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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
B. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
C. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
D. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
E. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

Answer: E

Explanation:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

NEW QUESTION # 93
Which of the following is true of Delta Lake and the Lakehouse?

A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
B. Z-order can only be applied to numeric values stored in Delta Lake tables
C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
E. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

Answer: C

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.

NEW QUESTION # 94
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Rely on Delta Lake schema enforcement to prevent duplicate records.
B. VACUUM the Delta table after each batch completes.
C. Perform an insert-only merge with a matching condition on a unique key.
D. Perform a full outer join on a unique key and overwrite existing data.
E. Set the configuration delta.deduplicate = true.

Answer: C

Explanation:
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.

NEW QUESTION # 95
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
B. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)
C. Store only the date value and calculate all time attributes in queries.
D. Store the date as string in ISO format (YYYY-MM-DD) for readability.

Answer: A

Explanation:
Pre-calculating commonly used time attributes in the date dimension enables efficient filtering, grouping, and aggregation without requiring repeated computations at query time. This design supports a wide range of time-based analyses and improves query performance and usability in dimensional models.

NEW QUESTION # 96
A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
B. Use the "Validate" option in the notebook to check for syntax errors.
C. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
D. Open the web terminal from the notebook and run a shell command to validate the pipeline code.

Answer: B

Explanation:
Databricks provides a "Validate" option within the Lakeflow Declarative Pipeline development interface that checks pipeline configurations, transformations, and syntax errors before actual execution.
This feature parses and validates the pipeline logic defined in notebooks or workspace files to ensure correctness and consistency of table dependencies, DLT (Delta Live Table) syntax, and schema references.
The validation process does not process or move any data, making it ideal for testing new configurations before deployment.
Using the shell terminal (B) or workspace files (D) does not perform integrated pipeline-level validation, while reconnecting to compute clusters (C) is unrelated to syntax checks. Therefore, the verified and correct approach is A.

NEW QUESTION # 97
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