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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

  • Data engineers
  • Data scientists
  • Data analysts

Google Professional-Data-Engineer Exam Syllabus Topics:

Topic Details
Topic 1
  • Modeling Business Processes for Analysis and Optimization
Topic 2
  • Designing Data Processing Systems
  • Flexible Data Representation
Topic 3
  • Building and Maintaining Data Structures and Databases
Topic 4
  • Analyzing Data and Enabling Machine Learning
Topic 5
  • Visualizing Data and Advocating Policy
  • Automation
  • Decision Support
  • Data Summarization

The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google. >> Professional-Data-Engineer Exam Test <<

New Professional-Data-Engineer Test Answers | New Professional-Data-Engineer Test Experience

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Google Certified Professional Data Engineer Exam Sample Questions (Q151-Q156):

NEW QUESTION # 151
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by locationid and deviceversion with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

  • A. Partition table data by createdate, locationid and device_version
  • B. Cluster table data by createdate locationid and device_version
  • C. Cluster table data by createdate, partition by location and deviceversion
  • D. Partition table data by createdate cluster table data by tocationid and device_version

Answer: B
NEW QUESTION # 152
Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

  • A. You will not use the data to back a user-facing or latency-sensitive application.
  • B. You expect to store at least 10 TB of data.
  • C. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
  • D. You need to integrate with Google BigQuery.

Answer: D Explanation:
For example, if you plan to store extensive historical data for a large number of remote- sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage-reads would be much more frequent in this case, and reads are much slower with HDD storage.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd
NEW QUESTION # 153
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. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
  • C. Establish a Cloud Interconnect between all remote data centers and Google.
  • D. Have the data acquisition devices publish data to Cloud Pub/Sub.

Answer: D Explanation:
Pubsub is global service with high message delivery capacity.
NEW QUESTION # 154
Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

  • A. Increase the size of the Hadoop cluster.
  • B. Rewrite the job in Pig.
  • C. Rewrite the job in Apache Spark.
  • D. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

Answer: B
NEW QUESTION # 155
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the
shade of each dot represents what class it is. You want to classify this data accurately using a linear
algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?