Databricks-DE-Pro Debugging and Deploying Practice Question
Exhibit
{
"error": "AnalysisException",
"message": "Table or view not found: default.sales_data",
"trace": "at org.apache.spark.sql.errors.QueryCompilationErrors$.tableOrViewNotFoundError"
}Refer to the exhibit. A data engineer is deploying a production pipeline that references a table in the default schema. The job fails with the provided error. What is the root cause?
⚠ Common exam trap
Test-takers often assume local temporary views or notebook session-scoped tables persist automatically when the code is deployed as a production job.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The table exists only in the temporary session catalog of a different notebook.
The error indicates that the Spark session cannot resolve the table 'default.sales_data'. In Databricks, jobs often run in a different environment or context than an interactive notebook. If the table was created in an interactive session, it might not exist in the environment where the job runs, or the database context is missing. This highlights the importance of using absolute paths or proper schema initialization in production code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The cluster is running an outdated version of the Spark runtime.
Why it's wrong here
An outdated runtime would typically trigger incompatibility errors or library missing errors rather than a table-not-found exception. The error clearly specifies a metadata lookup failure within the catalog service, which is independent of the specific Spark version running on the cluster nodes during job execution.
- ✗
The job is running under a service principal that lacks permissions to the Hive Metastore.
Why it's wrong here
Insufficient permissions usually result in an 'AccessDenied' or 'AuthorizationException' rather than a 'TableNotFound' error. The system is successfully querying the catalog but failing to locate the specific table object, implying a configuration or environment mismatch rather than a security or identity-based restriction issue.
- ✓
The table exists only in the temporary session catalog of a different notebook.
Why this is correct
Temporary views or tables created in interactive notebook sessions are not persisted in the shared Hive Metastore and are scoped to the session. Since the job runs in a separate, isolated environment, it cannot access objects defined in the transient memory of a different interactive session.
- ✗
The cluster has insufficient memory to load the table metadata.
Why it's wrong here
Metadata operations are lightweight and managed by the metastore service, not the cluster's RAM. Insufficient memory would manifest as a 'java.lang.OutOfMemoryError' during the processing stage, not as a failure to resolve a table name. The error is strictly related to catalog resolution and object location.
Visual reference
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.