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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Alerting | 10% | - Performance and health monitoring - Setting up alerts and notifications - Pipeline observability and logging |
| Topic 2: Data Ingestion & Acquisition | 7% | - Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution |
| Topic 3: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching |
| Topic 4: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Topic 5: Data Modelling | 6% | - Schema design and management - Delta Lake table design - Medallion Architecture implementation |
| Topic 6: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Access control and permissions - Data encryption and masking |
| Topic 7: Data Governance | 7% | - Data lineage and metadata tracking - Policy enforcement - Unity Catalog management |
| Topic 8: Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools |
| Topic 9: Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Topic 10: Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices |
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NEW QUESTION # 24
A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve.
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
According to Databricks Delta Lake optimization documentation, Liquid Clustering is a next-generation file organization capability that automatically manages file co-location without requiring explicit partitioning or manual Z-ORDERing. When combined with Predictive Optimization, Databricks automatically maintains clustering across frequently filtered or queried columns, adapting dynamically as query workloads evolve.
This approach eliminates the need for manual maintenance (such as periodic OPTIMIZE or Z-ORDER commands) while improving query performance on large tables-particularly for high-ingest streaming workloads.
Delta caching (B) only improves performance for cached queries and does not address file layout issues, and (D) handles file size optimization but not clustering. Thus, C is the most efficient, modern, and low-maintenance solution recommended by Databricks.
NEW QUESTION # 25
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 namedstore_saies_summaryand the schema is as follows:
The tabledaily_store_salescontains all the information needed to updatestore_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Ifdaily_store_salesis implemented as a Type 1 table and the column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summarytable?
Answer: E
Explanation:
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 References: [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 # 26
Create a sales database using the DBFS location 'dbfs:/mnt/delta/databases/sales.db/'
Answer: D
Explanation:
Explanation
The answer is
CREATE DATABASE sales LOCATION 'dbfs:/mnt/delta/databases/sales.db/'
Note: with the introduction of the Unity catalog and three-layer namespace usage of SCHEMA and DATABASE is interchangeable
NEW QUESTION # 27
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?
Answer: A
Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
Reference:
Databricks documentation on jobs and job clusters: https://docs.databricks.com/jobs.html
NEW QUESTION # 28
You would like to build a spark streaming process to read from a Kafka queue and write to a Delta table every
15 minutes, what is the correct trigger option
Answer: B
Explanation:
Explanation
The answer is trigger(processingTime = "15 Minutes")
Triggers:
*Unspecified
This is the default. This is equivalent to using processingTime="500ms"
*Fixed interval micro-batches .trigger(processingTime="2 minutes")
The query will be executed in micro-batches and kicked off at the user-specified intervals
*One-time micro-batch .trigger(once=True)
The query will execute a single micro-batch to process all the available data and then stop on its own
*One-time micro-batch.trigger .trigger(availableNow=True) -- New feature a better version of (once=True) Databricks supports trigger(availableNow=True) in Databricks Runtime 10.2 and above for Delta Lake and Auto Loader sources. This functionality combines the batch processing approach of trigger once with the ability to configure batch size, resulting in multiple parallelized batches that give greater control for right-sizing batches and the resultant files.
NEW QUESTION # 29
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