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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Workspace navigation and management 3. Compute options: clusters, SQL warehouses, serverless
Topic 2: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Connecting to external data sources 2. Batch and streaming ingestion 3. Delta Lake basics and usage - Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL
Topic 3: Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. Complex data processing logic 2. DataFrame operations and transformations 3. Aggregations, joins, and window functions
Topic 4: Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Permissions and access control 2. Data governance model
Topic 5: Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management - Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
In which of the following file formats is data from Delta Lake tables primarily stored?

A. Parquet
B. A proprietary, optimized format specific to Databricks
C. Delta
D. CSV
E. JSON

Answer: A

Explanation:
Explanation
https://docs.delta.io/latest/delta-faq.html

NEW QUESTION # 138
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?

A. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
B. They can schedule the query to run every 1 day from the Jobs UI.
C. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
D. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
E. They can schedule the query to run every 12 hours from the Jobs UI.

Answer: D

Explanation:
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone.
This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
* In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
* Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
* Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
* Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. References: Schedule a query, What are Databricks SQL alerts?, Jobs.

NEW QUESTION # 139
A data engineer streams customer orders into a Kafka topic (orders_topic) and is currently writing the ingestion script of a DLT pipeline. The data engineer needs to ingest the data from Kafka brokers to DLT using Databricks What is the correct code for ingesting the data?

Answer: D

NEW QUESTION # 140
Which of the following data workloads will utilize a Gold table as its source?

A. A job that enriches data by parsing its timestamps into a human-readable format
B. A job that aggregates uncleaned data to create standard summary statistics
C. A job that queries aggregated data designed to feed into a dashboard
D. A job that ingests raw data from a streaming source into the Lakehouse
E. A job that cleans data by removing malformatted records

Answer: C

Explanation:
1: A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. Reference: Databricks Documentation - What is the medallion lakehouse architecture?, Databricks Documentation - What is a Medallion Architecture?, K21Academy - Delta Lake Architecture & Azure Databricks Workspace.

NEW QUESTION # 141
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to manipulate the same data using a variety of languages
B. The ability to distribute complex data operations
C. The ability to collaborate in real time on a single notebook
D. The ability to support batch and streaming workloads
E. The ability to set up alerts for query failures

Answer: D

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse.
Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. References: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS] Learn more
1blob:https://www.bing.com/a746b4b4-48d0-4f44-9736-44d1ce0c4228learn.microsoft.com2blob:https://www.bing.com/525fbb0f-e02f-4a70-8085-22c065fe0ca0 medium.com3blob:https://www.bing.com/5cb5bd07-1008-4cf7-9fa3-42a5a689c7d5 slideshare.net4blob:https://www.bing.com/9a7e8352-30c1-4356-a73f-a7253b607ef7 docs.databricks.com5blob:https://www.bing.com/3f65cc27-d573-4810-b272-01238a431c03 github.com6blob:https://www.bing.com/334f6880-dfeb-4e61-bd9a-76efae0a2d01 key2consulting.com

NEW QUESTION # 142
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