Professional-Data-Engineer Exam Questions & Answers
Google Cloud Certified Professional Data Engineer • Google
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Sample Professional-Data-Engineer Questions
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Which of these sources can you not load data into BigQuery from?
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?
https://cloud.google.com/logging/docs/view/logs-explorer-interface?cloudshell=true
Government regulations in the banking industry mandate the protection of client's personally identifiable information (PII). Your company requires PII to be access controlled encrypted and compliant with major data protection standards In addition to using Cloud Data Loss Prevention (Cloud DIP) you want to follow Google-recommended practices and use service accounts to control access to PII. What should you do?
You need to orchestrate a pipeline with several Google Cloud services: a batch Dataflow job, then a BigQuery query job followed by a Vertex AI batch prediction. The logic is sequential. You want a lightweight, serverless orchestration solution with minimal operational overhead. What service should you use?
When the requirement specifies a 'lightweight' and 'serverless' orchestration for Google Cloud APIs with 'minimal operational overhead,' Cloud Workflows is the preferred choice over Cloud Composer.
Lightweight and Serverless: Cloud Workflows is a fully managed, HTTP-based orchestration service that scales to zero and has no base cost. It is designed specifically to chain Google Cloud services together using YAML or JSON.
Operational Overhead: Unlike Cloud Composer (which requires managing a Kubernetes-based environment and has a minimum running cost), Workflows is truly serverless with no infrastructure to manage.
Service Integration: Workflows has built-in connectors for Dataflow, BigQuery, and Vertex AI, making it ideal for simple sequential logic.
Correcting other options:
B (Cloud Composer): While it can handle this logic, it is not 'lightweight.' It is better suited for complex data engineering pipelines with non-GCP dependencies.
C (Compute Engine): This is not serverless and requires significant operational overhead to manage the VM and cron state.
D (Dataproc/Oozie): This is a legacy Hadoop-based orchestration tool and is definitely not lightweight or serverless.
'Workflows is a fully managed orchestration platform that executes services in an order that you define... Workflows is serverless, scales to zero, and has no infrastructure to manage. It is ideal for orchestrating Google Cloud services like BigQuery, Dataflow, and Vertex AI with low latency.' (Source: Workflows product overview)
'Use Workflows for low-latency, high-volume, and lightweight orchestration of Google Cloud services.' (Source: Choose an orchestration service)
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
Decoupling producer from consumer
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
Near real-time SQL query
Maintain at least 2 years of historical data, which will be queried with SQ
Which pipeline should you use to meet these requirements?
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