Databricks : Databricks-Certified-Data-Engineer-Professional Questions & Answers

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
D) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

2. A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A) Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
B) Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
C) Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
D) Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
E) Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

3. A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A) Updating the table schema requires a default value provided for each file added.
B) New fields cannot be added to a production Delta table.
C) New fields will not be computed for historic records.
D) Updating the table schema will invalidate the Delta transaction log metadata.
E) Spark cannot capture the topic partition fields from the kafka source.

4. Which statement describes a key benefit of an end-to-end test?

A) It closely simulates real world usage of your application.
B) It makes it easier to automate your test suite
C) It provides testing coverage for all code paths and branches.
D) It pinpoint errors in the building blocks of your application.

5. Which statement characterizes the general programming model used by Spark Structured Streaming?

A) Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
B) Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
C) Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
D) Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
E) Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

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