1Z0-1195-25 Syllabus — Learning Objectives by Topic

Use this syllabus as your checklist for 1Z0‑1195‑25.

What’s covered

• Topic 1: Data Platform Fundamentals
• Topic 2: Storage and Data Lake on OCI
• Topic 3: Ingestion and Integration
• Topic 4: Processing and Transformation (Batch)
• Topic 5: Streaming and Change Data Capture (CDC)
• Topic 6: Analytics and Consumption
• Topic 7: Security, Governance, and Operations

Topic 1: Data Platform Fundamentals

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1.1 Core data platform concepts (lake, warehouse, lakehouse)

• Differentiate a data lake, data warehouse, and lakehouse in terms of storage, governance, and consumption.
• Explain batch vs streaming processing and when each is appropriate.
• Recognize common data pipeline stages: ingest, validate, transform, serve, monitor.
• Given a scenario, choose ETL vs ELT based on where transformations should run.

1.2 Data engineering workflow and pipeline patterns

• Describe common pipeline patterns: scheduled batch, event-driven, micro-batch, and CDC-based replication.
• Explain why idempotency and retries are required for reliable ingestion jobs.
• Identify why metadata (source, lineage, timestamps) is essential for trustworthy analytics.
• Given a scenario, choose a pipeline pattern that meets freshness and resiliency requirements.

1.3 Data quality and analytics readiness

• Define core data quality dimensions: completeness, validity, timeliness, and consistency.
• Recognize common analytics failure modes caused by poor data quality (duplicate events, missing keys).
• Explain why slowly changing dimensions and late-arriving data complicate downstream reporting.
• Given a scenario, choose validation checks to run before publishing a dataset.

Topic 2: Storage and Data Lake on OCI

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2.1 Object Storage fundamentals for data platforms

• Explain how Object Storage is used for raw, curated, and published data zones.
• Given a scenario, choose Standard vs Archive tiers based on access patterns and cost.
• Recognize when multipart uploads and parallelism are required for large ingests.
• Identify common security controls for lake storage (encryption, IAM policies, private access).

2.2 Data formats, partitioning, and schema strategy

• Differentiate common analytical formats (Parquet, ORC, Avro) at a conceptual level.
• Given a scenario, choose partition keys that reduce scan cost and improve query performance.
• Explain schema-on-read vs schema-on-write and how it affects governance and flexibility.
• Recognize how compression and file sizing impact distributed processing performance.

2.3 Lifecycle management and organization

• Design a folder/object naming strategy that supports discovery and automation.
• Explain why retention policies and lifecycle rules reduce storage sprawl and cost.
• Recognize how tagging and compartments support access control and chargeback/showback.
• Given a scenario, choose governance controls for sensitive datasets (restricted compartments, audit).

Topic 3: Ingestion and Integration

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3.1 Ingestion sources and connectivity

• Identify common data sources: operational databases, SaaS apps, files, logs, and streams.
• Given a scenario, choose online vs offline transfer methods based on volume and time constraints.
• Explain why network connectivity and identity controls are prerequisites for reliable ingestion.
• Recognize when to stage data before transformation to support replay and backfills.

3.2 ETL/ELT tools and orchestration concepts

• Differentiate data integration vs processing engines and where each fits in the pipeline.
• Given a scenario, choose orchestration features needed (scheduling, dependencies, retries, alerts).
• Explain how parameterization enables environment promotion and reusable pipelines.
• Recognize the need for secrets management when connecting to protected data sources.

3.3 Validation, reconciliation, and backfills

• Design record-count and checksum-based reconciliation checks for ingestion validation.
• Given a scenario, choose a backfill strategy that avoids double-processing and preserves ordering.
• Explain why late-arriving data requires watermarking or windowing strategies.
• Recognize when to quarantine bad records vs fail the pipeline.

Topic 4: Processing and Transformation (Batch)

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4.1 Distributed processing concepts (Spark/Data Flow)

• Explain how distributed processing works at a high level (parallelism, partitions, shuffles).
• Given a scenario, choose a batch processing engine vs pushing transforms into the warehouse.
• Recognize common performance bottlenecks (wide shuffles, skew, small files).
• Identify best practices for writing efficient transformations (filter early, avoid unnecessary joins).

