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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q290-Q295):

NEW QUESTION # 290
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace format("delta") with format("stream")
B. Replace predict with a stream-friendly prediction function
C. Replace spark.read with spark.readStream
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace "transactions" with the path to the location of the Delta table

Answer: C

Explanation:
1: To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. Reference: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 291
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 set up separate expectations for each table when developing their DLT pipeline.
B. They cannot determine which table is dropping the records.
C. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.
D. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
E. They can set up DLT to notify them via email when records are dropped.

Answer: C

Explanation:
One of the features of DLT is that it provides data quality metrics for each dataset in the pipeline, such as the number of records that pass or fail expectations, the number of records that are dropped, and the number of records that are written to the target. These metrics can be accessed from the DLT pipeline page, where the data engineer can click on each table and view the data quality statistics for the latest update or any previous update. This way, they can identify which table is dropping the records and why. Reference:
Monitor Delta Live Tables pipelines
Manage data quality with Delta Live Tables

NEW QUESTION # 292
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

A. When they are working interactively with a small amount of data
B. When they are working with SQL within Databricks SQL
C. When they are manually running reports with a large amount of data
D. When they are concerned about the ability to automatically scale with larger data
E. When they are running automated reports to be refreshed as quickly as possible

Answer: A

Explanation:
Explanation
A Single Node cluster is a cluster consisting of an Apache Spark driver and no Spark workers. A Single Node cluster supports Spark jobs and all Spark data sources, including Delta Lake. A Standard cluster requires a minimum of one Spark worker to run Spark jobs.

NEW QUESTION # 293
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
B. The COPY INTO statement requires the table to be refreshed to view the copied rows.
C. The previous day's file has already been copied into the table.
D. The PARQUET file format does not support COPY INTO.
E. The names of the files to be copied were not included with the FILES keyword.

Answer: C

Explanation:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. References: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS

NEW QUESTION # 294
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

A. Option D
B. Option C
C. Option B
D. Option A
E. Option E

Answer: E

Explanation:
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

NEW QUESTION # 295
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