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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id.
What should the data engineer do to ensure events are sequenced correctly?

A) Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
B) Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
C) Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
D) Use dropDuplicates() to remove out-of-order and duplicate records in LDP.

2. 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) Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
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) Shuffle Hash Joins are always more efficient than Sort Merge Joins.

3. Which of the following is true of Delta Lake and the Lakehouse?

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

4. A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?

A) Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
B) Run the notebook on a single node cluster to keep driver from falling.
C) Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
D) Rewrite their code to avoid putting memory pressure on the driver node.

5. A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?

A) Use a service principal ID and its OAuth client secret.
B) Use a shared user account and its OAuth client secret.
C) Use a service principal with OAuth token federation.
D) Use a service principal and its Personal Access Token.

Solutions: