PDE practice questions 141–150

1. Question 141 · 340 in bank

   Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?

   • AAdd firewall rules in project A so only traffic from the VPC in project A is permitted.
   • BConfigure VPC Service Controls in the organization with a perimeter around project A.
   • CUse Identity and Access Management conditions to ensure that only users and service accounts in project A. can access resources in project A.
   • DConfigure VPC Service Controls in the organization with a perimeter around the VPC of project A.

   Show answer

   Answer: B

   Explanation

   Setting up a perimeter around project A is future proof, the question asks to "ensure that project B and any future project cannot access data in the project A topic", IAM is not future proof. Reference: https://cloud.google.com/vpc-service-controls/docs/overview#isolate p.s: VPC Service Controls is not the same thing as VPC, instead its a security layer on top of a VPC and it should be used together with IAM, not one or the other (https://cloud.google.com/vpc-service-controls/docs/overview#how-vpc-service-controls-works)

   Source: Adapted from community discussion — fabiogoma

   References

   • Google Cloud documentation

   Next question

2. Question 142 · 340 in bank

   You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier, 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

   • ACreate a new Memorystore for Redis instance with Standard Tier. Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.
   • BCreate a new Memorystore for Redis instance with Standard Tier. Set capacity to 5 GB and create multiple read replicas. Delete the old instance.
   • CCreate a new Memorystore for Memcached instance. Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance. Delete the old instance.
   • DCreate multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity). Modify the Dataflow pipeline and new clients to use all instances.

   Show answer

   Answer: B

   Explanation

   - Upgrading to the Standard Tier and adding read replicas is an effective way to scale and manage increased read load. - The additional capacity (5 GB) provides more space for data, and read replicas help distribute the read load across multiple instances.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

   Next question

3. Question 143 · 340 in bank

   You have a streaming pipeline that ingests data from Pub/Sub in production. You need to update this streaming pipeline with improved business logic. You need to ensure that the updated pipeline reprocesses the previous two days of delivered Pub/Sub messages. What should you do? (Choose two.)

   • AUse the Pub/Sub subscription clear-retry-policy flag
   • BUse Pub/Sub Snapshot capture two days before the deployment.
   • CCreate a new Pub/Sub subscription two days before the deployment.
   • DUse the Pub/Sub subscription retain-acked-messages flag.
   • EUse Pub/Sub Seek with a timestamp.

   Show answer

   Answer: D

   Explanation

   DE Another way to replay messages that have been acknowledged is to seek to a timestamp. 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. You only need to do this step if you intend to seek to a timestamp, not to a snapshot. https://cloud.google.com/pubsub/docs/replay-message

   Source: Adapted from community discussion — tibuenoc

   References

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

   Next question

4. Question 144 · 340 in bank

   You currently use a SQL-based tool to visualize your data stored in BigQuery. The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

   • ACreate materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_staleness parameter to 4 hours and the enable_refresh parameter to true. Reference the materialized views in the data visualization tool.
   • BCreate views for the visualization queries. Reference the views in the data visualization tool.
   • CCreate a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler to trigger the Cloud Function every 4 hours. Reference the parquet files in the data visualization tool.
   • DCreate materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically. Reference the materialized views in the data visualization tool.

   Show answer

   Answer: A

   Explanation

   A https://cloud.google.com/bigquery/docs/materialized-views-create#non-incremental In scenarios where data staleness is acceptable, for example for batch data processing or reporting, non-incremental materialized views can improve query performance and reduce cost. allow_non_incremental_definition option. This option must be accompanied by the max_staleness option. To ensure a periodic refresh of the materialized view, you should also configure a refresh policy.

   Source: Adapted from community discussion — ricardovazz

   References

   • Google Cloud documentation
   • Google Cloud documentation

   Next question

5. Question 145 · 340 in bank

   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?

   • ASend the data to Google Cloud Datastore and then export to BigQuery.
   • BSend the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
   • CSend the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.
   • DExport 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.

