Reading logs and metrics to work out what a pipeline actually did
Cloud Logging and Cloud Monitoring are the primary tools for observing data pipeline health, letting you review execution logs, spot errors, and track performance metrics for services like Dataflow, Cloud Composer, and Dataproc. Logs Explorer lets you filter and query log entries, while Monitoring dashboards and alerting policies help you proactively detect issues before they impact downstream data consumers.
Must-know
- Cloud Logging automatically ingests logs from most GCP data services (Dataflow, Dataproc, Cloud Composer/Airflow, BigQuery jobs) without extra configuration, viewable in the Logs Explorer.
- Logs Explorer supports a structured query language for filtering by resource type, log severity (e.g., ERROR, WARNING), and text/label matches, which is essential for troubleshooting failed pipeline runs.
- Cloud Composer/Airflow task logs are also written to Cloud Logging and are accessible both via the Airflow UI and Logs Explorer, useful when diagnosing DAG task failures.
- Cloud Monitoring provides prebuilt and custom dashboards plus alerting policies based on metrics (e.g., Dataflow job status, worker CPU, pipeline latency) to notify teams of anomalies via channels like email or Pub/Sub.
- Log-based metrics can be created in Cloud Logging to turn specific log patterns (e.g., a certain error message) into a Cloud Monitoring metric for alerting or dashboarding.
- Logs are retained for a default period (typically 30 days for most log types) unless routed to a log sink for longer-term storage (e.g., BigQuery, Cloud Storage) for audit or historical analysis.
A data engineer needs to investigate why a Dataflow batch job failed overnight. They want to see only the ERROR-severity log entries produced by that specific job within the last 24 hours. Which approach in Cloud Logging accomplishes this most directly?
What you have tried across GCP ADP's objectives, not a readiness score.
Data Preparation and Ingestion
- When to load first and when to transform first, and what sits between the two
- Picking a way to move existing data into Google Cloud
- Judging whether a dataset is trustworthy enough to build on
- Fixing messy records before they reach a report
- Telling CSV, JSON, Parquet, Avro, and relational tables apart, and where each fits
- Picking how to pull data out of a source system
- Matching a workload to the right storage or database service
- Getting files and tables loaded with a CLI, a transfer service, or a client library
Data Analysis and Presentation
- Writing BigQuery SQL that answers a reporting question
- Exploring and charting data inside a hosted notebook
- Turning a question from the business into an analysis that settles it
- Building a dashboard and getting it in front of the right people
- Deciding whether a job calls for Looker or for Looker Studio
- Editing LookML to change what a model exposes
- Spotting a problem worth solving with BigQuery ML or AutoML
- Calling a hosted Google language model straight from BigQuery
- Sequencing a machine learning project from raw data to served predictions
- Building, fitting, and scoring a model with SQL alone
- Running predictions against a model you already trained
- Keeping trained models catalogued in one place
Data Pipeline Orchestration
- Matching a transformation job to Dataproc, Dataflow, Dataform, or a managed alternative
- Weighing whether the transform belongs before or after the load
- Assembling the services a simple transformation pipeline needs
- Putting a query on a schedule and keeping it running
- Watching a Dataflow job and spotting where it stalls
- Reading logs and metrics to work out what a pipeline actually did
- Choosing what should drive a multi-step workflow
- Streaming messages into BigQuery as they arrive rather than in batches
- Wiring a trigger so one event starts the next step
Data Management
- Granting only the access a person or service actually needs
- Controlling who can read a bucket, and what uniform access changes
- Sharing a dataset with another team or company without copying it
- Matching a storage class to how often the data gets read
- Expiring old data automatically so it stops costing money
- Picking somewhere to park data that must be kept but is rarely read
- Comparing the managed backup and restore options across services
- Working out when a second copy is worth what it costs
- Regions, dual-regions, multi-regions, and zones as redundancy choices
- Deciding who should hold the encryption keys
- What a key management service does for creating, rotating, and revoking keys
- Protecting data on the wire versus data sitting on a disk
Coverage checked against the published exam guide on Aug 12, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.