PDE practice questions 71–80

1. Question 71 · 340 in bank

   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?

   • AUse gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to 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.
   • BUse gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once.
   • CSpecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
   • DSpecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

   Show answer

   Answer: A

   Explanation

   The correct answer must be D A and B can be eliminated immediately since kms generated keys are considered potentially accessible by CSP. C is incorrect because memory store is essentially a cache service. Additional authenticated data (AAD) acts as a "salt", it is not a cipher.

   Source: Adapted from community discussion — dhs227

   References

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

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2. Question 72 · 340 in bank

   You have data pipelines running on BigQuery, Dataflow, and Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products or features of the platform. What should you do?

   • AExport the information to Cloud Monitoring, and set up an Alerting policy
   • BRun a Virtual Machine in Compute Engine with Airflow, and export the information to Cloud Monitoring
   • CExport the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
   • DDevelop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs

   Show answer

   Answer: A

   Explanation

   Cloud Monitoring (formerly known as Stackdriver) is a fully managed monitoring service provided by GCP, which can collect metrics, logs, and other telemetry data from various GCP services, including BigQuery, Dataflow, and Dataproc. Alerting Policies: Cloud Monitoring allows you to define alerting policies based on specific conditions or thresholds, such as pipeline failures, latency spikes, or other custom metrics. When these conditions are met, Cloud Monitoring can trigger notifications (e.g., emails) to alert the team managing the pipelines. Cross-Project Monitoring: Cloud Monitoring supports monitoring resources across multiple GCP projects, making it suitable for your requirement to monitor pipelines in multiple projects. Managed Solution: Cloud Monitoring is a managed service, reducing the operational overhead compared to running your own virtual machine instances or building custom solutions.

   Source: Adapted from community discussion — barnac1es

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3. Question 73 · 340 in bank

   You are working on a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

   • ACreate a new view with BigQuery that does not include a column with city information.
   • BUse SQL in BigQuery to transform the state column using a one-hot encoding method, and make each city a column with binary values.
   • CUse TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file and upload that as part of your model to BigQuery ML.
   • DUse Cloud Data Fusion to assign each city to a region that is labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.

   Show answer

   Answer: B

   Explanation

   One-hot encoding is a common technique used to handle categorical data in machine learning. This approach will transform the city name variable into a series of binary columns, one for each city. Each row will have a "1" in the column corresponding to the city it represents and "0" in all other city columns. This method is effective for linear regression models as it enables the model to use city data as a series of numeric, binary variables. BigQuery supports SQL operations that can easily implement one-hot encoding, thus minimizing the amount of coding required and efficiently preparing the data for the model.

   Source: Adapted from community discussion — MaxNRG

   References

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

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4. Question 74 · 340 in bank

   You work for a large bank that operates in locations throughout North America. You are setting up a data storage system that will handle bank account transactions. You require ACID compliance and the ability to access data with SQL. Which solution is appropriate?

   • AStore transaction data in Cloud Spanner. Enable stale reads to reduce latency.
   • BStore transaction in Cloud Spanner. Use locking read-write transactions.
   • CStore transaction data in BigQuery. Disabled the query cache to ensure consistency.
   • DStore transaction data in Cloud SQL. Use a federated query BigQuery for analysis.

   Show answer

   Answer: B

   Explanation

   I'd say B as the documentation primarily says ACID compliance for Spanner, not Cloud SQL. https://cloud.google.com/blog/topics/developers-practitioners/your-google-cloud-database-options-explained Also, spanner supports read-write transactions for use cases, as handling bank transactions: https://cloud.google.com/spanner/docs/transactions#read-write_transactions

   Source: Adapted from community discussion — devaid

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 75 · 340 in bank

   A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

   • AImplement clustering in BigQuery on the ingest date column.
   • BImplement clustering in BigQuery on the package-tracking ID column.
   • CTier older data onto Cloud Storage files and create a BigQuery table using Cloud Storage as an external data source.
   • DRe-create the table using data partitioning on the package delivery date.

   Show answer

   Answer: B

   Explanation

   B is the answer. https://cloud.google.com/bigquery/docs/clustered-tables Clustered tables in BigQuery are tables that have a user-defined column sort order using clustered columns. Clustered tables can improve query performance and reduce query costs. In BigQuery, a clustered column is a user-defined table property that sorts storage blocks based on the values in the clustered columns. The storage blocks are adaptively sized based on the size of the table. A clustered table maintains the sort properties in the context of each operation that modifies it. Queries that filter or aggregate by the clustered columns only scan the relevant blocks based on the clustered columns instead of the entire table or table partition.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation

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6. Question 76 · 340 in bank

   Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility. The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?

   • AMigrate the workloads to Dataproc plus HDFS; modernize later.
   • BMigrate the workloads to Dataproc plus Cloud Storage; modernize later.
   • CMigrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.
   • DModernize the Spark workload for Dataflow and the Hive workload for BigQuery.

