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

SectionWeightObjectives
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Bronze, silver, gold data layers
  • 2. Data governance fundamentals
  • 3. Differences between data lake, data warehouse, and lakehouse
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Task dependencies and orchestration
  • 2. Jobs and job scheduling
  • 3. Monitoring and alerting
Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Performance optimization techniques
  • 2. Spark APIs in Python
  • 3. Built-in and user-defined functions
- Spark DataFrames and Spark SQL
  • 1. Spark SQL queries and functions
  • 2. DataFrame operations and transformations
  • 3. Window functions
Data Ingestion15-20%- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
- Batch ingestion methods
  • 1. Spark APIs for ingestion
  • 2. Integration with external systems
  • 3. DBR autoloader
Delta Lake20-25%- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Optimize and Z-order
  • 3. Time travel and data versioning
- Delta Lake operations
  • 1. Delta Live Tables
  • 2. Schema evolution and enforcement
  • 3. Merge, update, delete operations

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q198-Q203):

NEW QUESTION # 198
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

Answer: A


NEW QUESTION # 199
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

Answer: C

Explanation:
Explanation
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under
"Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.


NEW QUESTION # 200
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

Answer: E

Explanation:
For this scenario where a one-TB JSON dataset needs to be converted into Parquet format without employing Delta Lake's auto-sizing features, the goal is to avoid unnecessary data shuffles and yet ensure optimal file sizes for the output Parquet files. Here's a breakdown of why option A is most suitable:
Setting maxPartitionBytes: The spark.sql.files.maxPartitionBytes configuration controls the size of blocks that Spark reads from the data source (in this case, the JSON files) but also influences the output size of files when data is written without repartition or coalesce operations. Setting this parameter to 512 MB directly addresses the requirement to manage the output file size effectively.
Data Ingestion and Processing:
Ingesting Data: Load the JSON dataset into a DataFrame.
Applying Transformations: Perform any required narrow transformations that do not involve shuffling data (like filtering or adding new columns).
Writing to Parquet: Directly write the transformed DataFrame to Parquet files. The setting for maxPartitionBytes ensures that each part-file is approximately 512 MB, meeting the requirement for part-file size without additional steps to repartition or coalesce the data.
Performance Consideration: This approach is optimal because:
It avoids the overhead of shuffling data, which can be significant, especially with large datasets.
It directly ties the read/write operations to a configuration that matches the target output size, making it efficient in terms of both computation and I/O operations.
Alternative Options Analysis:
Option B and D: Involves repartitioning, which would trigger a shuffle of the data, contradicting the requirement to avoid shuffling for performance reasons.
Option C: Uses coalesce, which is less intensive than repartition but can still lead to uneven partition sizes and does not directly control the output file size as effectively as setting maxPartitionBytes.
Option E: Setting shuffle partitions to 512 doesn't directly control the output file size for writing to Parquet and could lead to smaller files depending on the dataset's partitioning post-transformations.
Reference
Apache Spark Configuration
Writing to Parquet Files in Spark


NEW QUESTION # 201
A SQL Dashboard was built for the supply chain team to monitor the inventory and product orders, but all of the timestamps displayed on the dashboards are showing in UTC format, so they requested to change the time zone to the location of New York. How would you approach resolving this issue?

Answer: B

Explanation:
Explanation
The answer is, Under SQL Admin Console, set the SQL configuration parameter time zone to America/New_York Here are steps you can take this to configure, so the entire dashboard is changed without changing individual queries Configure SQL parameters To configure all warehouses with SQL parameters:
1.Click Settings at the bottom of the sidebar and select SQL Admin Console.
2.Click the SQL Warehouse Settings tab.
3.In the SQL Configuration Parameters textbox, specify one key-value pair per line. Sepa-rate the name of the parameter from its value using a space. For example, to ena-ble ANSI_MODE:
Graphical user interface, text, application Description automatically generated

Similarly, we can add a line in the SQL Configuration parameters
timezone America/New_York
SQL configuration parameters | Databricks on AWS


NEW QUESTION # 202
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A - automated orchestration and management in Lakeflow Declarative Pipelines.


NEW QUESTION # 203
......

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