PDE practice questions 131–140

1. Question 131 · 340 in bank

   You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbcIO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL. instance is running in Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

   • ASet up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.
   • BTurn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.
   • CAdd the external IP addresses of the Dataflow worker as authorized networks in the Cloud SQL instance.
   • DSet up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.

   Show answer

   Answer: A

   Explanation

   A looks to be the best out of the 4, D is complicated involving Compute Engine which is unnecessary making it cumbersome to address the problem

   Source: Adapted from community discussion — clouditis

   References

   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 132 · 340 in bank

   You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely. The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags. After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data. What should you do? (Choose two.)

   • ACreate two separate authorized datasets; one for the data analytics team and another for the consumer support team.
   • BEnsure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.
   • CReplace the authorized dataset with an authorized view. Use row-level security and apply filter_expression to limit data access.
   • DRemove the bigquery.dataViewer role from the data analytics team on the authorized datasets.
   • EEnforce access control in the policy tag taxonomy.

   Show answer

   Answer: E

   Explanation

   B & E B - It will ensure they don't have access to secure columns E- It will allow to enforce column level security Ref - https://cloud.google.com/bigquery/docs/column-level-security-intro

   Source: Adapted from community discussion — GCP001

   References

   • Google Cloud documentation

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3. Question 133 · 340 in bank

   You have a Cloud SQL for PostgreSQL instance in Region’ with one read replica in Region2 and another read replica in Region3. An unexpected event in Region’ requires that you perform disaster recovery by promoting a read replica in Region2. You need to ensure that your application has the same database capacity available before you switch over the connections. What should you do?

   • AEnable zonal high availability on the primary instance. Create a new read replica in a new region.
   • BCreate a cascading read replica from the existing read replica in Region3.
   • CCreate two new read replicas from the new primary instance, one in Region3 and one in a new region.
   • DCreate a new read replica in Region1, promote the new read replica to be the primary instance, and enable zonal high availability.

   Show answer

   Answer: C

   Explanation

   The best option here is C. Create two new read replicas from the new primary instance, one in Region3 and one in a new region. Here's the breakdown: Capacity Restoration: Promoting the Region2 replica makes it the new primary. You need to replicate from this new primary to maintain redundancy and capacity. Creating two replicas (Region3, new region) accomplishes this. Geographic Distribution: Distributing replicas across regions ensures availability if another regional event occurs. Speed: Creating new replicas from the promoted primary is likely faster than promoting another existing replica.

   Source: Adapted from community discussion — CGS22

   References

   • Google Cloud documentation
   • Google Cloud documentation

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4. Question 134 · 340 in bank

   You orchestrate ETL pipelines by using Cloud Composer. One of the tasks in the Apache Airflow directed acyclic graph (DAG) relies on a third-party service. You want to be notified when the task does not succeed. What should you do?

   • AAssign a function with notification logic to the on_retry_callback parameter for the operator responsible for the task at risk.
   • BConfigure a Cloud Monitoring alert on the sla_missed metric associated with the task at risk to trigger a notification.
   • CAssign a function with notification logic to the on_failure_callback parameter tor the operator responsible for the task at risk.
   • DAssign a function with notification logic to the sla_miss_callback parameter for the operator responsible for the task at risk.

   Show answer

   Answer: C

   Explanation

   - The on_failure_callback is a function that gets called when a task fails. - Assigning a function with notification logic to this parameter is a direct way to handle task failures. - When the task fails, this function can trigger a notification, making it an appropriate solution for the need to be alerted on task failures.

   Source: Adapted from community discussion — raaad

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5. Question 135 · 340 in bank

   Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

   • ARun a local version of Jupiter on the laptop.
   • BGrant the user access to Google Cloud Shell.
   • CHost a visualization tool on a VM on Google Compute Engine.
   • DDeploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

   Show answer

   Answer: D

   Explanation

   correct answer -> Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine. Google says: Use Cloud Datalab to easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. Reference: https://cloud.google.com/datalab/docs ***This is exactly the tool needed in this scenario*** But what about her laptop slowing her down? Well, Cloud Datalab is packaged as a container and run in a VM (Virtual Machine) instance. Reference: https://cloud.google.com/datalab/docs/concepts/key-concepts

   Source: Adapted from community discussion — samdhimal

   References

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

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6. Question 136 · 340 in bank

   You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL. database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet. What should you do?

