Unveil details about the GCP-PDE exam topics and questions. Become familiar with the proven practice tests, study guide materials, and sample questions. It will help to make your Google Cloud Platform – Professional Data Engineer (GCP-PDE) certification journey easy.
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Google GCP-PDE Exam Summary:-
- Name of the Exam: Google Professional Data Engineer
- Exam Code: GCP-PDE
- Exam Price: $200 USD
- Duration: 120 minutes
- Number of Questions: 50
- Passing Score: 70%
Google GCP-PDE Exam Topics:-
- Designing data processing systems
- Building and operationalizing data processing systems
- Operationalizing machine learning models
- Ensuring solution quality
Improve Your Knowledge-Base:
Get access to some of the sample GCP-PDE practice test questions to boost your knowledge base. Preparation is a steady process for some time, and you must always try to keep improving via learning. Access the questions from anywhere and from any device. The easy access to the questions would help you to get better with the GCP-PDE exam topics. You can solve these questions easily or learn new things from them.
Here Are Some Useful Questions:
01. Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance.
How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?
a) Use a row key of the form <timestamp>.
b) Use a row key of the form <sensorid>.
c) Use a row key of the form <timestamp>#<sensorid>.
d) Use a row key of the form <sensorid>#<timestamp>.
Answer: d
02. Your company is loading comma-separated values (CSV) files into BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.
What is the most likely cause of this problem?
a) The CSV data loaded in BigQuery is not flagged as CSV.
b) The CSV data had invalid rows that were skipped on import.
c) The CSV data has not gone through an ETL phase before loading into BigQuery.
d) The CSV data loaded in BigQuery is not using BigQuery’s default encoding.
Answer: d
03. You have 250,000 devices which produce a JSON device status event every 10 seconds. You want to capture this event data for outlier time series analysis. What should you do?
a) Ship the data into BigQuery. Develop a custom application that uses the BigQuery API to query the dataset and displays device outlier data based on your business requirements.
b) Ship the data into BigQuery. Use the BigQuery console to query the dataset and display device outlier data based on your business requirements.
c) Ship the data into Cloud Bigtable. Use the Cloud Bigtable cbt tool to display device outlier data based on your business requirements.
d) Ship the data into Cloud Bigtable. Install and use the HBase shell for Cloud Bigtable to query the table for device outlier data based on your business requirements.
Answer: c
04. You need to stream time-series data in Avro format, and then write this to both BigQuery and Cloud Bigtable simultaneously using Dataflow. You want to achieve minimal end-to-end latency.
Your business requirements state this needs to be completed as quickly as possible. What should you do?
a) Create a pipeline and use ParDo transform.
b) Create a pipeline that groups the data into a PCollection and uses the Combine transform.
c) Create a pipeline that groups data using a PCollection, and then use Avro I/O transform to write to Cloud Storage. After the data is written, load the data from Cloud Storage into BigQuery and Bigtable.
d) Create a pipeline that groups data using a PCollection and then uses Bigtable and BigQueryIO transforms.
Answer: d
05. You are designing a relational data repository on Google Cloud to grow as needed. The data will be transactionally consistent and added from any location in the world.
You want to monitor and adjust node count for input traffic, which can spike unpredictably. What should you do?
a) Use Cloud Spanner for storage. Monitor CPU utilization and increase node count if more than 70% utilized for your time span.
b) Use Cloud Spanner for storage. Monitor storage usage and increase node count if more than 70% utilized.
c) Use Cloud Bigtable for storage. Monitor data stored and increase node count if more than 70% utilized.
d) Use Cloud Bigtable for storage. Monitor CPU utilization and increase node count if more than 70% utilized for your time span.
Answer: a
Self-Assessment with GCP-PDE Practice Test:
You can check preparation before the final exam by trying the Google GCP-PDE practice test. The tests are handy in web-based and desktop versions. You can track your progress, with our customizable mock exams. The tests help to identify the weaker areas of preparation. They also help remove mistakes before the actual GCP-PDE Google Professional Data Engineer exam. These exam simulation tests help you get rid of anxiety about the actual exam by offering a real exam-like scenario.
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It is a proven fact that success in the GCP-PDE exam is not achieved easily, but you can do it with ease if you have a trusted study partner. Think about the career scopes that will open for you after earning the Google Cloud Platform – Professional Data Engineer (GCP-PDE) certification. You can get some of the most real questions that will boost your exam preparation. At VMExam.com, we offer you questions with an actual exam environment that gradually aids your preparation level and earns you the GCP-PDE certification on your first attempt.
