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

Section	Weight	Objectives
Topic 1: Developing Code for Data Processing using Python and SQL	22%	- Implement complex data processing logic - Write efficient and maintainable code - Use Databricks-specific libraries and APIs
Topic 2: Data Governance	7%	- Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards
Topic 3: Cost & Performance Optimisation	13%	- Improve query and pipeline performance - Apply cost management best practices - Optimize compute and storage resources
Topic 4: Data Ingestion & Acquisition	7%	- Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources
Topic 5: Monitoring and Alerting	10%	- Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications
Topic 6: Ensuring Data Security and Compliance	10%	- Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions
Topic 7: Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation
Topic 8: Data Modelling	6%	- Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture
Topic 9: Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management
Topic 10: Debugging and Deploying	10%	- Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
B. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
C. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
D. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

Answer: A

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated.

NEW QUESTION # 135
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema. Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Answer: C

Explanation:
To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.

NEW QUESTION # 136
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
E. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Answer: E

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 137
A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A. Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
B. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
C. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
D. Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.

Answer: B

Explanation:
Databricks recommends Liquid Clustering for optimizing data layout in large Delta tables where query filters involve high-cardinality columns. Liquid Clustering automatically manages file organization and supports incremental maintenance without the need to rewrite data when clustering keys evolve. This is a key advantage over static partitioning or Z-ordering, which require costly file rewrites whenever optimization keys change. By combining Liquid Clustering with a periodic OPTIMIZE command, Databricks automatically compacts small files and maintains efficient data skipping performance. As stated in the Delta Lake optimization guide, Liquid Clustering is designed for scalability, minimal maintenance, and adaptability for analytical workloads with evolving query patterns--making B the correct answer.

NEW QUESTION # 138
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

A. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
B. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
C. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
D. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster

Answer: D

Explanation:
The correct Databricks CLI command to create a new cluster is databricks clusters create. You specify the runtime with --spark-version (here '14.3.x-scala2.12'), the number of workers with -- num-workers, the node type with --node-type-id, and the cluster name with --cluster-name. This command properly initializes the cluster with the desired configuration.

NEW QUESTION # 139
......

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