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

Section	Weight	Objectives
Topic 1: Data Pipeline Architecture	15-20%	- Implement incremental data processing - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance - Understand ELT vs ETL patterns
Topic 2: Delta Lake Fundamentals	20-25%	- Create and manage Delta tables - Understand ACID transactions and time travel - Write to and read from Delta tables - Explain Delta Lake features and benefits
Topic 3: Apache Spark Data Processing Fundamentals	20-25%	- Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Use Spark SQL for data processing
Topic 4: Python for Data Engineering	10-15%	- Use PySpark for data processing - Implement user-defined functions (UDFs) - Work with Spark APIs in Python
Topic 5: Lakehouse Platform Concepts	10-15%	- Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts
Topic 6: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Join and union DataFrames - Write and execute Spark SQL queries - Aggregate and group data

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q195-Q200):

NEW QUESTION # 195
An analyst reports that a dashboard showed incorrect numbers before a data correction was applied at 09:00 today. The engineer needs to query the table exactly as it existed at version 118.
Which statement returns that data?

A. SELECT * FROM sales VERSION AS OF 118
B. SELECT * FROM sales WHERE _commit_version = 118
C. DESCRIBE HISTORY sales LIMIT 118
D. RESTORE TABLE sales TO VERSION AS OF 118

Answer: A

Explanation:
Delta time travel supports VERSION AS OF <n> (or TIMESTAMP AS OF) to read a prior snapshot without modifying the table. RESTORE would actually roll the table back, which changes the current state rather than simply inspecting history.

NEW QUESTION # 196
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

A. Delta Live Tables
B. Delta Lake
C. Data Explorer
D. Auto Loader
E. Unity Catalog

Answer: A

Explanation:
Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. Reference: Delta Live Tables Overview, Data Quality Monitoring

NEW QUESTION # 197
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

A. They can turn on the Auto Stop feature for the SQL endpoint.
B. They can turn on the Serverless feature for the SQL endpoint.
C. They can increase the maximum bound of the SQL endpoint's scaling range
D. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
E. They can increase the cluster size of the SQL endpoint.

Answer: B

Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non- running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL

NEW QUESTION # 198
A Databricks workflow fails at the last stage due to an error in a notebook. This workflow runs daily. The data engineer fixes the mistake and wants to rerun the pipeline. This workflow is very costly and time-intensive to run. Which action should the data engineer do in order to minimise downtime and cost?

A. Switch to another cluster
B. Restart the cluster
C. Re-run the entire workflow
D. Repair run

Answer: D

Explanation:
Repair run in Databricks allows rerunning only the failed tasks in a workflow instead of re-running the entire pipeline, minimizing downtime and reducing costs.

NEW QUESTION # 199
A data engineer has two DataFrames: orders_df (all orders) and customers_df (only active customers). The data engineer needs to filter out orders placed by inactive customers. Which operation should the data engineer use?

A. orders_df and customers_df inner join
B. orders_df and customers_df cross join
C. customers_df left join with orders_df
D. orders_df left join with customers_df

Answer: A

Explanation:
An inner join keeps only orders whose customer key matches a row in customers_df. Since customers_df contains only active customers, orders from inactive customers are excluded.

NEW QUESTION # 200
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