更新的最新DP-750考證|高通過率的考試材料|全面覆蓋的DP-750：Implementing Data Engineering Solutions Using Azure Databricks

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Deploy and manage data pipelines and workloads	30-35%	- Pipeline design and orchestration 1. Notebook-based vs declarative pipelines 2. Databricks Jobs and Workflows - Operational reliability 1. Error handling and retries 2. Monitoring and logging (Azure Monitor integration) - Lakehouse architecture operations 1. Delta Live Tables pipelines 2. Delta Lake optimization and clustering strategies
Secure and govern data using Unity Catalog	15-20%	- Data governance fundamentals 1. Data lineage and auditing 2. Catalog, schema, and table management - Access control and policies 1. Row-level and column-level security 2. Tags and policy enforcement 3. Attribute-based access control (ABAC)
Configure and manage Azure Databricks environments	15-20%	- Workspace and compute configuration 1. Cluster types and configuration (job, all-purpose, serverless) 2. Runtime, Spark, and Photon configuration 3. Autoscaling, termination, and performance tuning - Security and authentication setup 1. Access control for compute resources 2. Azure Key Vault integration 3. Service principals and managed identities
Prepare and process data	30-35%	- Data transformation and modeling 1. SQL and PySpark transformations 2. Joins, aggregations, and normalization/denormalization 3. Delta Lake table design and SCD patterns - Data ingestion 1. Batch ingestion using COPY INTO and CTAS 2. Auto Loader and CDC ingestion patterns 3. Streaming ingestion using Spark Structured Streaming - Data quality and validation 1. Handling nulls, duplicates, and missing data 2. Schema enforcement and validation rules 3. Pipeline expectations and data quality constraints

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最新的 Microsoft Certified: Fabric Data Engineer Associate DP-750 免費考試真題 (Q63-Q68):

問題 #63
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to create a job in Lakeflow Jobs named Job1 that:
* Ingests data from cloud storage
* Runs two independent transformation tasks
The transformation tasks must run only after the ingestion completes and must run in parallel.
You need to design the task logic for Job1.
What should you configure?

A. One ingestion task with two parallel downstream transformation tasks
B. independent tasks with no defined dependencies
C. two ingestion tasks, each followed by a transformation task
D. a single task that performs ingestion and transformations sequentially

答案：A

解題說明：
Job1 should contain one ingestion task that acts as the common upstream dependency for two separate transformation tasks. Once ingestion succeeds, Lakeflow Jobs can start both downstream tasks concurrently because neither transformation depends on the other. This design represents the actual workflow, avoids duplicated ingestion, and reduces total execution time through parallelism. Creating two ingestion tasks would repeat the same source processing and could introduce inconsistent results or unnecessary costs. A single sequential task would prevent parallel transformation and make failures harder to isolate and retry. Defining three independent tasks without dependencies could allow transformations to start before ingestion has completed. An explicit directed task graph therefore provides the required execution order while preserving parallelism for independent downstream processing.

問題 #64
What improves join performance for small lookup tables?

A. Cartesian join
B. Sort merge join
C. Shuffle join
D. Broadcast join

答案：D

解題說明：
Broadcast joins send the small table to all worker nodes, avoiding expensive shuffling. This significantly improves performance. Shuffle and sort merge joins are heavier. Cartesian joins are inefficient and generally avoided.

問題 #65
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:
* transactionjd (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?

A. Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.
B. Add a not null constraint to transactionjd and a check constraint to amount.
C. Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
D. Use a select statement with where conditions to validate the data before querying.

答案：B

解題說明：
The correct answer is D - a NOT NULL constraint on transaction_id and a CHECK constraint on amount.
Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount
> 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.
Options A and C (SELECT with WHERE / views) are read-time constructs - they don't prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely.
Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-constraints

問題 #66
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT(") > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT(') > 1;
What occurs when you run the query?

A. Values appear in both tables more than once.
B. Values appear in Table2 but NOT Table1.
C. Values appear in either table more than once.
D. Values appear in Table more than once.

答案：A

解題說明：
The correct answer is B - values appear in both tables more than once.
Reading the query from the inside out: each subquery identifies values that appear more than once within their own table. The first subquery returns Column1 values that are duplicated in Table1. The second returns Column2 values that are duplicated in Table2. INTERSECT then returns only the values that appear in both result sets - meaning values that are duplicated in Table1 AND also duplicated in Table2.
Option A describes a UNION result (values in either table), not INTERSECT. Option C only considers duplicates in Table1, ignoring the Table2 condition - that would be the first subquery in isolation. Option D describes values exclusive to Table2 (EXCEPT or MINUS), the opposite of INTERSECT's requirement that values appear in both sets.
INTERSECT always requires membership in both operands - that's its fundamental definition in standard SQL.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-qry-select- setops

問題 #67
You need to develop the task logic for a new job in Lakeflow Jobs that processes telemetry data.
Each task must contain only the appropriate logic for its step in the pipeline. The solution must support the planned changes and meet the data ingestion and processing requirements.
What should you do?

A. Create three tasks that each contains the identical logic and use task retries.
B. Use a single Databricks notebook task that performs ingestion, cleansing, and curation in one script.
C. Create separate tasks for ingestion, cleansing, and curation.
D. Use a single SQL task that performs ingestion, cleansing, and curation by running merge commands.

答案：C

解題說明：
The correct answer is D. Breaking the pipeline into separate tasks for ingestion, cleansing, and curation is the foundation of well-designed Lakeflow Jobs pipelines. Each task should own one responsibility - when a task does too much, debugging a failure becomes a hunt through unrelated code, and retry logic becomes expensive because you re-execute work that already succeeded.
Contoso ' s planned changes explicitly call for ' a clear execution order and dependencies ' and ' orchestrate multi-step ingestion and transformation workflows. ' Separate tasks map directly to those goals: Lakeflow Jobs tracks each task ' s status independently, so if cleansing fails, ingestion doesn ' t re-run.
Option A bundles everything into one notebook, which means a curation bug forces a full re-ingestion. Option B copies logic three times - any future change must be applied in triplicate, which is a maintenance hazard.
Option C forces everything through SQL MERGE, which is the wrong tool for raw-event ingestion and doesn
' t address cleansing or schema drift.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/

問題 #68
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