4.2 Job execution, scheduling, and environments

• Describe how jobs are packaged and executed (artifacts, dependencies, parameters).
• Given a scenario, choose concurrency limits and retries to protect downstream systems.
• Explain why dev/test/prod separation is important for data platforms.
• Recognize the role of configuration management and infrastructure-as-code for repeatable pipelines.

4.3 Monitoring and troubleshooting batch pipelines

• Identify the key metrics for batch pipelines: throughput, latency, failure rate, and cost per run.
• Given a scenario, diagnose common failures: permissions, schema drift, network timeouts, and out-of-memory.
• Explain why data lineage and run metadata accelerate root-cause analysis.
• Recognize when to optimize compute vs optimize data layout (partitioning, compaction).

Topic 5: Streaming and Change Data Capture (CDC)

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5.1 Streaming fundamentals (topics, partitions, consumers)

• Define core streaming concepts: topics, partitions, offsets, and consumer groups.
• Given a scenario, choose ordering guarantees and partitioning keys that support downstream analytics.
• Explain the difference between at-least-once and exactly-once processing at a conceptual level.
• Recognize common reliability patterns: dead-letter queues, replay, and idempotent sinks.

5.2 CDC and replication concepts (GoldenGate)

• Explain what CDC is and why it is used for near-real-time replication.
• Given a scenario, choose CDC over batch extracts to meet freshness requirements.
• Recognize common CDC considerations: schema changes, large transactions, and failover.
• Identify how to validate replicated data (lag metrics, reconciliation checks).

5.3 Event-driven architectures for data products

• Design an event-driven ingestion flow that triggers processing when new data arrives.
• Given a scenario, choose micro-batching vs true streaming based on downstream constraints.
• Explain why backpressure and rate limiting are necessary to keep consumers stable.
• Recognize when to store raw events to enable replay and new derived datasets.

Topic 6: Analytics and Consumption

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6.1 Warehousing and analytical query fundamentals

• Differentiate operational workloads (OLTP) from analytical workloads (OLAP).
• Given a scenario, choose a warehouse vs lake query approach based on performance and governance needs.
• Recognize why dimensional models support reporting and self-service analytics.
• Explain why data freshness and SLAs should be explicit for published datasets.

6.2 BI dashboards and KPIs (Analytics Cloud concepts)

• Describe how dashboards and reports consume curated datasets and semantic definitions.
• Given a scenario, choose appropriate visualization types for different metrics and distributions.
• Explain why consistent metric definitions prevent executive dashboard disagreement.
• Recognize common BI risks: stale data, unclear filters, and missing drill-down paths.

6.3 Sharing, access, and self-service analytics

• Define role-based access needs for analysts, data engineers, and business users.
• Given a scenario, choose dataset sharing controls that prevent overexposure of sensitive fields.
• Explain why data catalogs and dataset documentation improve adoption and reuse.
• Recognize when to publish certified datasets vs exploratory sandbox datasets.

Topic 7: Security, Governance, and Operations

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7.1 IAM and access control for data platforms

• Apply least-privilege IAM to ingestion, processing, storage, and analytics components.
• Given a scenario, choose compartment and policy structures that align with teams and environments.
• Recognize why separation of duties reduces risk in production data systems.
• Explain how tagging supports governance and cost allocation.

7.2 Encryption, auditing, and compliance fundamentals

• Explain encryption at rest and in transit and where to enforce each in a data pipeline.
• Given a scenario, choose key management and secret management controls for regulated data.
• Recognize the role of audit trails and logging for compliance investigations.
• Identify data classification and retention considerations for enterprise data platforms.

7.3 Observability and cost management

• Define platform health signals: pipeline success rate, lag, data freshness, and cost per dataset.
• Given a scenario, implement alerting that detects broken ingestion or stale dashboards early.
• Explain why capacity planning is required for peak loads and backfills.
• Recognize cost drivers (storage tiering, compute time, data scans) and how to control them.