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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Access control and permissions - Data encryption and masking |
| Topic 2: Cost & Performance Optimisation | 13% | - Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) |
| Topic 3: Data Modelling | 6% | - Schema design and management - Delta Lake table design - Medallion Architecture implementation |
| Topic 4: Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Topic 5: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 6: Data Governance | 7% | - Unity Catalog management - Policy enforcement - Data lineage and metadata tracking |
| Topic 7: Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors |
| Topic 8: Monitoring and Alerting | 10% | - Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging |
| Topic 9: Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic |
| Topic 10: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
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NEW QUESTION # 140
Which of the following results in the creation of an external table?
Answer: B
Explanation:
Explanation
Answer is CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION
'/mnt/delta/transactions'
Anytime a table is created using Location it is considered an external table, below is the current syntax.
Syntax
CREATE TABLE table_name ( column column_data_type...) USING format LOCATION "dbfs:/"
NEW QUESTION # 141
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:
After the database was successfully created and permissions configured, a member of the finance team runs the following code:
If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?
Answer: D
Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html
NEW QUESTION # 142
A table named user_ltv is being used to create a view that will be used by data analysis on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
An analyze who is not a member of the auditing group executing the following query:
Which result will be returned by this query?
Answer: A
Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.
NEW QUESTION # 143
Where are Interactive notebook results stored in Databricks product architecture?
Answer: E
Explanation:
Explanation
The answer is Data and Control plane,
Only Job results are stored in Data Plane(your storage), Interactive notebook results are stored in a combination of the control plane (partial results for presentation in the UI) and customer storage.
https://docs.microsoft.com/en-us/azure/databricks/getting-started/overview#--high-level-architecture Snippet from the above documentation, Graphical user interface, application Description automatically generated
How to change this behavior?
You can change this behavior using Workspace/Admin Console settings for that workspace, once enabled all of the interactive results are stored in the customer account(data plane) except the new notebook visualization feature Databricks has recently introduced, this still stores some metadata in the control pane irrespective of the below settings. please refer to the documentation for more details.
Graphical user interface, text, application, email Description automatically generated
Why is this important to know?
I recently worked on a project where we had to deal with sensitive information of customers and we had a security requirement that all of the data need to be stored in the data plane including notebook results.
NEW QUESTION # 144
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 thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summary table?
Answer: E
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 # 145
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