice all aling. please read find the keyword massive backpressure https://cloud.google.com/blog/products/data-analytics/guide-to-common-cloud-dataflow-use-case-patterns-part-1 if the call takes on average 1 sec, that would cause massive backpressure on the pipeline. In these circumstances you should consider batching these requests, instead.

   Source: Adapted from community discussion — John_Pongthorn

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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3. Question 93 · 340 in bank

   You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center. Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB. Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time. What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

   • AStorage Transfer Service for the migration; Pub/Sub and Cloud Data Fusion for the real-time updates
   • BBigQuery Data Transfer Service for the migration; Pub/Sub and Dataproc for the real-time updates
   • Cgsutil for the migration; Pub/Sub and Dataflow for the real-time updates
   • Dgsutil for both the migration and the real-time updates

   Show answer

   Answer: C

   Explanation

   C is the answer. https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#gsutil_for_smaller_transfers_of_on-premises_data The gsutil tool is the standard tool for small- to medium-sized transfers (less than 1 TB) over a typical enterprise-scale network, from a private data center to Google Cloud.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation

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4. Question 94 · 340 in bank

   You are using Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in. How should you design your row key and tables to ensure that you can access the data with the simplest query?

   • ACreate one unique table for all of the indices, and then use the index and timestamp as the row key design.
   • BCreate one unique table for all of the indices, and then use a reverse timestamp as the row key design.
   • CFor each index, have a separate table and use a timestamp as the row key design.
   • DFor each index, have a separate table and use a reverse timestamp as the row key design.

   Show answer

   Answer: B

   Explanation

   This is special case , plese Take a look carefully the below link and read at last paragraph at the bottom of this comment, let everyone share idea, We will go with B, C https://cloud.google.com/bigtable/docs/schema-design#time-based Don't use a timestamp by itself or at the beginning of a row key, because this will cause sequential writes to be pushed onto a single node, creating a hotspot. If you usually retrieve the most recent records first, you can use a reversed timestamp in the row key by subtracting the timestamp from your programming language's maximum value for long integers (in Java, java.lang.Long.MAX_VALUE). With a reversed timestamp, the records will be ordered from most recent to least recent.

   Source: Adapted from community discussion — John_Pongthorn

   References

   • Google Cloud documentation

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5. Question 95 · 340 in bank

   You are building a report-only data warehouse where the data is streamed into BigQuery via the streaming API. Following Google's best practices, you have both a staging and a production table for the data. How should you design your data loading to ensure that there is only one master dataset without affecting performance on either the ingestion or reporting pieces?

   • AHave a staging table that is an append-only model, and then update the production table every three hours with the changes written to staging.
   • BHave a staging table that is an append-only model, and then update the production table every ninety minutes with the changes written to staging.
   • CHave a staging table that moves the staged data over to the production table and deletes the contents of the staging table every three hours.
   • DHave a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes.

   Show answer

   Answer: C

   Explanation

   [C] I found the correct answer based on a real case, where Google's Solutions Architect team decided to move an internal process to use BigQuery. The related doc is here: https://cloud.google.com/blog/products/data-analytics/moving-a-publishing-workflow-to-bigquery-for-new-data-insights

   Source: Adapted from community discussion — NicolasN

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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6. Question 96 · 340 in bank

   You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?

   • AJob validation
   • BExceptions in worker code
   • CGraph or pipeline construction
   • DInsufficient permissions

   Show answer

   Answer: B

   Explanation

   B. Exceptions in worker code While your job is running, you might encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Dataflow job. Exceptions in user code (for example, your DoFn instances) are reported in the Dataflow monitoring interface. Reference (Lists all answer choices and when to pick each one): https://cloud.google.com/dataflow/docs/guides/troubleshooting-your-pipeline#Causes

   Source: Adapted from community discussion — AWSandeep

   References

   • Google Cloud documentation

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7. Question 97 · 340 in bank

   Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?

   • ADo daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.
   • BFilter data in Cloud Logging by project, resource, and user; then export the data in CSV format.
   • CFilter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.
   • DExport Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.

   Show answer

   Answer: A

   Explanation

   A. Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user. You cannot import custom or filtered billing criteria into BigQuery. There are three types of Cloud Billing data tables with a fixed schema that must further drilled-down via BigQuery views. Reference: https://cloud.google.com/billing/docs/how-to/export-data-bigquery#setup

   Source: Adapted from community discussion — AWSandeep

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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8. Question 98 · 340 in bank

   The Development and External teams have the project viewer Identity and Access Management (IAM) role in a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

   

   • ARemove Cloud Storage IAM permissions to the External Team on the acme-raw-data project.
   • BCreate Virtual Private Cloud (VPC) firewall rules on the acme-raw-data project that deny all ingress traffic from the External Team CIDR range.
   • CCreate a VPC Service Controls perimeter containing both projects and BigQuery as a restricted API. Add the External Team users to the perimeter's Access Level.
   • DCreate a VPC Service Controls perimeter containing both projects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level.

   Show answer

   Answer: D

   Explanation

   D. Create a VPC Service Controls perimeter containing both projects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level. Reveal Solution

   Source: Adapted from community discussion — AWSandeep

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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9. Question 99 · 340 in bank

   Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup to serve customers globally. Your current goal is to optimize for cost, and your post-funding goal is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

   • AUse Cloud Spanner to configure a single region instance initially, and then configure multi-region Cloud Spanner instances after securing funding.
   • BUse a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US, Europe, and Asia replication after securing funding.
   • CUse a Cloud SQL for PostgreSQL zonal instance first, and Bigtable with US, Europe, and Asia after securing funding.
   • DUse a Cloud SQL for PostgreSQL zonal instance first, and Cloud SQL for PostgreSQL with highly available configuration after securing funding.

   Show answer

   Answer: A

   Explanation

   A. Use Cloud Spanner to configure a single region instance initially, and then configure multi-region Cloud Spanner instances after securing funding. When you create a Cloud Spanner instance, you must configure it as either regional (that is, all the resources are contained within a single Google Cloud region) or multi-region (that is, the resources span more than one region). You can change the instance configuration to multi-regional (or global) at anytime.

   Source: Adapted from community discussion — AWSandeep

   References

   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 100 · 340 in bank

    You need to migrate 1 PB of data from an on-premises data center to Google Cloud. Data transfer time during the migration should take only a few hours. You want to follow Google-recommended practices to facilitate the large data transfer over a secure connection. What should you do?

    • AEstablish a Cloud Interconnect connection between the on-premises data center and Google Cloud, and then use the Storage Transfer Service.
    • BUse a Transfer Appliance and have engineers manually encrypt, decrypt, and verify the data.
    • CEstablish a Cloud VPN connection, start gcloud compute scp jobs in parallel, and run checksums to verify the data.
    • DReduce the data into 3 TB batches, transfer the data using gsutil, and run checksums to verify the data.

    Show answer

    Answer: A

    Explanation

    A is the answer. https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#storage-transfer-service-for-large-transfers-of-on-premises-data Like gsutil, Storage Transfer Service for on-premises data enables transfers from network file system (NFS) storage to Cloud Storage. Although gsutil can support small transfer sizes (up to 1 TB), Storage Transfer Service for on-premises data is designed for large-scale transfers (up to petabytes of data, billions of files).

    Source: Adapted from community discussion — zellck

    References

    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation
    • Google Cloud documentation

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PDE practice, question 91. 340 questions available.