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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 2: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Topic 4: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
Microsoft DP-750 덤프의 PDF 버전과 Software 버전의 내용은 동일합니다. PDF버전은 프린트 가능한 버전으로서 단독구매하셔도 됩니다. Software 버전은 테스트용으로 PDF 버전 공부를 마친후 시험전에 실력테스트 가능합니다. Software 버전은 PDF버전의 보조용이기에 단독 판매하지 않습니다. 소프트웨어버전까지 필요하신 분은 PDF버전을 구입하실때 공동구매하셔야 합니다.
질문 # 35
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales.
Sales stores transaction data and contains the following columns:
- transaction_id (string)
- transaction_date (date)
- amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
- amount must be greater than 0.
- transaction_id must never be null.
- Invalid records must be rejected when data is written to the Sales
table.
What should you do?
정답：C
설명：
To enforce these table-level data quality rules and automatically reject any invalid write operations in Azure Databricks, you must configure NOT NULL and CHECK constraints on the Delta table using standard SQL commands.
In Delta Lake, these constraints are enforced instantly on write. If an incoming record violates any of them, the entire transaction fails, and the invalid records are rejected.
1. Enforce transaction_id Cannot Be Null
Add a NOT NULL constraint to the transaction_id column. This blocks any write attempt containing a null identifier.
ALTER TABLE sales_data ALTER COLUMN transaction_id SET NOT NULL;
2. Enforce amount Must Be Greater Than 0
Add a CHECK constraint to the table to validate that the amount values strictly exceed zero.
ALTER TABLE sales_data ADD CONSTRAINT check_amount_positive CHECK (amount > 0); Reference:
https://docs.databricks.com/aws/en/tables/constraints
질문 # 36
What ensures failure recovery in Databricks Structured Streaming?
정답：B
설명：
Checkpointing stores streaming state and progress, allowing recovery after failures without data loss or duplication. Auto scaling adjusts compute resources but does not ensure reliability.
Partition pruning improves query performance. Broadcast joins optimize joins but are unrelated to recovery.
질문 # 37
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to recommend a pipeline that ingests files from cloud storage, performs cleansing and enrichment transformations, and writes curated Delta tables for analytics. The solution must minimize development effort and provide built-in monitoring and automatic retries.
What should you include in the recommendation?
정답：A
설명：
The best choice is a Lakeflow Spark Declarative Pipelines (SDP) pipeline.
Low Development Effort: Lakeflow SDP (formerly known as Delta Live Tables or DLT) is a completely declarative ETL framework. You simply define the target schemas and data transformations using standard SQL or Python. Databricks automatically manages the underlying operational complexities, state maintenance, task orchestration, and DAG dependencies for you.
Built-in Quality & Monitoring: It offers out-of-the-box data monitoring capabilities via Expectations, which allow you to specify data cleansing policies (like drop, retain, or fail on bad rows) with zero custom validation code. It also captures complete, automatic end-to-end data lineage and operational stats straight into Unity Catalog.
Built-in Resilience: Infrastructure failure handling and automatic retries are natively managed by the Lakeflow runtime.
Native Storage Ingestion: Using read_files() (Auto Loader) within SDP allows effortless, incremental ingestion of files from cloud object storage directly into curated Delta tables.
Reference:
https://docs.databricks.com/aws/en/ldp/
질문 # 38
You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
정답：C,E
설명：
The correct answers are B and E.
The goal is to reduce storage costs while preserving 30 days of time travel. Two actions are needed:
Set delta.deletedFileRetentionDuration to a value aligned with the 30-day requirement (Option B). Note: the answer option states '10 days' which would be insufficient for 30-day time travel - in practice this property should be set to at least 30 days. This property defines the retention floor: VACUUM will not delete any file newer than this threshold.
Run VACUUM on Table1 (Option E). VACUUM physically removes unreferenced data files older than the retention duration from storage. Without running VACUUM, obsolete files accumulate indefinitely regardless of the retention property setting - the property tells VACUUM what to keep; VACUUM is what does the actual cleanup.
Option C (OPTIMIZE) compacts small files for better query performance but never deletes anything. Option D (logRetentionDuration) keeps the transaction log for time travel but doesn't free up the data file storage.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum
질문 # 39
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Table1. Table1 stores customer data.
You need to implement a data retention solution that meets the following requirements:
- Deleted data must be retained for 30 days to support audits.
- Deleted data that is older than 30 days must be removed permanently.
- The solution must minimize administrative effort
Which two properties should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
정답：A,D
설명：
To configure an Azure Databricks managed Delta table to retain deleted data for 30 days and minimize administrative overhead, you must set the following table properties:
delta.logRetentionDuration: Set this to interval 30 days. This property controls how long the transaction log history is kept, which is essential for audit trails and time travel.delta.
deletedFileRetentionDuration: Set this to interval 30 days. This property determines the threshold for when deleted data files become eligible for permanent removal by the VACUUM command.
Reference:
https://docs.databricks.com/aws/en/delta/history
질문 # 40
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