Free PDF Microsoft - DP-750 - Implementing Data Engineering Solutions Using Azure Databricks–Efficient Latest Braindumps Ebook

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Microsoft DP-750 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare and process data	30-35%	- 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 transformation and modeling 1. Joins, aggregations, and normalization/denormalization 2. Delta Lake table design and SCD patterns 3. SQL and PySpark transformations - Data quality and validation 1. Schema enforcement and validation rules 2. Handling nulls, duplicates, and missing data 3. Pipeline expectations and data quality constraints
Topic 2: 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. Service principals and managed identities 3. Azure Key Vault integration
Topic 3: Deploy and manage data pipelines and workloads	30-35%	- Lakehouse architecture operations 1. Delta Lake optimization and clustering strategies 2. Delta Live Tables pipelines - Pipeline design and orchestration 1. Notebook-based vs declarative pipelines 2. Databricks Jobs and Workflows - Operational reliability 1. Monitoring and logging (Azure Monitor integration) 2. Error handling and retries
Topic 4: Secure and govern data using Unity Catalog	15-20%	- Access control and policies 1. Row-level and column-level security 2. Attribute-based access control (ABAC) 3. Tags and policy enforcement - Data governance fundamentals 1. Catalog, schema, and table management 2. Data lineage and auditing

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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q14-Q19):

NEW QUESTION # 14
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.dropna(subset=["order_amount"])
Does this meet the goal?

A. No
B. Yes

Answer: B

Explanation:
The correct answer is A - Yes.
df.dropna(subset=['order_amount']) is the idiomatic PySpark way to remove rows where a specific column contains a null. It inspects only the columns listed in subset and drops any row where those columns are null.
The resulting DataFrame contains only rows where order_amount is not null - exactly what the requirement asks for.
The subset parameter is important: without it, dropna() would drop rows where ANY column is null, which could incorrectly exclude rows that have nulls in other columns but a valid order_amount. By specifying subset=['order_amount'], the filter is applied precisely and only to the column in question.
This method is semantically equivalent to df.filter(df.order_amount.isNotNull()) and to the SQL clause WHERE order_amount IS NOT NULL. Both are correct - dropna with a subset is arguably the more readable Pythonic approach.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics

NEW QUESTION # 15
Which layer contains cleaned and conformed data in Databricks Lakehouse architecture?

A. Gold
B. Bronze
C. Silver
D. Raw

Answer: C

Explanation:
Silver layer contains cleaned, validated, and enriched data ready for analytics. Bronze stores raw ingested data. Gold contains aggregated business-level data. Raw is not part of formal medallion architecture naming.

NEW QUESTION # 16
You have an Azure Databricks workspace.
You need to ingest streaming data from Azure Event Hubs by using Apache Spark Structured Streaming The solution must authenticate to Event Hubs and read the event payload.
How should you complete the PySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Reading from Azure Event Hubs in Spark Structured Streaming requires three things:
An EventHubsConf object built with the Event Hubs connection string (eventhubs.connectionString). This object is then converted to a map with .toMap before being passed to Spark.
spark.readStream.format('eventhubs').options(**ehConf).load() to create the streaming DataFrame. The
'eventhubs' format is provided by the azure-eventhubs-spark connector library.
A cast('string') on the body column to decode the binary payload. Event Hubs delivers messages with the raw event bytes in a column called body - without the cast, you get binary data rather than the readable JSON or text payload.
This is the standard, documented integration pattern for connecting Azure Databricks to Event Hubs with Structured Streaming, providing the checkpoint-based exactly-once semantics required by the Contoso telemetry pipeline.
Reference: https://learn.microsoft.com/en-us/azure/databricks/connect/storage/events/eventhubs

NEW QUESTION # 17
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?

A. an Azure Data Factory pipeline that uses data flows
B. a Lakeflow Spark Declarative Pipelines (SDP) pipeline
C. a Databricks notebook triggered by a scheduled job
D. an Apache Spark Structured Streaming job

Answer: B

Explanation:
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/

NEW QUESTION # 18
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes records to a Delta table named Table1 by using a data quality rule named rule1.
You need to meet the following requirements:
- Records that violate rule1 must NOT be written to Table1, but the
pipeline must continue processing valid records.
- Data engineers must be able to review expectation metrics by using
minimal development effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 19
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