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Databricks Certified Data Engineer Professional Sample Questions:

1. The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A) The update throws an error because changes to existing columns in the target schema are not supported.
B) The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
C) The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
D) The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.

2. A data engineer is performing a join operation to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A) streamingDF.join(userLookup, ["user_id"], how="left")
B) streamingDF.join(userLookup, ["user_id"], how="outer")
C) userLookup.join(streamingDF, ["user_id"], how="right")
D) streamingDF.join(userLookup, ["userid"], how="inner")
E) userLookup.join(streamingDF, ["userid"], how="inner")

3. A data engineer us ingesting JSON files from cloud object storage using Databricks Auto Loader.
The source folder may occasionally receive large files of data, which risks overwhelming the stream. To ensure predictable micro-batch sizes, the team wants to throttle ingestion based on the volume of data scanned at 1 GB, regardless of the number of files. Which Auto Loader configuration should the data engineer used to achieve this?

A) Configure cloudFiles.maxSizePerTrigger with 1 GB to place a limit.
B) Configure cloudFiles.maxPartitionBytes with 1GB to limit data in each partition.
C) Configure cloudFiles.maxFilesPerTrigger and estimate the average file size to approximate a size-based throttle of 1 GB.
D) Configure cloudFiles.maxBytesPerTrigger with 1 GB to place a limit.

4. The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A) When tables are created, make sure that the external keyword is used in the create table statement.
B) When the workspace is being configured, make sure that external cloud object storage has been mounted.
C) When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
D) Whenever a table is being created, make sure that the location keyword is used.
E) Whenever a database is being created, make sure that the location keyword is used

5. A data engineer is using Structured Streaming to read in transaction data from a bronze Delta table. It was discovered that the data has quality issues where sometimes the transaction value is negative, and when that occurs, the rows need to be routed to a separate quarantine table. They have low latency requirements for the good data since it is used by downstream systems, but the bad data will only be analyzed periodically and has no production dependencies. The quarantine job needs to be implemented so that it cannot affect the production processes that depend on the good data, and the cost of the job needs to be minimized. How should the quarantine process be implemented in order to satisfy these requirements?

A) The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing, and should not share compute with other processes. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing, and should be implemented on a separate small cluster and only run once a day to minimize cost.
B) The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. A new boolean column called "quarantine" should be added to the dataframe, and its value should be set to true if the transaction value is less than 0 and false if the transaction value is greater than or equal to 0. Processing and storing all the data together will save costs.
C) The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing. Both should run as separate streams on the same cluster to minimize cost.
D) The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. Inside a foreachBatch function, the dataframe should be filtered so that records with a transaction value greater than or equal to 0 are written to the good data table and records with a transaction value less than 0 are written to a quarantine table. Try/Catch can be added around the writes in the foreachBatch function so that the stream can't fail.

Solutions: