A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A. If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?
Correct Answer: A
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV. The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Create an external table" section.
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table. Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales. Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
Correct Answer: E
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.
Given the following error traceback (from display(df.select(3*"heartrate"))) which shows AnalysisException: cannot resolve 'heartrateheartrateheartrate', which statement describes the error being raised?
Correct Answer: C
Comprehensive and Detailed Explanation From Exact Extract: Exact extract: "select() expects column names or Column expressions." Exact extract: "Use col("name") (or df["name"]) to reference a column; Python string operations act on strings, not columns."
While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling. How should the data engineer address this issue?
Correct Answer: D
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents: When Spark performs wide transformations such as sortBy or orderBy, large data volumes can exceed memory limits, causing disk spilling. The official Databricks performance tuning guide recommends increasing the shuffle partition count to distribute the data more evenly across executors. By default, Spark uses a fixed number of shuffle partitions (e.g., 200), which can lead to memory imbalance and spill if some partitions are too large. Increasing this number (via spark.sql.shuffle.partitions) results in smaller partitions, reduced in-memory pressure, and improved sort performance. Other options like broadcast joins or single partition sorts do not apply to single-table sorts, and converting to filters changes query logic. Thus, option D is the correct remedy.
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?
Correct Answer: C
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents: Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs. Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases. Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.