   Show answer

   Answer: B

   Explanation

   B is the answer. https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc#overview When you want to move your Apache Spark workloads from an on-premises environment to Google Cloud, we recommend using Dataproc to run Apache Spark/Apache Hadoop clusters. Dataproc is a fully managed, fully supported service offered by Google Cloud. It allows you to separate storage and compute, which helps you to manage your costs and be more flexible in scaling your workloads. https://cloud.google.com/bigquery/docs/migration/hive#data_migration Migrating Hive data from your on-premises or other cloud-based source cluster to BigQuery has two steps: 1. Copying data from a source cluster to Cloud Storage 2. Loading data from Cloud Storage into BigQuery

   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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7. Question 77 · 340 in bank

   You work for a financial institution that lets customers register online. As new customers register, their user data is sent to Pub/Sub before being ingested into BigQuery. For security reasons, you decide to redact your customers' Government issued Identification Number while allowing customer service representatives to view the original values when necessary. What should you do?

   • AUse BigQuery's built-in AEAD encryption to encrypt the SSN column. Save the keys to a new table that is only viewable by permissioned users.
   • BUse BigQuery column-level security. Set the table permissions so that only members of the Customer Service user group can see the SSN column.
   • CBefore loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic hash.
   • DBefore loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token.

   Show answer

   Answer: D

   Explanation

   D is the answer. https://cloud.google.com/dlp/docs/pseudonymization#supported-methods Cloud DLP supports three pseudonymization techniques, all of which use cryptographic keys. Following are the available methods: - Format preserving encryption: An input value is replaced with a value that has been encrypted using the FPE-FFX encryption algorithm with a cryptographic key, and then prepended with a surrogate annotation, if specified. By design, both the character set and the length of the input value are preserved in the output value. Encrypted values can be re-identified using the original cryptographic key and the entire output value, including surrogate annotation.

   Source: Adapted from community discussion — zellck

   References

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

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8. Question 78 · 340 in bank

   You are migrating a table to BigQuery and are deciding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID, and the city and state in which the store is located. You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state, city, and individual store. How would you model this table for the best query performance?

   • APartition by transaction time; cluster by state first, then city, then store ID.
   • BPartition by transaction time; cluster by store ID first, then city, then state.
   • CTop-level cluster by state first, then city, then store ID.
   • DTop-level cluster by store ID first, then city, then state.

   Show answer

   Answer: A

   Explanation

   A is the answer. https://cloud.google.com/bigquery/docs/partitioned-tables This page provides an overview of partitioned tables in BigQuery. A partitioned table is a special table that is divided into segments, called partitions, that make it easier to manage and query your data. By dividing a large table into smaller partitions, you can improve query performance, and you can control costs by reducing the number of bytes read by a query. You can partition BigQuery tables by: - Time-unit column: Tables are partitioned based on a TIMESTAMP, DATE, or DATETIME column in the table. https://cloud.google.com/bigquery/docs/clustered-tables Clustered tables in BigQuery are tables that have a user-defined column sort order using clustered columns. Clustered tables can improve query performance and reduce query costs.

   Source: Adapted from community discussion — zellck

   References

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

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9. Question 79 · 340 in bank

   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?

   • ACreate a Google Cloud Dataflow job to process the data.
   • BCreate a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
   • CCreate a Hadoop cluster on Google Compute Engine that uses persistent disks.
   • DCreate a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
   • ECreate a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

   Show answer

   Answer: D

   Explanation

   D is correct because it uses managed services, and also allows for the data to persist on GCS beyond the life of the cluster. A is not correct because the goal is to re-use their Hadoop jobs and MapReduce and/or Spark jobs cannot simply be moved to Dataflow. B is not correct because the goal is to persist the data beyond the life of the ephemeral clusters, and if HDFS is used as the primary attached storage mechanism, it will also disappear at the end of the cluster’s life. C is not correct because the goal is to use managed services as much as possible, and this is the opposite. E is not correct because the goal is to use managed services as much as possible, and this is the opposite.

   Source: Adapted from community discussion — MaxNRG

   References

   • Google Cloud documentation

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10. Question 80 · 340 in bank

    You are updating the code for a subscriber to a Pub/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. Your subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?

    • ASet up the Pub/Sub emulator on your local machine. Validate the behavior of your new subscriber logic before deploying it to production.
    • BCreate a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created.
    • CUse Cloud Build for your deployment. If an error occurs after deployment, use a Seek operation to locate a timestamp logged by Cloud Build at the start of the deployment.
    • DEnable dead-lettering on the Pub/Sub topic to capture messages that aren't successfully acknowledged. If an error occurs after deployment, re-deliver any messages captured by the dead-letter queue.

    Show answer

    Answer: B

    Explanation

    B. Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created. According to the second reference in the list below, a concern with deploying new subscriber code is that the new executable may erroneously acknowledge messages, leading to message loss. Incorporating snapshots into your deployment process gives you a way to recover from bugs in new subscriber code. Answer cannot be C because To seek to a timestamp, you must first configure the subscription to retain acknowledged messages using retain-acked-messages. If retain-acked-messages is set, Pub/Sub retains acknowledged messages for 7 days. References: https://cloud.google.com/pubsub/docs/replay-message https://cloud.google.com/pubsub/docs/replay-overview#seek_use_cases

    Source: Adapted from community discussion — AWSandeep

    References

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

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PDE practice, question 71. 340 questions available.