Google Professional Cloud Data Engineer Actual Exam

Google Professional Cloud Data Engineer

This certification demonstrates expertise in designing, building, and maintaining scalable data systems and data processing pipelines on Google Cloud Platform.

Focus

The Google Professional Cloud Data Engineer Actual Exam certification focuses on Designing, Building, and Maintaining Scalable Data Systems and Data Processing Pipelines

Exam Duration

The Google Professional Cloud Data Engineer Actual Exam exam duration is 180 minutes (3 hours)

Num of Question

The exam contains a total of 50 questions

Exam Fee

The exam fee is $200.

Exam Validity

The CCRN-Pediatric certification is valid for Exam result is valid for 2 years.

Prior Certification

• No specific prior certification required

Exam Format

The Google Professional Cloud Data Engineer Actual Exam exam is a Multiple choice, multiple select, and hands-on question formats

Passing Conditions

To pass Google Professional Cloud Data Engineer Actual Exam exam, applicants must achieve a minimum score of Score at least 720 out of 1000

Who Should Take

Data engineers, cloud architects, software engineers, and professionals responsible for designing, building, and maintaining large-scale data systems

• Data Engineer
• Cloud Architect
• Software Engineer

Prerequisites

1 year or more experience with Cloud Dataflow, BigQuery, Cloud Storage, and Cloud Datastore or equivalent experience

Or can substitute with certifications:

• Cloud Certified - Professional Data Engineer
• Google Cloud Certified - Professional Data Engineer

Certification Process

• Step 1: Meet the prerequisite experience requirements
• Step 2: Register for the exam
• Step 3: Complete the exam within the given time frame
• Step 4: Score at least 720/1000 points to pass the exam
• Step 5: Maintain the certification by completing the renewal requirements after 2 years

Domains with Weights

• Design and Build Scalable Data Systems (45%): Designing data processing pipelines, data warehousing, data transformation, and data governance. Experience with building scalable data systems and data workflows using BigQuery, Cloud Storage, and Cloud Dataflow.
• Implement and Manage Data Pipelines (26%): Designing, building, and running data pipelines using Cloud Dataflow, Cloud Storage, and BigQuery. Managing data flows and monitoring data pipelines.
• Work with Unstructured Data and Visualize Insights (15%): Understanding and processing unstructured data, image analysis, and text analysis using Cloud Storage and TensorFlow. Visualizing data insights and information using Google Data Studio and Google Cloud Platform Data Studio.
• Implement Cybersecurity and Governance (10%): Data access governance and data security using Cloud Identity and Access Management (IAM). Data encryption using Cloud Key Management Service (KMS).
• Implement Big Data and Machine Learning Workloads (4%): Experience working with BigQuery, Cloud Dataflow, and Cloud Storage. Implementing machine learning using TensorFlow and predicting outcomes using BigQuery ML and Cloud AI Platform.