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

Section	Weight	Objectives
Topic 1: Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Spark SQL queries and functions 2. DataFrame operations and transformations 3. Window functions - Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions
Topic 2: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Monitoring and alerting 3. Task dependencies and orchestration
Topic 3: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Data governance fundamentals 3. Differences between data lake, data warehouse, and lakehouse
Topic 4: Data Ingestion	15-20%	- Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration - Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems
Topic 5: Delta Lake	20-25%	- Delta Lake operations 1. Schema evolution and enforcement 2. Merge, update, delete operations 3. Delta Live Tables - Delta Lake fundamentals 1. Optimize and Z-order 2. Time travel and data versioning 3. ACID transactions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q171-Q176):

NEW QUESTION # 171
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 and Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
B. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
C. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024
/512), and then write to parquet.
D. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
E. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

Answer: B

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.
References
* Apache Spark Configuration
* Writing to Parquet Files in Spark

NEW QUESTION # 172
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

Answer: C

Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time.
However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

NEW QUESTION # 173
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
C. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
D. The update throws an error because changes to existing columns in the target schema are not supported.

Answer: A

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
:
Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.
html#schema-evolution

NEW QUESTION # 174
A Delta Lake table representing metadata about content from user has the following schema:
Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. User_id
B. Post_time
C. Post_id
D. Date

Answer: D

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
* Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned.
* Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
* Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.
References:
* Delta Lake Documentation on Table Partitioning: Optimizing Layout with Partitioning

NEW QUESTION # 175
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse.
The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

Answer: C

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints.
Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.References:
* Databricks Documentation on Delta Lake: Delta Lake Guide
* Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

NEW QUESTION # 176
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