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

SectionWeightObjectives
Topic 1: Data Transformation, Cleansing, and Quality~12%- Enforce data quality and quarantine bad data
- Apply advanced Spark transformations
Topic 2: Data Sharing and Federation~8%- Configure Delta Sharing and Lakehouse Federation
Topic 3: Data Modeling~10%- Apply dimensional modeling techniques
- Design scalable Delta Lake schemas and clustering
Topic 4: CI/CD, Testing, and Deployment~6%- Deploy with Declarative Automation Bundles, CLI, and REST API
- Implement testing and deployment pipelines
Topic 5: Cost and Performance Optimization~13%- Optimize queries, clusters, and storage
- Leverage system tables and observability tools
Topic 6: Security and Governance~10%- Manage Unity Catalog permissions and ACLs
- Implement row-level security, column masking, and compliance
Topic 7: Streaming Workloads and Change Data Capture~11%- Implement reliable streaming pipelines
- Apply AUTO CDC APIs and exactly-once semantics
Topic 8: Monitoring, Logging, and Troubleshooting~8%- Use Spark UI, Query Profiler, and system tables
- Diagnose common pipeline and job failures
Topic 9: Developing Code for Data Processing using Python and SQL~22%- Implement scalable Python/SQL code and project structures
- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
- Manage dependencies, libraries, and UDFs

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
B. The activity details table already exists; CHECK constraints can only be added during initial table creation.
C. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
D. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
E. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.


Question 2

A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Executor's log file
B. Cluster Event Log
C. Driver's log file
D. Ganglia
E. Workspace audit logs


Question 3

The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A. Size on Disk is < Size in Memory
B. Size on Disk is> 0
C. On Heap Memory Usage is within 75% of off Heap Memory usage
D. The RDD Block Name included the '' annotation signaling failure to cache
E. The number of Cached Partitions> the number of Spark Partitions


Question 4

A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
B. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
C. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
D. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.


Question 5

A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)

A. Choose a compute instance with more network bandwidth.
B. Choose a compute instance with more disk space.
C. Reduce spark.sql.files.maxPartitionBytes.
D. Increase spark.sql.files.maxPartitionBytes.
E. Choose a compute instance with a higher core-to-memory ratio.


Solutions:

Question 1
Answer: C
Question 2
Answer: B
Question 3
Answer: B
Question 4
Answer: C
Question 5
Answer: C,E

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