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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Data Modelling | 6% | - Medallion Architecture implementation - Schema design and management - Delta Lake table design |
| Topic 3: Data Governance | 7% | - Policy enforcement - Unity Catalog management - Data lineage and metadata tracking |
| Topic 4: Monitoring and Alerting | 10% | - Pipeline observability and logging - Performance and health monitoring - Setting up alerts and notifications |
| Topic 5: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Topic 6: Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Topic 7: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching |
| Topic 8: Debugging and Deploying | 10% | - CI/CD and DevOps practices - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs |
| Topic 9: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Topic 10: Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks |
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NEW QUESTION # 87
An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
* Personally Identifiable Information (PII) such as email, phone number, and IP address must be masked or anonymized before storage.
* Both batch and streaming pipelines must apply consistent PII handling.
* Masking logic must be auditable and reproducible.
* The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?
Answer: C
Explanation:
Databricks recommends applying data masking or anonymization before persisting PII to ensure compliance with privacy regulations such as GDPR and HIPAA. In a Lakeflow Declarative Pipeline , developers can define custom Python or SQL-based masking functions to standardize PII handling across both batch and streaming inputs.
This approach ensures that data entering the Delta Lake is already anonymized, guaranteeing consistent and auditable behavior. By applying masking during ingestion (in the Bronze layer), audit trails are preserved through pipeline event logs.
While Unity Catalog column masks (option C) can enforce dynamic masking at query time, they do not prevent PII storage. Thus, option D aligns with the best practice of securing PII before storage , while still supporting reproducibility and analytics usability.
NEW QUESTION # 88
While investigating a data issue in a Delta table, you wanted to review logs to see when and who updated the table, what is the best way to review this data?
Answer: D
Explanation:
Explanation
The answer is Run SQL command DESCRIBE HISTORY table_name.
here is the sample data of how DESCRIBE HISTORY table_name looks
* +-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------
* |version| timestamp|userId|userName|operation| operationParameters|
job|notebook|clusterId|readVersion|isolationLevel|isBlindAppend| operationMetrics|
* +-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------
* | 5|2019-07-29 14:07:47| null| null| DELETE|[predicate -> ["(...|null| null| null| 4| Serializable| false|[numTotalRows -> ...|
* | 4|2019-07-29 14:07:41| null| null| UPDATE|[predicate -> (id...|null| null| null| 3| Serializable| false|[numTotalRows -> ...|
* | 3|2019-07-29 14:07:29| null| null| DELETE|[predicate -> ["(...|null| null| null| 2| Serializable| false|[numTotalRows -> ...|
* | 2|2019-07-29 14:06:56| null| null| UPDATE|[predicate -> (id...|null| null| null| 1| Serializable| false|[numTotalRows -> ...|
* | 1|2019-07-29 14:04:31| null| null| DELETE|[predicate -> ["(...|null| null| null| 0| Serializable| false|[numTotalRows -> ...|
* | 0|2019-07-29 14:01:40| null| null| WRITE|[mode -> ErrorIfE...|null| null| null| null| Serializable| true|[numFiles -> 2, n...|
+-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------------+
NEW QUESTION # 89
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?
Answer: B
Explanation:
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.
NEW QUESTION # 90
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
Answer: C
Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.References:
Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).
NEW QUESTION # 91
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.
Which command should be removed from the notebook before scheduling it as a job?
Answer: B
Explanation:
Cmd 6 is the command that should be removed from the notebook before scheduling it as a job. This command is selecting all the columns from the finalDF dataframe and displaying them in the notebook. This is not necessary for the job, as the finalDF dataframe is already written to a table in Cmd 7. Displaying the dataframe in the notebook will only consume resources and time, and it will not affect the output of the job. Therefore, Cmd 6 is redundant and should be removed.
The other commands are essential for the job, as they perform the following tasks:
Cmd 1: Reads the raw_data table into a Spark dataframe called rawDF.
Cmd 2: Prints the schema of the rawDF dataframe, which is useful for debugging and understanding the data structure.
Cmd 3: Selects all the columns from the rawDF dataframe, as well as the nested columns from the values struct column, and creates a new dataframe called flattenedDF.
Cmd 4: Drops the values column from the flattenedDF dataframe, as it is no longer needed after flattening, and creates a new dataframe called finalDF.
Cmd 5: Explains the physical plan of the finalDF dataframe, which is useful for optimizing and tuning the performance of the job.
Cmd 7: Writes the finalDF dataframe to a table called flat_data, using the append mode to add new data to the existing table.
NEW QUESTION # 92
......
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