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| Section | Weight | Objectives |
|---|---|---|
| Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Monitoring and Alerting | 10% | - Setting up alerts and notifications - Pipeline observability and logging - Performance and health monitoring |
| Data Modelling | 6% | - Medallion Architecture implementation - Schema design and management - Delta Lake table design |
| Cost & Performance Optimisation | 13% | - Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) |
| Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Data transformation and aggregation - Batch and incremental processing logic |
| Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks |
| Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Auto Loader and streaming ingestion - Connecting to diverse data sources |
| Debugging and Deploying | 10% | - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Data encryption and masking - Compliance standards implementation |
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NEW QUESTION # 80
How do you access or use tables in the unity catalog?
Answer: A
Explanation:
Explanation
The answer is catalog_name.schema_name.table_name
Graphical user interface, diagram Description automatically generated
Note: Database and Schema are analogous they are interchangeably used in the Unity catalog.
FYI, A catalog is registered under a metastore, by default every workspace has a default metastore called hive_metastore, with a unity catalog you have the ability to create meatstores and share that across multiple workspaces.
Diagram Description automatically generated
NEW QUESTION # 81
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?
Answer: E
Explanation:
Explanation
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under
"Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.
NEW QUESTION # 82
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding
30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?
Answer: D
Explanation:
The scenario presented involves inconsistent microbatch processing times in a Structured Streaming job during peak hours, with the need to ensure that records are processed within 10 seconds. The trigger once option is the most suitable adjustment to address these challenges:
* Understanding Triggering Options:
* Fixed Interval Triggering (Current Setup):The current trigger interval of 10 seconds may contribute to the inconsistency during peak times as it doesn't adapt based on the processing time of the microbatches. If abatch takes longer to process, subsequent batches will start piling up, exacerbating the delays.
* Trigger Once:This option allows the job to run a single microbatch for processing all available data and then stop. It is useful in scenarios where batch sizes are unpredictable and can vary significantly, which seems to be the case during peak hours in this scenario.
* Implementation of Trigger Once:
* Setup:Instead of continuously running, the job can be scheduled to run every 10 seconds using a Databricks job. This scheduling effectively acts as a custom trigger interval, ensuring that each execution cycle handles all available data up to that point without overlapping or queuing up additional executions.
* Advantages:This approach allows for each batch to complete processing all available data before the next batch starts, ensuring consistency in handling data surges and preventing the system from being overwhelmed.
* Rationale Against Other Options:
* Option A and E (Decrease Interval):Decreasing the trigger interval to 5 seconds might exacerbate the problem by increasing the frequency of batch starts without ensuring the completion of previous batches, potentially leading to higher overhead and less efficient processing.
* Option B (Increase Interval):Increasing the trigger interval to 30 seconds could lead to latency issues, as the data would be processed less frequently, which contradicts the requirement of processing records in less than 10 seconds.
* Option C (Modify Partitions):While increasing parallelism through more shuffle partitions can improve performance, it does not address the fundamental issue of batch scheduling and could still lead to inconsistency during peak loads.
* Conclusion:
* By using the trigger once option and scheduling the job every 10 seconds, you ensure that each microbatch has sufficient time to process all available data thoroughly before the next cycle begins, aligning with the need to handle peak loads more predictably and efficiently.
References
* Structured Streaming Programming Guide - Triggering
* Databricks Jobs Scheduling
NEW QUESTION # 83
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: A
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 # 84
The data engineering team is looking to add a new column to the table, but the QA team would like to test the change before implementing in production, which of the below options allow you to quickly copy the table from Prod to the QA environment, modify and run the tests?
Answer: B
Explanation:
Explanation
The answer is SHALLOW CLONE
SHALLOW CLONE If you wish to create a copy of a table quickly to test out applying changes without the risk of modifying the current table, SHALLOW CLONE can be a good option. Shallow clones just copy the Delta transaction logs, meaning that the data doesn't move so it can be very quick.
1.CREATE OR REPLACE TABLE {new_table_name} SHALLOW CLONE
{source_table_name}|[LOCATION path]
DEEP CLONE fully copies data and metadata from a source table to a target. This copy occurs incre-mentally, so executing this command again can sync changes from the source to the target location. It copies all of the data and transaction logs this can take a long time based on the size of the table.
1.CREATE OR REPLACE TABLE {new_table_name} DEEP CLONE {source_table_name}|[LOCATION path]
NEW QUESTION # 85
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