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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

A) Shuffle Hash Joins are always more efficient than Sort Merge Joins.
B) The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
C) The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
D) Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.

2. 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?

A) Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B) 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.
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.files.maxPartitionBytes to 512 MB, 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.

3. What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A) PII masking is only required for direct identifiers.
B) Dynamic data masking is applied only at rest, so it does not affect query performance.
C) Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.
D) Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.

4. A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

A) Use secrets in init scripts to store configuration data
B) Install libraries on DBFS
C) Create init scripts on DBFS.
D) Add libraries to compute policies
E) Use compute policies to set system properties, environment variables, and Spark configuration parameters.

5. An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A) Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
B) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
C) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
D) Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
E) Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.

Solutions: