The answer is that the table is not registered in the Spark metastore; it only exists in the lakehouse. This is because a Spark notebook in Azure Synapse Analytics resolves table references through its own internal Spark metastore, not directly against the lakehouse storage layer. When you create a Delta table in the lakehouse—for example, by writing data to a storage path—it does not automatically create a corresponding entry in the Spark metastore. The Spark engine needs that metadata registration to locate and query the table, so without it, you get an AnalysisException even though the underlying data files exist. On the DP-203 exam, this scenario tests your understanding of the separation between storage and metadata in Azure Synapse, a common trap where candidates assume a table in the lakehouse is automatically queryable from any Spark session. Remember the tip: "Lakehouse holds the data, metastore holds the map"—if the map is missing, Spark can't find the treasure.
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are running a Spark notebook in Azure Synapse Analytics that reads from a Delta table and writes to a Parquet file. The job fails with the error: 'AnalysisException: Table or view not found: bronze.sales'. The table exists in the lakehouse. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The table is not registered in the Spark metastore; it is only in the lakehouse.
In Azure Synapse Analytics, a Spark notebook uses its own Spark metastore to resolve table references. If the table 'bronze.sales' exists only in the lakehouse (i.e., as a Delta table in the underlying storage) but is not registered in the Spark metastore, the Spark engine cannot find it and throws an AnalysisException. The error indicates a metadata resolution failure, not a permission or data corruption issue.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 user does not have read permission on the table.
Why it's wrong here
Permission errors typically return an AuthorizationException, not AnalysisException.
✓
The table is not registered in the Spark metastore; it is only in the lakehouse.
Why this is correct
In Synapse, lakehouse tables are stored in a separate catalog; the Spark session's default catalog may not include it, so the table is not found.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The Parquet file format is incompatible with the Delta source.
Why it's wrong here
Delta can be read as Parquet, but the error is about table not found, not format.
✗
The Delta table is corrupted.
Why it's wrong here
Corruption would cause a read failure, not a table not found error.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the misconception that a table existing in the lakehouse automatically makes it visible to Spark notebooks, when in fact the Spark metastore and lakehouse catalog are separate metadata layers that must be explicitly synchronized.
Detailed technical explanation
How to think about this question
The Spark metastore in Azure Synapse can be either a default Hive metastore (backed by Azure SQL Database) or a custom metastore. When a Delta table is created via the lakehouse UI or SQL endpoint, it may not be automatically registered in the Spark metastore unless explicitly created using Spark SQL (e.g., CREATE TABLE USING DELTA). The error occurs because Spark resolves table names by querying the metastore, not by scanning the lakehouse storage directly. A common workaround is to use the table path directly (e.g., spark.read.format('delta').load('abfss://...')) or register the table with CREATE TABLE IF NOT EXISTS.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The table is not registered in the Spark metastore; it is only in the lakehouse. — In Azure Synapse Analytics, a Spark notebook uses its own Spark metastore to resolve table references. If the table 'bronze.sales' exists only in the lakehouse (i.e., as a Delta table in the underlying storage) but is not registered in the Spark metastore, the Spark engine cannot find it and throws an AnalysisException. The error indicates a metadata resolution failure, not a permission or data corruption issue.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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