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| Section | Weight | Objectives |
|---|---|---|
| Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts |
| Apache Spark Data Processing Fundamentals | 20-25% | - Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Create and use Spark DataFrames |
| Spark SQL and DataFrames | 15-20% | - Join and union DataFrames - Aggregate and group data - Handle null values and data quality - Write and execute Spark SQL queries |
| Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs) |
| Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Write to and read from Delta tables - Understand ACID transactions and time travel - Create and manage Delta tables |
| Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns - Implement incremental data processing - Design data pipelines for batch and streaming |
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問題 #61
A data engineer uses COPY INTO to load CSV files from an external location into a Delta table.
The job is accidentally triggered twice within the same hour.
What is the expected outcome?
答案：A
解題說明：
COPY INTO maintains state about which source files have already been ingested, so re-running it is idempotent and previously loaded files are skipped. Files are only reprocessed if FORCE = true (or copy_options ('force' = 'true')) is specified.
問題 #62
A data engineer is setting up a new Databricks pipeline that ingests clickstream events from Kafka and daily product catalogs from cloud object storage. To ensure auditability and easy reprocessing, the engineer wants to land all source data first. Later stages will handle cleaning, deduplication, and business modeling before the data is used in dashboards.
Which approach aligns with Medallion Architecture principles?
答案：C
解題說明：
Databricks describes the Bronze layer in the Medallion Architecture as the raw ingestion layer , where source data is landed with minimal validation or transformation so that the original records are preserved for auditability and reprocessing. This is especially important for sources like Kafka clickstream events and file-based product catalogs, because downstream logic such as cleansing, deduplication, conformance, and business modeling should happen later in Silver and Gold layers. Bronze data is commonly stored in an append-only pattern and may include basic ingestion metadata, but it should not be heavily transformed at this stage. Databricks explicitly recommends limiting cleanup and validation in Bronze and using Silver for validation and deduplication. That makes option C the best match to Databricks guidance. Options A, B, and D all skip or misuse the Bronze layer, which reduces traceability and makes recovery or replay harder if business logic changes later.
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問題 #63
A Python file is ready to go into production and the client wants to use the cheapest but most efficient type of cluster possible. The workload is quite small, only processing 10GBs of data with only simple joins and no complex aggregations or wide transformations. Which cluster meets the requirement?
答案：C
解題說明：
A job cluster with spot instances enabled is the cheapest and efficient option for small, production workloads. Spot instances reduce costs, and a job cluster ensures the cluster only runs during the workload execution.
問題 #64
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
答案：A
解題說明：
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.
問題 #65
A data engineer uploads a CSV file using the "Create or modify a table using file upload" option in Databricks. To avoid incorrect schema inference, they disable Automatically detect column types before creating a Unity Catalog-managed table. What is the outcome?
答案：D
解題說明：
Disabling Automatically detect column types causes Databricks to create every uploaded CSV column as STRING, avoiding automatic numeric, date, or Boolean inference.
問題 #66
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