Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 321:

  You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated data. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?

  A. Use BigQuery BI Engine with materialized views

  B. Use BigQuery BI Engine with streaming data.

  C. Use BigQuery Bl Engine with authorized views

  D. Use BigQuery Bl Engine with logical reviews

  Correct Answer: B

• Question 322:

  You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

  A. 1 Use a regional Cloud Storage bucket 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by one day to recover the acknowledged messages 4 Start the Dataflow job in a secondary region and write in a bucket in the same region

  B. 1 Use a multi-regional Cloud Storage bucket 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region

  C. 1. Use a dual-region Cloud Storage bucket.

  2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs

  3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages

  4 Start the Dataflow job in a secondary region

  D. 1. Use a dual-region Cloud Storage bucket with turbo replication enabled 2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs 3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages 4 Start the Dataflow job in a secondary region.

  Correct Answer: C

  A dual-region Cloud Storage bucket is a type of bucket that stores data redundantly across two regions within the same continent. This provides higher availability and durability than a regional bucket, which stores data in a single region. A dual-region bucket also provides lower latency and higher throughput than a multi-regional bucket, which stores data across multiple regions within a continent or across continents. A dual-region bucket with turbo replication enabled is a premium option that offers even faster replication across regions, but it is more expensive and not necessary for this scenario. By using a dual-region Cloud Storage bucket, you can ensure that your data is protected from regional outages, and that you can access it from either region with low latency and high performance. You can also monitor the Dataflow metrics with Cloud Monitoring to determine when an outage occurs, and seek the subscription back in time by 15 minutes to recover the acknowledged messages. Seeking a subscription allows you to replay the messages from a Pub/Sub topic that were published within the message retention duration, which is one day in this case. By seeking the subscription back in time by 15 minutes, you can meet the RPO of 15 minutes, which means the maximum amount of data loss that is acceptable for your business. You can then start the Dataflow job in a secondary region and write to the same dual-region bucket, which will resume the processing of the messages and prevent data loss. Option A is not a good solution, as using a regional Cloud Storage bucket does not provide any redundancy or protection from regional outages. If the region where the bucket is located experiences an outage, you will not be able to access your data or write new data to the bucket. Seeking the subscription back in time by one day is also unnecessary and inefficient, as it will replay all the messages from the past day, even though you only need to recover the messages from the past 15 minutes. Option B is not a good solution, as using a multi-regional Cloud Storage bucket does not provide the best performance or cost-efficiency for this scenario. A multi-regional bucket stores data across multiple regions within a continent or across continents, which provides higher availability and durability than a dual-region bucket, but also higher latency and lower throughput. A multi-regional bucket is more suitable for serving data to a global audience, not for processing data with Dataflow within a single continent. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes. Option D is not a good solution, as using a dual-region Cloud Storage bucket with turbo replication enabled does not provide any additional benefit for this scenario, but only increases the cost. Turbo replication is a premium option that offers faster replication across regions, but it is not required to meet the RPO of 15 minutes. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes. References: Storage locations | Cloud Storage | Google Cloud, Dataflow metrics | Cloud Dataflow | Google Cloud, Seeking a subscription | Cloud Pub/Sub | Google Cloud, Recovery point objective (RPO) | Acronis.

• Question 323:

  You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app You have reviewed old chat logs and lagged each conversation for intent based on each customer's stated intention for contacting customer service About 70% of customer requests are simple requests that are solved within 10 intents The remaining 30% of inquiries require much longer, more complicated requests Which intents should you automate first?

  A. Automate the 10 intents that cover 70% of the requests so that live agents can handle more complicated requests

  B. Automate the more complicated requests first because those require more of the agents' time

  C. Automate a blend of the shortest and longest intents to be representative of all intents

  D. Automate intents in places where common words such as "payment" appear only once so the software isn't confused

  Correct Answer: A

• Question 324:

  An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?

  A. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.

  B. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.

  C. Write an application that makes a queue in a NoSQL database

  D. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic

  Correct Answer: C

• Question 325:

  Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

  A. 1 Create a project for storage of the data for your organization. 2 Create a central Cloud Storage bucket with three folders to store the files for each department.

  3. Create a central BigQuery dataset with tables prefixed with the department name.

  4 Give viewer rights for the storage project for the users of your departments.

  B. 1Create a project for storage of the data for each of your departments. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets.

  3. Create user groups for authorized readers for each bucket and dataset.

  4 Enable the IT team to administer the user groups to add or remove users as the departments' request.

  C. 1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets.

  3. Publish the data that each department shared in Analytics Hub.

  4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.

  D. 1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones. 4 Enable each department to own and share the data of their data lakes.

  Correct Answer: C

  Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department,

  which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in

  Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.

  References:

  Architecture and functions in a data mesh - Google Cloud Professional Data Engineer Certification uide | Learn - Google Cloud Build a Data Mesh with Dataplex | Google Cloud Skills Boost

• Question 326:

  You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

  A. Deploy small Kafka clusters in your data centers to buffer events.

  B. Have the data acquisition devices publish data to Cloud Pub/Sub.

  C. Establish a Cloud Interconnect between all remote data centers and Google.

  D. Write a Cloud Dataflow pipeline that aggregates all data in session windows.

  Correct Answer: B

• Question 327:

  You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

  A. Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.

  B. Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

  C. Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance

  D. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.

  Correct Answer: D

  The import and export feature uses the native RDB snapshot feature of Redis to import data into or export data out of a Memorystore for Redis instance. The use of the native RDB format prevents lock-in and makes it very easy to move data within Google Cloud or outside of Google Cloud. Import and export uses Cloud Storage buckets to store RDB files. Reference: https://cloud.google.com/memorystore/docs/redis/import-export-overview

• Question 328:

  You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

  A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

  B. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

  C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

  D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

  Correct Answer: A

• Question 329:

  You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

  A. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

  B. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.

  C. Change the data pipeline to use BigQuery for storing stock trades, and update your application.

  D. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

  Correct Answer: A

• Question 330:

  You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

  A. Create an authorized view that contains the CMEK to decrypt the data when accessed.

  B. Provide the partner organization a copy of your CMEKs to decrypt the data.

  C. Copy the tables you need to share to a dataset without CMEKs Create an Analytics Hub listing for this dataset.

  D. Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights. viewer role on the bucket to the partner organization.

  Correct Answer: C

  If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK,

  because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service

  that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and

  also control the level of access and the duration of the sharing. References:

  Customer-managed Cloud KMS keys

  [Authorized views]

  [Analytics Hub overview]

  [Creating an Analytics Hub listing]