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

Section	Objectives
Topic 1: Data Ingestion and Processing	- ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals
Topic 2: Delta Lake and Data Management	- Time travel and versioning - Schema evolution and enforcement - Delta Lake transactions and ACID properties
Topic 3: Data Modeling and Transformation	- Spark SQL transformations - Performance optimization techniques - Dimensional modeling concepts
Topic 4: Production Pipelines and Orchestration	- Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies
Topic 5: Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q181-Q186):

NEW QUESTION # 181
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.autoBroadcastJoinThreshold
B. spark.sql.files.maxPartitionBytes
C. spark.sql.files.openCostInBytes
D. spark.sql.adaptive.advisoryPartitionSizeInBytes
E. spark.sql.adaptive.coalescePartitions.minPartitionNum

Answer: B

Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.

NEW QUESTION # 182
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
B. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
C. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
D. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

Answer: D

Explanation:
The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let's understand this model in more detail.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

NEW QUESTION # 183
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 tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
C. Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
D. Share tables WITHOUT HISTORY and enable partitioning for better query performance.

Answer: B

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 # 184
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
C. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.

Answer: B

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 185
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
B. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
C. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
D. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
E. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.

Answer: C

Explanation:
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources. By Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted.

NEW QUESTION # 186
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