   • AUpdate your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the simba.googlebigqueryodbc.ini file to point to your data center’s NAT gateway.
   • BUse Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your on-premises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.
   • CUse Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward-SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on-premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.
   • DUse Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream. Add those IP addresses to the firewall allowlist of your on-premises data center. Use IP Allowlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.

   Show answer

   Answer: B

   Explanation

   - Datastream is a serverless change data capture and replication service, which can be used to replicate data changes from MySQL to BigQuery. - Using Cloud Interconnect provides a private, secure connection between your on-premises environment and Google Cloud ==> This method ensures that data doesn't go through the public internet and is a recommended approach for secure, large-scale data migrations. - Setting up private connectivity with Datastream allows for secure and direct data transfer.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation

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7. Question 137 · 340 in bank

   You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

   • AUse BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
   • BCreate a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.
   • CLoad files from AWS and Azure to Cloud Storage with Cloud Shell gsutil rsync arguments.
   • DUse the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

   Show answer

   Answer: D

   Explanation

   - BigQuery Omni allows us to analyze data stored across Google Cloud, AWS, and Azure directly from BigQuery without having to move or copy the data. - It extends BigQuery's data analysis capabilities to other clouds, enabling cross-cloud analytics.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation

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8. Question 138 · 340 in bank

   You have a variety of files in Cloud Storage that your data science team wants to use in their models. Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?

   • AProvide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.
   • BCreate an external table in BigQuery and use SQL to transform the data as necessary. Provide the data science team access to the external tables to explore the raw data.
   • CLoad the data into BigQuery and use SQL to transform the data as necessary. Provide the data science team access to staging tables to explore the raw data.
   • DProvide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.

   Show answer

   Answer: D

   Explanation

   - Dataprep is a serverless, no-code data preparation tool that allows users to visually explore, cleanse, and prepare data for analysis. - It's designed for business analysts, data scientists, and others who want to work with data without writing code. - Dataprep can directly access and transform data in Cloud Storage, making it a suitable choice for a team that prefers a low-code, user-friendly solution.

   Source: Adapted from community discussion — raaad

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9. Question 139 · 340 in bank

   You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

   • ABuild BigQuery user-defined functions (UDFs).
   • BCreate Dataplex data quality tasks.
   • CBuild Dataform assertions into your code.
   • DWrite a Spark-based stored procedure.

   Show answer

   Answer: C

   Explanation

   - Dataform provides a feature called "assertions," which are essentially SQL-based tests that you can define to verify the quality of your data. - Assertions in Dataform are a built-in way to perform data quality checks, including checking for uniqueness and null values in your tables.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

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10. Question 140 · 340 in bank

    Your organization stores customer data in an on-premises Apache Hadoop cluster in Apache Parquet format. Data is processed on a daily basis by Apache Spark jobs that run on the cluster. You are migrating the Spark jobs and Parquet data to Google Cloud. BigQuery will be used on future transformation pipelines so you need to ensure that your data is available in BigQuery. You want to use managed services, while minimizing ETL data processing changes and overhead costs. What should you do?

    • AMigrate your data to Cloud Storage and migrate the metadata to Dataproc Metastore (DPMS). Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
    • BMigrate your data to Cloud Storage and register the bucket as a Dataplex asset. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
    • CMigrate your data to BigQuery. Refactor Spark pipelines to write and read data on BigQuery, and run them on Dataproc Serverless.
    • DMigrate your data to BigLake. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc on Compute Engine.

    Show answer

    Answer: A

    Explanation

    - This option involves moving Parquet files to Cloud Storage, which is a common and cost-effective storage solution for big data and is compatible with Spark jobs. - Using Dataproc Metastore to manage metadata allows us to keep Hadoop ecosystem's structural information. - Running Spark jobs on Dataproc Serverless takes advantage of managed Spark services without managing clusters. - Once the data is in Cloud Storage, you can also easily load it into BigQuery for further analysis.

    Source: Adapted from community discussion — raaad

    References

    • Google Cloud documentation

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PDE practice, question 131. 340 questions available.