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| Section | Weight | Objectives |
|---|---|---|
| Data Processing with Spark | 25-30% | - Spark DataFrames and Spark SQL
|
| Delta Lake | 20-25% | - Delta Lake operations
|
| Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
| Data Ingestion | 15-20% | - Batch ingestion methods
|
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질문 # 169
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?
정답：A
설명：
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "A shallow clone creates a copy of the metadata that references the source data files; it is fast and inexpensive." Exact extract: "A deep clone copies the data." Exact extract: "Clones provide a point-in-time snapshot for experimentation without impacting the source." A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for 20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.
질문 # 170
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?
정답：A
설명：
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.
:
Databricks documentation on Delta Lake schema management: https://docs.databricks.com/delta/delta-batch.
html#schema-management
질문 # 171
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?
정답：E
설명：
Explanation
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. References: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE
질문 # 172
A data team ' s Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Which step must also be completed to put the proposed query into production?
정답：D
설명：
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
:
Databricks documentation on Structured Streaming: https://docs.databricks.com/spark/latest/structured- streaming/index.html Databricks documentation on streaming checkpoints: https://docs.databricks.com/spark/latest/structured- streaming/production.html#checkpointing
질문 # 173
The business reporting team requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts, transforms, and loads the data for their pipeline runs in 10 minutes. Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?
정답：C
설명：
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Job clusters are created for a job run and terminate when the job completes." Exact extract: "All-purpose (interactive) clusters are intended for interactive development and collaboration." An hourly batch that completes in ~10 minutes is best run on ephemeral job clusters so compute exists only during execution and auto-terminates afterward, minimizing cost while meeting the hourly SLA. Interactive clusters persist (incurring idle cost). Structured Streaming for an hourly refresh adds continuous compute cost with no benefit here. Event-driven triggers are unnecessary for a simple hourly SLA.
Reference:
질문 # 174
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Databricks-Certified-Professional-Data-Engineer최신 인증시험 기출문제: https://www.itcertkr.com/Databricks-Certified-Professional-Data-Engineer_exam.html
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