An Overview Of GCP-Data Engineer Exams

A GCP-Data Engineer is someone who can make data-driven decisions through data processing systems and by collecting, transforming, and publishing data. A Data engineer can run and design data processing systems. He can build and run secured data processing systems and monitor data processing systems through a focus on security, scalability, and reliability. Professional Data Engineer ensures flexibility, fidelity, operationalization, and compliance of data processing systems.

Prerequisites for the GCP-Data Engineer Exam

There are no prerequisites, either in terms of experience in the industry or required attendance of a training course. However, it is recommended that you have3+ years of industry experience including 1+ years of designing and managing solutions using Google cloud storage platforms.

GCP-Data Engineer Exam Details

The Professional Data Engineer Certification exam comprises 50 multiple-choice questions, with a duration of 120 minutes. The registration fee averages $200 per attempt. You can take the exams remotely or in person at a test center. The exam consists only of multiple-answer choice items. The exam is valid for 2 years from the date of passing the exam. Recertification is accomplished by retaking the exam during the recertification eligibility period and achieving a passing score. However, there is no passing score given to pass the exam. Data-driven decision-making skill is important.

GCP-Data Engineer Exam Info

Exam Name: Google Professional Cloud Architect

Total Questions: 50 questions

Passing Score: 70 percent score on the overall exam

Exam Duration: 120 minutes

Exam Type: Multiple-Choice Questions

Exam Cost: USD 200 (plus tax where applicable)

Prerequisite: None

Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.

Updated Course Outline For GCP-Data Engineer Exam

Section 1. Designing data processing systems

1.1 Selecting the appropriate storage technologies. Considerations include:

a. Mapping storage systems to business requirements

b. Data modeling

c. Trade-offs involving latency, throughput, transactions

d. Distributed systems

e. Schema design

1.2 Designing data pipelines. Considerations include:

a. Data publishing and visualization (e.g., BigQuery)

b. Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub, Apache Kafka)

c. Online (interactive) vs. batch predictions

d. Job automation and orchestration (e.g., Cloud Composer)

1.3 Designing a data processing solution. Considerations include:

a. Choice of infrastructure

b. System availability and fault tolerance

c. Use of distributed systems

d. Capacity planning

e. Hybrid cloud and edge computing

f. Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)

g. At least once, in order, and exactly once, etc., event processing

1.4 Migrating data warehousing and data processing. Considerations include:

a. Awareness of the current state and how to migrate a design to a future state

b. Migrating from on-premise to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)

c. Validating a migration

Section 2. Building and operationalizing data processing systems

2.1 Building and operationalizing storage systems. Considerations include:

a. Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore)

b. Storage costs and performance

c. Life cycle management of data

2.2 Building and operationalizing pipelines. Considerations include:

a. Data cleansing

b. Batch and streaming

c. Transformation

d. Data acquisition and import

e. Integrating with new data sources

2.3 Building and operationalizing processing infrastructure. Considerations include:

a. Provisioning resources

b. Monitoring pipelines

c. Adjusting pipelines

d. Testing and quality control

Section 3. Operationalizing machine learning models

3.1 Leveraging pre-built ML models as a service. Considerations include:

a. ML APIs (e.g., Vision API, Speech API)

b. Customizing ML APIs (e.g., AutoML Vision, Auto ML text)

c. Conversational experiences (e.g., Dialogflow)

3.2 Deploying an ML pipeline. Considerations include:

a. Ingesting appropriate data

b. Retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML)

c. Continuous evaluation

3.3 Choosing the appropriate training and serving infrastructure. Considerations include:

a. Distributed vs. single machine

b. Use of edge compute

c. Hardware accelerators (e.g., GPU, TPU)

3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include:

a. Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)

b. Impact of dependencies of machine learning models

c. Common sources of error (e.g., assumptions about data)

Section4. Ensuring solution quality

4.1 Designing for security and compliance. Considerations include:

a. Identity and access management (e.g., Cloud IAM)

b. Data security (encryption, key management)

c. Ensuring privacy (e.g., Data Loss Prevention API)

d. Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))

4.2 Ensuring scalability and efficiency. Considerations include:

a. Building and running test suites

b. Pipeline monitoring (e.g., Cloud Monitoring)

c. Assessing, troubleshooting, and improving data representations and data processing infrastructure

d. Resizing and autoscaling resources

4.3 Ensuring reliability and fidelity. Considerations include:

a. Performing data preparation and quality control (e.g., Dataprep)

b. Verification and monitoring

c. Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)

d. Choosing between ACID, idempotent, and eventually consistent requirements

4.4 Ensuring flexibility and portability. Considerations include:

a. Mapping to current and future business requirements

b. Designing for data and application portability (e.g., multicolored, data residency requirements)

c. Data staging, cataloging, and discovery

Frequently Asked Questions (FAQs)

Is Google Certified Professional Data Engineer certification worth it?

Google Professional Data Engineer certification is worth it for cloud engineers. If you are a cloud or data engineer and you are not able to implement minor technical configurations, then this certification will help you enhance your abilities in the implementation of technicalities in google storage and monitoring the google cloud platform and cloud dataflow.

How many attempts can be made to pass the GCP-Data Engineer exam?

If you fail to pass the exam on the first go, you can take it again within 14 days after the date of your previous attempt. You can take the exam for the third time as well if you fail on the second attempt and it can be done after 60 days. A wait for a year is required for those who fail to pass the exam on their third attempt.

Why should you become GCP-Data Engineer certified?

A Google Professional Data Engineer is valuable over many other data analysts. It is to enhance the experience of data analysts by expanding their familiarity with huge data processing systems, providing increased feature parameters and machine learning models, and providing advanced knowledge of actual data engineering and other attributes

How much does the GCP-Data Engineer certification cost?

GCP- Professional Data Engineer certification to $200 for the Professional level.