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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Topic 2: Data Governance | 7% | - Unity Catalog management - Policy enforcement - Data lineage and metadata tracking |
| Topic 3: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Topic 4: Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices |
| Topic 5: Data Modelling | 6% | - Medallion Architecture implementation - Delta Lake table design - Schema design and management |
| Topic 6: Ensuring Data Security and Compliance | 10% | - Access control and permissions - Data encryption and masking - Compliance standards implementation |
| Topic 7: Monitoring and Alerting | 10% | - Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging |
| Topic 8: Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools |
| Topic 9: Cost & Performance Optimisation | 13% | - Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) |
| Topic 10: Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks |
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NEW QUESTION # 11
Which of the following command can be used to drop a managed delta table and the underlying files in the storage?
Answer: E
Explanation:
Explanation
The answer is DROP TABLE table_name,
When a managed table is dropped, the table definition is dropped from metastore and everything including data, metadata, and history are also dropped from storage.
NEW QUESTION # 12
A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing.
Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?
Answer: C
Explanation:
The correct method to define a table using Lakeflow Declarative Pipelines (LDP) is with the @dlt.table decorator, which persists the output as a managed Delta table. When ingesting raw JSON data, spark.read.
json() or spark.read.format( " json " ).load() is the standard approach. This reads JSON-formatted files from the source and stores them in Delta format automatically managed by Databricks.
Reference Source: Databricks Lakeflow Declarative Pipelines Developer Guide - "Create tables from raw JSON and Delta sources."
NEW QUESTION # 13
A data engineer has created a transactions Delta table on Databricks that should be used by the analytics team. The analytics team wants to use the table with another tool that requires Apache Iceberg format.
What should the data engineer do?
Answer: C
Explanation:
Delta Lake introduced Delta Universal Format (Delta UniForm), which allows seamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can be converted into an Iceberg table while maintaining Delta capabilities.
Explanation of Each Option:
(A) Require the analytics team to use a tool that supports Delta table
Incorrect: While Delta Lake is widely used, requiring the team to change tools is not a flexible or scalable solution.
(B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table Incorrect:
The uniform feature must be enabled after conversion.
You cannot directly enable uniform without first converting the table.
(C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team Incorrect:
Creating a separate Iceberg copy would duplicate storage and increase maintenance complexity.
This is not necessary when Delta UniForm allows direct compatibility with Iceberg.
(D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table Correct:
The best approach is to convert the existing Delta table to Iceberg using the Databricks Delta to Iceberg migration tools.
After conversion, enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.
Conclusion:
The best practice for interoperability between Delta and Iceberg is to convert the Delta table to Iceberg and enable uniform, ensuring cross-compatibility without data duplication.
Thus, Option (D) is the correct answer.
Reference:
Delta UniForm for Apache Iceberg - Databricks Documentation
Convert Delta to Iceberg - Databricks
NEW QUESTION # 14
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:
Which limitation will the team face while diagnosing this problem?
Answer: D
Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.
References:
* Databricks documentation on Delta Lake schema management: https://docs.databricks.com/delta/delta- batch.html#schema-management
NEW QUESTION # 15
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:
The table daily_store_sales contains all the information needed to update store_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?
Answer: C
Explanation:
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.
NEW QUESTION # 16
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