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

Section	Weight	Objectives
Data Modeling and Storage	20%	- Storage Optimization - Data Modeling - File Formats
Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Data Processing	28%	- ETL Pipelines - Structured Streaming - Spark SQL - Data Transformation
Databricks Lakehouse Platform	24%	- Delta Lake - Data Management - Lakehouse Architecture - Unity Catalog
Monitoring and Troubleshooting	16%	- Troubleshooting - Performance Optimization - Monitoring

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q194-Q199):

NEW QUESTION # 194
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 new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
B. The update throws an error because changes to existing columns in the target schema are not supported.
C. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
D. 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.

Answer: D

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

NEW QUESTION # 195
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI.
Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job.
They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A. A user can only transfer job ownership to a group if they are also a member of that group.
B. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
C. Other than the default "admins" group, only individual users can be granted privileges on jobs.
D. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
E. Only workspace administrators can grant "Owner" privileges to a group.

Answer: D

Explanation:
A job cannot have more than one owner. A job cannot have a group as an owner.

NEW QUESTION # 196
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load 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?

A. Schedule a job to execute the pipeline once hour on a new job cluster.
B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
D. Configure a job that executes every time new data lands in a given directory.

Answer: A

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

NEW QUESTION # 197
A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

A. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
B. Enable Delta caching.
C. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.
D. Enable auto-compaction (optimizeWrite and autoCompact).

Answer: C

Explanation:
According to Databricks Delta Lake optimization documentation, Liquid Clustering is a next- generation file organization capability that automatically manages file co-location without requiring explicit partitioning or manual Z-ORDERing. When combined with Predictive Optimization, Databricks automatically maintains clustering across frequently filtered or queried columns, adapting dynamically as query workloads evolve.
This approach eliminates the need for manual maintenance (such as periodic OPTIMIZE or Z- ORDER commands) while improving query performance on large tables--particularly for high- ingest streaming workloads.
Delta caching (B) only improves performance for cached queries and does not address file layout issues, and (D) handles file size optimization but not clustering. Thus, C is the most efficient, modern, and low-maintenance solution recommended by Databricks.

NEW QUESTION # 198
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
C. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
E. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

Answer: C

Explanation:
The configuration that automatically recovers from query failures and keeps costs low is to use a new job cluster, set retries to unlimited, and set maximum concurrent runs to 1. This configuration has the following advantages:
A new job cluster is a cluster that is created and terminated for each job run. This means that the cluster resources are only used when the job is running, and no idle costs are incurred. This also ensures that the cluster is always in a clean state and has the latest configuration and libraries for the job.
Setting retries to unlimited means that the job will automatically restart the query in case of any failure, such as network issues, node failures, or transient errors. This improves the reliability and availability of the streaming job, and avoids data loss or inconsistency. Setting maximum concurrent runs to 1 means that only one instance of the job can run at a time. This prevents multiple queries from competing for the same resources or writing to the same output location, which can cause performance degradation or data corruption. Therefore, this configuration is the best practice for scheduling Structured Streaming jobs for production, as it ensures that the job is resilient, efficient, and consistent.

NEW QUESTION # 199
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