DUMPSKING'S PROFESSIONAL-DATA-ENGINEER DUMPS QUESTIONS WITH 365 DAYS FREE UPDATES

DumpsKing's Professional-Data-Engineer Dumps Questions With 365 Days Free Updates

DumpsKing's Professional-Data-Engineer Dumps Questions With 365 Days Free Updates

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To be eligible for the Google Professional-Data-Engineer Certification, candidates need to have a strong understanding of data engineering principles and practical experience in building and deploying data processing systems. Google Certified Professional Data Engineer Exam certification is ideal for professionals who work in data engineering or related fields, such as data analysis, business intelligence, and data science. It is also suitable for individuals who aspire to become data engineers and want to learn more about the field.

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Google Certified Professional Data Engineer Exam Sample Questions (Q359-Q364):

NEW QUESTION # 359
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

  • A. Redefine the schema by evenly distributing reads and writes across the row space of the table.
  • B. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
  • C. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.
  • D. The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Answer: A

Explanation:
https://cloud.google.com/bigtable/docs/performance#troubleshooting
If you find that you're reading and writing only a small number of rows, you might need to redesign your schema so that reads and writes are more evenly distributed.


NEW QUESTION # 360
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 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
  • C. 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.
  • D. 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

Answer: B

Explanation:
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.


NEW QUESTION # 361
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

  • A. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
  • B. They have not assigned the timestamp, which causes the job to fail
  • C. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
  • D. They have not set the triggers to accommodate the data coming in late, which causes the job to fail

Answer: C


NEW QUESTION # 362
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)

  • A. Preserve the structure of the data as much as possible.
  • B. Denormalize the data as must as possible.
  • C. Use BigQuery UPDATE to further reduce the size of the dataset.
  • D. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
  • E. Develop a data pipeline where status updates are appended to BigQuery instead of updated.

Answer: D,E


NEW QUESTION # 363
You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated dat
a. 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 Bl Engine with logical reviews
  • B. Use BigQuery BI Engine with streaming data.
  • C. Use BigQuery BI Engine with materialized views
  • D. Use BigQuery Bl Engine with authorized views

Answer: B


NEW QUESTION # 364
......

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