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Career Path
Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.
Training Courses Recommended for the Exam Preparation
Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions
The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems (~30% of the exam) | 30% | - Designing data pipelines
|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
|
| Storing the data (~20% of the exam) | 20% | - Using a data lake
|
| Ingesting and processing the data (~20% of the exam) | 20% | - Deploying and operationalizing the pipelines
|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Automating data processes
|
Google Certified Professional Data Engineer Sample Questions:
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
- Executing the transformations on a schedule
- Enabling non-developer analysts to modify transformations
- Providing a graphical tool for designing transformations
What should you do?
- A. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
- B. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
- C. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query
- D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
Correct Answer: B 🗳️
You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?
- A. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
- B. Use bq load to load a batch of sensor data every 60 seconds.
- C. Use the MERGE statement to apply updates in batch every 60 seconds.
- D. Use the INSERT statement to insert a batch of data every 60 seconds.
Correct Answer: A 🗳️
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?
- A. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
- B. Specify the Tableobject in the code.
- C. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
- D. Use .fromQuery operation to read specific fields from the table.
Correct Answer: D 🗳️
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When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
- A. 500 GB
- B. 1 GB
- C. 1 TB
- D. 500 TB
Correct Answer: C 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?
- A. Use gsutil cp J to compress the content being uploaded to Cloud Storage
- B. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than
20 Mb/sec so it does not interfere with the production traffic - C. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
- D. Use Transfer Appliance to copy the data to Cloud Storage
Correct Answer: D 🗳️
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