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[General] Real Google Professional-Data-Engineer Exam Environment with Our Practice Test E

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【General】 Real Google Professional-Data-Engineer Exam Environment with Our Practice Test E

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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 (Q314-Q319):NEW QUESTION # 314
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?
  • A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
  • B. Convert the streaming insert code to batch load for individual messages.
  • C. Re-write the application to load accumulated data every 2 minutes.
  • D. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
Answer: D
Explanation:
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage

NEW QUESTION # 315
Scaling a Cloud Dataproc cluster typically involves ____.
  • A. deleting applications from unused nodes periodically
  • B. increasing or decreasing the number of master nodes
  • C. increasing or decreasing the number of worker nodes
  • D. moving memory to run more applications on a single node
Answer: C
Explanation:
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1) increase the number of workers to make a job run faster
2) decrease the number of workers to save money
3) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage

NEW QUESTION # 316
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
  • A. Trigger based on element count
  • B. Trigger based on element size in bytes
  • C. Trigger that is a combination of other triggers
  • D. Trigger based on time
Answer: B
Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 317
What are two of the characteristics of using online prediction rather than batch prediction?
  • A. Predictions are returned in the response message.
  • B. It is optimized to minimize the latency of serving predictions.
  • C. Predictions are written to output files in a Cloud Storage location that you specify.
  • D. It is optimized to handle a high volume of data instances in a job and to run more complex models.
Answer: A,B
Explanation:
Online prediction
Optimized to minimize the latency of serving predictions.
Predictions returned in the response message.
Batch prediction
Optimized to handle a high volume of instances in a job and to run more complex models.
Predictions written to output files in a Cloud Storage location that you specify.
Reference:
https://cloud.google.com/ml-engi ... us_batch_prediction

NEW QUESTION # 318
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.
You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?
  • A. The zone
  • B. The maximum number of workers
  • C. The disk size per worker
  • D. The number of workers
Answer: A

NEW QUESTION # 319
......
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