   Show answer

   Answer: B

   Explanation

   B is more correct than other options so B is the answer. But if this is actual use case you have to deal with use Cloud BigTable instead of bigquery. So the pipeline will be like this. IOT-Devices -> Cloud Pub/Sub -> Cloud BigTable -> Cloud Data Studio (For real-time analytics)

   Source: Adapted from community discussion — DataExpert

   References

   • Google Cloud documentation
   • Google Cloud documentation

   Next question

6. Question 146 · 340 in bank

   You need to modernize your existing on-premises data strategy. Your organization currently uses: • Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication. • Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps. You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

   • AUse Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
   • BUse Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
   • CUse Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.
   • DUse Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

   Show answer

   Answer: B

   Next question

7. Question 147 · 340 in bank

   You recently deployed several data processing jobs into your Cloud Composer 2 environment. You notice that some tasks are failing in Apache Airflow. On the monitoring dashboard, you see an increase in the total workers memory usage, and there were worker pod evictions. You need to resolve these errors. What should you do? (Choose two.)

   • AIncrease the directed acyclic graph (DAG) file parsing interval.
   • BIncrease the Cloud Composer 2 environment size from medium to large.
   • CIncrease the maximum number of workers and reduce worker concurrency.
   • DIncrease the memory available to the Airflow workers.
   • EIncrease the memory available to the Airflow triggerer.

   Show answer

   Answer: C

   References

   • Google Cloud documentation

   Next question

8. Question 148 · 340 in bank

   You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west3 region. You want to follow Google-recommended practices. What should you do?

   • ASet the constraints/gcp.resourceLocations organization policy constraint to in:europe-west3-locations.
   • BDeploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.
   • CSet the constraints/gcp.resourceLocations organization policy constraint to in:eu-locations.
   • DCreate a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.

   Show answer

   Answer: A

   Explanation

   - The constraints/gcp.resourceLocations organization policy constraint is used to define where resources in the organization can be created. - Setting it to in:europe-west3-locations would specify that resources can only be created in the europe-west3 region.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation

   Next question

9. Question 149 · 340 in bank

   You are a BigQuery admin supporting a team of data consumers who run ad hoc queries and downstream reporting in tools such as Looker. All data and users are combined under a single organizational project. You recently noticed some slowness in query results and want to troubleshoot where the slowdowns are occurring. You think that there might be some job queuing or slot contention occurring as users run jobs, which slows down access to results. You need to investigate the query job information and determine where performance is being affected. What should you do?

   • AUse slot reservations for your project to ensure that you have enough query processing capacity and are able to allocate available slots to the slower queries.
   • BUse Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.
   • CUse available administrative resource charts to determine how slots are being used and how jobs are performing over time. Run a query on the INFORMATION_SCHEMA to review query performance.
   • DUse Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.

   Show answer

   Answer: C

   Explanation

   - BigQuery provides administrative resource charts that show slot utilization and job performance, which can help identify patterns of heavy usage or contention. - Additionally, querying the INFORMATION_SCHEMA with the JOBS or JOBS_BY_PROJECT view can provide detailed information about specific queries, including execution time, slot usage, and whether they were queued.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation

   Next question

10. Question 150 · 340 in bank

    You migrated a data backend for an application that serves 10 PB of historical product data for analytics. Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

    • A1. Store the historical data in BigQuery for analytics. 2. Use a materialized view to precompute the last state of a product. 3. Serve the last state data directly from BigQuery to the API.
    • B1. Store the products as a collection in Firestore with each product having a set of historical changes. 2. Use simple and compound queries for analytics. 3. Serve the last state data directly from Firestore to the API.
    • C1. Store the historical data in Cloud SQL for analytics. 2. In a separate table, store the last state of the product after every product change. 3. Serve the last state data directly from Cloud SQL to the API.
    • D1. Store the historical data in BigQuery for analytics. 2. In a Cloud SQL table, store the last state of the product after every product change. 3. Serve the last state data directly from Cloud SQL to the API.

    Show answer

    Answer: D

    Explanation

    D. 1. Store the historical data in BigQuery for analytics. 2. In a Cloud SQL table, store the last state of the product after every product change. 3. Serve the last state data directly from Cloud SQL to the AP This approach leverages BigQuery's scalability and efficiency for handling large datasets for analytics. BigQuery is well-suited for managing the 10 PB of historical product data. Meanwhile, Cloud SQL provides the necessary performance to handle the API queries with the required low latency. By storing the latest state of each product in Cloud SQL, you can efficiently handle the high QPS with sub-second latency, which is crucial for the API's performance. This combination of BigQuery and Cloud SQL offers a balanced solution for both the large-scale analytics and the high-performance API needs.

    Source: Adapted from community discussion — datapassionate

    References

    • Google Cloud documentation

    Next question

Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page

PDE practice, question 141. 340 questions available.