FREE PDF 2025 PROFESSIONAL-DATA-ENGINEER: HIGH HIT-RATE VALID DUMPS GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM FREE

Free PDF 2025 Professional-Data-Engineer: High Hit-Rate Valid Dumps Google Certified Professional Data Engineer Exam Free

Free PDF 2025 Professional-Data-Engineer: High Hit-Rate Valid Dumps Google Certified Professional Data Engineer Exam Free

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Google Certified Professional Data Engineer Exam Sample Questions (Q339-Q344):

NEW QUESTION # 339
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

  • A. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
  • B. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
  • C. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
  • D. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

Answer: A


NEW QUESTION # 340
You need to compose visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
The report must not be more than 3 hours delayed from live data.
The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

  • A. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
  • B. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible
    combination of criteria, and spread them across multiple tabs.
  • C. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
  • D. Look through the current data and compose a series of charts and tables, one for each possible
    combination of criteria.

Answer: C


NEW QUESTION # 341
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour.
How should you design the solution?

  • A. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
  • B. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
  • C. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
  • D. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.

Answer: A


NEW QUESTION # 342
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes.
The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

  • A. HDFS with Hive
  • B. Redis
  • C. MySQL
  • D. HBase
  • E. MongoDB
  • F. Cassandra

Answer: A,D,E


NEW QUESTION # 343
Your analytics team wants to build a simple statistical model to determine which customers are most likely
to work with your company again, based on a few different metrics. They want to run the model on Apache
Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud
Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on
a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.
How should you optimize the cluster for cost?

  • A. Use a higher-memory node so that the job runs faster
  • B. Use SSDs on the worker nodes so that the job can run faster
  • C. Migrate the workload to Google Cloud Dataflow
  • D. Use pre-emptible virtual machines (VMs) for the cluster

Answer: C


NEW QUESTION # 344
......

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