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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory.
The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

A. Data Explorer
B. Databricks SQL
C. Auto Loader
D. Delta Lake
E. Unity Catalog

Answer: C

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage.
Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 25
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

A. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
B. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.
C. They cannot determine which table is dropping the records.
D. They can set up DLT to notify them via email when records are dropped.
E. They can set up separate expectations for each table when developing their DLT pipeline.

Answer: B

Explanation:
Explanation
To identify the table in a Delta Live Tables (DLT) pipeline where data is being dropped due to quality concerns, the data engineer can navigate to the DLT pipeline page, click on each table in the pipeline, and view the data quality statistics. These statistics often include information about records dropped, violations of expectations, and other data quality metrics. By examining the data quality statistics for each table in the pipeline, the data engineer can determine at which table the data is being dropped.

NEW QUESTION # 26
Which of the following describes the storage organization of a Delta table?

A. Delta tables are stored in a single file that contains only the data stored within the table.
B. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.
C. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.
D. Delta tables are stored in a collection of files that contain only the data stored within the table.
E. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.

Answer: E

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1. Reference:
What is Delta Lake? | Databricks on AWS
Quickstart - Delta Lake Documentation

NEW QUESTION # 27
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. None of these changes will need to be made
B. The pipeline will need to use a batch source in place of a streaming source
C. The pipeline will need to be written entirely in SQL
D. The pipeline will need to stop using the medallion-based multi-hop architecture
E. The pipeline will need to be written entirely in Python

Answer: A

Explanation:
Delta Live Tables is a declarative framework for building reliable, maintainable, and testable data processing pipelines. You define the transformations to perform on your data and Delta Live Tables manages task orchestration, cluster management, monitoring, data quality, and error handling. Delta Live Tables supports both SQL and Python as the languages for defining your datasets and expectations. Delta Live Tables also supports both streaming and batch sources, and can handle both append-only and upsert data patterns. Delta Live Tables follows the medallion lakehouse architecture, which consists of three layers of data: bronze, silver, and gold. Therefore, migrating to Delta Live Tables does not require any of the changes listed in the options B, C, D, or E. The data engineer and data analyst can use the same languages, sources, and architecture as before, and simply declare their datasets and expectations using Delta Live Tables syntax. References:
* What is Delta Live Tables?
* Transform data with Delta Live Tables
* What is the medallion lakehouse architecture?

NEW QUESTION # 28
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?

A. spark.delta.sql
B. spark.table
C. spark.delta.table
D. dbutils.sql
E. spark.sql

Answer: E

Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. References: Running SQL Queries Programmatically, Formatted string literals, spark.sql

NEW QUESTION # 29
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