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

Section	Objectives
Ensuring Data Security and Compliance	- Applying Data Security Mechanisms 1. Use row filters and column masks to protect sensitive table data 2. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization 3. Use ACLs to secure workspace objects and enforce the principle of least privilege - Ensuring Compliance 1. Implement compliant batch and streaming pipelines that detect and mask PII 2. Develop data purging solutions that comply with data retention policies
Cost & Performance Optimization	- Optimize cost and performance 1. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 3. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 4. Apply Change Data Feed to address streaming table limitations and improve latency 5. Understand Delta optimization techniques such as deletion vectors and liquid clustering
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Create an append-only data pipeline capable of handling both batch and streaming data using Delta 2. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
Data Sharing and Federation	- Share and federate data 1. Configure Lakehouse Federation with appropriate governance across supported source systems 2. Use Delta Sharing to share live data from the Lakehouse with any computing platform 3. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Design dimensional models for analytical workloads with efficient querying and aggregation 3. Design and implement scalable data models using Delta Lake to manage large datasets 4. Simplify data layout decisions and optimize query performance using liquid clustering
Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Develop User-Defined Functions using Pandas/Python UDF 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 3. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration - Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 2. Explain the advantages and disadvantages of streaming tables compared to materialized views 3. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 4. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 5. Create pipeline components using control flow operators such as if/else and foreach 6. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 7. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 8. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
Monitoring and Alerting	- Monitoring 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 2. Use Query Profile and Spark UI to monitor workloads 3. Use system tables for observability of resource utilization, cost, auditing, and workloads 4. Use Lakeflow Declarative Pipelines event logs to monitor pipelines - Alerting 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues 2. Use SQL Alerts to monitor data quality
Data Governance	- Govern enterprise data 1. Create and add descriptions and metadata to enterprise data to improve discoverability 2. Demonstrate understanding of the Unity Catalog permission inheritance model
Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
Debugging and Deploying	- Debugging and Troubleshooting 1. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 2. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 3. Analyze errors and remediate failed job runs using job repairs and parameter overrides - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment

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Databricks Certified Data Engineer Professional Sample Questions (Q136-Q141):

NEW QUESTION # 136
Which statement describes Delta Lake optimized writes?

A. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
B. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
C. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

Answer: A

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 137
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?

A. Delta Lake statistics are not optimized for free text fields with high cardinality.
B. The Delta log creates a term matrix for free text fields to support selective filtering.
C. Delta Lake statistics are only collected on the first 4 columns in a table.
D. Text data cannot be stored with Delta Lake.
E. ZORDER ON review will need to be run to see performance gains.

Answer: A

Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data.

NEW QUESTION # 138
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?

A. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
B. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
C. A managed table will be created in the DBFS root storage container.
D. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
E. An managed table will be created in the storage container mounted to /mnt/finance_eda_bucket.

Answer: E

Explanation:
https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html#language-SQL

NEW QUESTION # 139
A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?

A. SELECT email_null_pct, phone_invalid_pct FROM (SELECT (COUNT(CASE WHEN email IS NULL THEN 1 END) *
100.0/COUNT (*)) as email_null_pct, (COUNT(CASE WHEN phone NOT RLIKE 'ˆ[0-9-+()\\s]+$' THEN 1 END)*
100.0/COUNT (*)) as phone_invalid_pct FROM customers)
B. SELECT COUNT (*) FROM customers WHERE email IS NULL OR phone_format_invalid = true
C. SELECT CASE WHEN email_null_pct >15 AND phone_invalid_pct> 10 THEN 1 ELSE 0 END FROM (SELECT (COUNT (CASE WHEN email IS NULL THEN 1 END) * 100.0 / COUNT (*)) as phone_invalid_pct FROM customers) metrics
D. SELECT email, phone FROM customers WHERE email IS NULL AND phone NOT RLIKE 'ˆ[0-9-
+()\\s]+$'

Answer: A

Explanation:
This pattern computes independent percentage metrics for each data quality condition in a single aggregated query. By calculating the percentage of missing emails and invalid phone formats as separate columns, it enables the SQL Alert to evaluate a compound condition where both thresholds must be exceeded before triggering.

NEW QUESTION # 140
A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
B. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
C. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
D. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")

Answer: C

Explanation:
Lakeflow Spark Declarative Pipelines combined with Auto Loader provide fully managed incremental file ingestion with exactly-once guarantees and minimal operational overhead.
Enabling schema inference and evolution allows new columns in incoming JSON files to be incorporated automatically, satisfying the requirements for streaming ingestion, schema evolution, and reduced manual infrastructure management.

NEW QUESTION # 141
......

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