CERTALYTH Certified Data Engineer – Professional

CCDE-P · CT-DE-P · Big Data and Data Engineering on Google Cloud

Design scalable data systems and pipelines on Google Cloud.

Google Cloud Professional Data Engineer

40 hoursCertificate with unique codeMultiple-choice exam

199EUR

Full bundle: course PDF + online exam + certification.

Recertification every 3 years: 99 EUR

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About the course

Learn to design, build, and operate data processing systems on Google Cloud, including ingestion, transformation, storage, and large-scale analysis. Aligned with the Google Cloud Professional Data Engineer exam.

Focus: Data engineering on GCP: BigQuery, Dataflow, pipelines, and data governance.

What you'll learn

Design batch and streaming data pipelines

Model and optimize data in BigQuery

Ensure data quality, security, and governance

Operationalize data solutions at scale

Sample questions (free)

A sample from the official exam set — correct answers are revealed only during the actual exam.

CERTALYTH Certified Data Engineer – Professional

About the course

What you'll learn

Sample questions (free)

You need to run a recurring ETL job that relies on Spark libraries, performs heavy shuffles, uses custom JARs, and requires tuning of executors and memory. The job runs several times per day. Which Google Cloud service is the best fit?

An application requires a globally-distributed relational database that supports strongly consistent ACID transactions and horizontal scale across regions with low-latency reads and writes. Which managed service should you choose?

Your team needs to develop data pipelines locally, run unit tests, and then execute the same pipeline on multiple runtime engines in production (on-premise and cloud). Which approach best supports portability and consistent semantics across environments?

You are designing an ingestion pipeline from Pub/Sub to BigQuery and must ensure duplicate messages are not written to the same target row when retries occur. Which design choice most directly prevents duplicate rows in BigQuery streaming inserts?

You expect a highly-variable number of consumers and unpredictable throughput from publishers. Which Pub/Sub subscription mode gives consumers the most control over flow, ack timing, and scalable parallel consumption?