Certification Databricks Certified-Data-Engineer-Professional Training, Certified-Data-Engineer-Professional Certified Questions

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

Section	Objectives
Topic 1: Ensuring Data Security and Compliance	- Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information - Data Security 1. Use ACLs to secure workspace objects and enforce least privilege 2. Use row filters and column masks for sensitive data 3. Apply anonymization and pseudonymization techniques
Topic 2: Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations - Data Quality 1. Develop data quarantining processes for invalid data 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
Topic 3: Monitoring and Alerting	- Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues - Monitoring 1. Use system tables for resource, cost, audit, and workload monitoring 2. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 4. Use Query Profiler and Spark UI to monitor workloads
Topic 4: Data Governance	- Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data - Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model
Topic 5: Cost & Performance Optimisation	- Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Delta Optimization 1. Apply data skipping and file pruning techniques 2. Use Change Data Feed to address streaming table limitations and improve latency 3. Understand deletion vectors and liquid clustering - Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling
Topic 6: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Develop User-Defined Functions using Pandas/Python UDFs 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 3. Manage and troubleshoot third-party library installations and dependencies - Building and Testing ETL Pipelines 1. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 2. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 3. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 4. Compare streaming tables and materialized views 5. Use APPLY CHANGES APIs for change data capture 6. Use control flow operators in pipeline components 7. Configure environments, dependencies, memory, and retry behavior 8. Develop unit and integration tests for data processing code
Topic 7: Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Share live Lakehouse data with external computing platforms 2. Configure Databricks-to-Databricks Sharing 3. Configure sharing with external platforms using the open sharing protocol
Topic 8: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Build append-only pipelines for batch and streaming data using Delta 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 3. Ingest data from message buses and cloud storage
Topic 9: Debugging and Deploying	- Deploying CI/CD 1. Integrate Git-based CI/CD workflows using Databricks Git Folders 2. Build and deploy Databricks resources using Databricks Asset Bundles - Debugging and Troubleshooting 1. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 2. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 3. Analyze errors and remediate failed job runs
Topic 10: Data Modelling	- Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Understand Liquid Clustering versus partitioning and Z-Ordering 3. Design and implement scalable data models using Delta Lake - Dimensional Modelling 1. Design dimensional models for analytical workloads

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Certified-Data-Engineer-Professional Certified Questions - Latest Certified-Data-Engineer-Professional Material

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Databricks Certified Data Engineer Professional Sample Questions (Q30-Q35):

NEW QUESTION # 30
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The event_date column is outside the table's partitioning and Z-ordering scheme.
B. The Delta table lacks optimization that enables dynamic file pruning.
C. The filter is executed only after the full data scan, preventing data skipping.
D. The filter condition involves a data type excluded from data skipping support.

Answer: A

Explanation:
Delta Lake's data skipping relies on partitioning and clustering (such as Z-ordering) on the filtered columns. If event_date is neither a partition column nor included in the table's clustering strategy, Spark must scan most files because file-level statistics cannot be effectively used to prune irrelevant data.

NEW QUESTION # 31
A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A. Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
B. Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
C. Share tables WITHOUT HISTORY and enable partitioning for better query performance.
D. Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.

Answer: D

Explanation:
The official Delta Sharing guidance specifies that in order for recipients to use time travel queries and streaming reads, providers must share Delta tables WITH HISTORY. Sharing history ensures the Delta log is included, which enables efficient access to table snapshots and incremental data streams.
Additionally, Change Data Feed (CDF) must be enabled prior to sharing if downstream consumers require streaming CDC queries. Without history, recipients cannot perform time travel or streaming queries. Open sharing supports static Delta tables but lacks streaming support.
Therefore, sharing tables WITH HISTORY and enabling CDF is the required configuration for both performance and functionality.

NEW QUESTION # 32
A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A. Use a SQL CONSTRAINT block referencing the JSON file path.
B. Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
C. Reference each expectation with @dlt.expect decorators in the table declaration.
D. Invoke an external API to validate records against the metadata rules.

Answer: B

Explanation:
Lakeflow Declarative Pipelines provide the expect_all method for programmatically applying multiple data quality expectations at once. The documentation explains that @dlt.expect_all accepts a dictionary of expectation names mapped to SQL constraints, allowing rules to be dynamically loaded from metadata such as JSON files. This ensures that pipelines remain maintainable and scalable without needing to hardcode individual @dlt.expect decorators. The event logs will track each expectation's pass and fail counts individually, making it auditable.

NEW QUESTION # 33
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When tables are created, make sure that the external keyword is used in the create table statement.
B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
C. Whenever a table is being created, make sure that the location keyword is used.
D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
E. Whenever a database is being created, make sure that the location keyword is used

Answer: C

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

NEW QUESTION # 34
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
B. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
C. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
D. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
E. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.

Answer: B

Explanation:
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types.

NEW QUESTION # 35
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