What about making a career with the GCP-PDE certification? Find out the free GCP-PDE sample questions, study guide PDF, and practice tests for a successful Google Cloud Platform – Professional Data Engineer (GCP-PDE) career start.
These materials are proven and help the candidate to pass the exam on their first attempt.
What Is the Google GCP-PDE Exam Structure?
The Professional Data Engineer exam is a multiple-choice exam, with 50 questions. You need to get a 70% mark to pass the Professional Data Engineer exam. The Google Cloud Platform – Professional Data Engineer (GCP-PDE) is suitable for candidates who are interested to learn more on the Cloud. The official price for the exam is $200 USD.
What Should Be Your Study Method for the GCP-PDE Exam Preparation?
Once you are determined to take the GCP-PDE exam, you must get ready with a study guide that combines all your necessary actions during the preparation and materials in one place.
Visit the Official Page for More Clarity:
Visiting the official page could feel a simple task, but a candidate must make sure, that he is not missing out any valuable information regarding the GCP-PDE exam. One must visit the official page at the beginning of their preparation to find out about the training and other online resources.
Work on the GCP-PDE Syllabus Topics:
The basic action of the GCP-PDE exam candidate should be going through the syllabus details and making out a chart to cover the syllabus topics on time. When it comes to covering the syllabus topics, books and online resources work best to ace the exam.
Success in the Google GCP-PDE exam is highly dependent on grasping the syllabus topics from the core. The more grasp you have, the more is the chance to succeed quickly. Do not hurry to learn the exam topics; learn one at a time. You can also target covering two to three topics daily from the core, but make sure that you don’t move to the next topic, until you finish one.
Increase Your Productivity through Routine Making:
How to make your study schedule the most productive? If the aspirant follows a planned routine, he is going to experience a more productive preparation. You might be a student, or a working professional, choose your productive time according to your current work and plan out your productive hours. If you want to enhance your productivity during the preparation, you must set aside your study hours. Focusing on daily study would help to learn the syllabus topics in a better manner.
Develop Writing Habit:
If you develop the habit of writing essential points during the study, you can revise quickly through these notes. Your study routine should be such that you can properly utilize the study resources. Therefore, follow some proven steps to pass the exam.
When Is the Right Time to Explore GCP-PDE Sample Questions & Mock Tests?
- The potential Google GCP-PDE certification candidates should not restrict themselves to learning the syllabus topics only. They can add more value to their preparation; if they explore different GCP-PDE sample questions through PDF format or regular format, their knowledge base could become stronger.
- The best time to explore sample questions is at the end of syllabus completion. Many valuable websites offer trusted and free sample questions for the GCP-PDE exam preparation.
- The preparation process is always better with these sample questions and practice test combinations. Many aspirants opt for the GCP-PDE dumps PDF materials and end up losing confidence in the exam hall during the actual exam preparation process.
- You can learn from the dumps materials, but working with GCP-PDE dumps PDF won’t help to assess your preparation level. Taking GCP-PDE mock exams would help the aspirant to get ready with the actual exam structure, and a candidate becomes an expert regarding time management through this process.
- Therefore, drop your focus from GCP-PDE exam related dumps PDF and get valuable insights through Professional Data Engineer practice tests.
- It is always essential to get the real exam experience before you reach the exam hall. GCP-PDE practice tests, work best in this regard. Continuous practicing helps in getting familiar with the actual exam structure and makes your journey easy while taking the exam.
- VMExam.com offers one of the most valuable practice tests for self assessment. The time-based practice tests help an aspirant to gain ideas on their time management level and answering capacity. The candidates may face difficulty during initial attempts, but through gradual practice, their knowledge base, speed, and marks improve.
- Don’t lose hope, if you are scoring poor in your initial attempts, take it as learn only approach, and be determined to work on the lacking syllabus sections.
How Does the GCP-PDE Certification Benefit You?
The purpose of becoming the Google Cloud Platform – Professional Data Engineer (GCP-PDE) is not only gaining knowledge. The aspirant earns the maximum advantage when they face any interview. With the Professional Data Engineer certification on their resume, the credibility of the aspirant is proved to the employers over other non-certified peers. Having the Professional Data Engineer certification, also helps the aspirants to negotiate well for new job roles or for salary hike.
Here Are Few GCP-PDE Sample Questions for Your Knowledge:
01. 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.
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.
03. You are building storage for files for a data pipeline on Google Cloud. You want to support JSON files. The schema of these files will occasionally change.
Your analyst teams will use running aggregate ANSI SQL queries on this data. What should you do?
a) Use BigQuery for storage. Provide format files for data load. Update the format files as needed.
b) Use BigQuery for storage. Select “Automatically detect” in the Schema section.
c) Use Cloud Storage for storage. Link data as temporary tables in BigQuery and turn on the “Automatically detect” option in the Schema section of BigQuery.
d) Use Cloud Storage for storage. Link data as permanent tables in BigQuery and turn on the “Automatically detect” option in the Schema section of BigQuery.
04. 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>.
05. You are designing storage for CSV files and using an I/O-intensive custom Apache Spark transform as part of deploying a data pipeline on Google Cloud. You intend to use ANSI SQL to run queries for your analysts.
How should you transform the input data?
a) Use BigQuery for storage. Use Dataflow to run the transformations.
b) Use BigQuery for storage. Use Dataproc to run the transformations.
c) Use Cloud Storage for storage. Use Dataflow to run the transformations.
d) Use Cloud Storage for storage. Use Dataproc to run the transformations.
