- A
The dimension table is not actually replicated due to its size exceeding the replication threshold
If the dimension table is too large to be replicated, it will be distributed and cause shuffle.
- B
The query is running with a low resource class
Why wrong: Resource class affects concurrency, not shuffle.
- C
Result set caching is enabled
Why wrong: Caching would improve performance, not cause shuffle.
- D
Statistics are outdated on the dimension table
Why wrong: Outdated statistics may cause suboptimal plans but not necessarily shuffle.
Question 653 of 851
Diagnose Shuffle Moves in Synapse Dedicated SQL Pool Joins
This DP-203 practice question tests your understanding of design and implement data storage. 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 troubleshooting a slow-running query in Azure Synapse Analytics dedicated SQL pool. The query joins a large fact table (hash-distributed on ProductID) with a small dimension table (replicated). Upon reviewing the query plan, you see a 'ShuffleMove' operation. What is the most likely cause of the slow performance?
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 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 dimension table is not actually replicated due to its size exceeding the replication threshold
The 'ShuffleMove' operation in a Synapse dedicated SQL pool query plan indicates that data is being moved between distributions to complete the join. If the small dimension table is supposed to be replicated but exceeds the replication threshold (default 60 GB compressed), it will not be replicated and instead remains hash-distributed. This forces a shuffle of the large fact table's data to align with the dimension table's distribution, causing significant data movement and slow performance.
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 dimension table is not actually replicated due to its size exceeding the replication threshold
Why this is correct
If the dimension table is too large to be replicated, it will be distributed and cause shuffle.
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 query is running with a low resource class
Why it's wrong here
Resource class affects concurrency, not shuffle.
- ✗
Result set caching is enabled
Why it's wrong here
Caching would improve performance, not cause shuffle.
- ✗
Statistics are outdated on the dimension table
Why it's wrong here
Outdated statistics may cause suboptimal plans but not necessarily shuffle.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a 'ShuffleMove' is always caused by a join key mismatch or poor statistics, but the specific scenario of a dimension table failing to replicate due to size is a common and subtle cause that directly triggers data movement.
Detailed technical explanation
How to think about this question
In Azure Synapse dedicated SQL pool, replication is a table distribution strategy that copies the entire table to each Compute node. The replication threshold is 60 GB compressed; if the table exceeds this, it remains hash-distributed. When a hash-distributed fact table joins a hash-distributed dimension table on different distribution keys, a 'ShuffleMove' (also called data movement or broadcast shuffle) is required to redistribute rows across distributions. This operation is expensive because it involves serializing, transferring, and deserializing data over the internal network, and can dominate query execution time in large fact tables.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-203 question test?
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The dimension table is not actually replicated due to its size exceeding the replication threshold — The 'ShuffleMove' operation in a Synapse dedicated SQL pool query plan indicates that data is being moved between distributions to complete the join. If the small dimension table is supposed to be replicated but exceeds the replication threshold (default 60 GB compressed), it will not be replicated and instead remains hash-distributed. This forces a shuffle of the large fact table's data to align with the dimension table's distribution, causing significant data movement and slow performance.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You have an Azure Synapse Analytics dedicated SQL pool with a table that uses hash distribution on CustomerID. You notice that queries joining this table with another table on OrderDate are slow. What is the most likely cause?
hard- A.The table is not partitioned by OrderDate
- B.Statistics on the join columns are outdated
- C.The table should use round-robin distribution instead
- ✓ D.The join columns are not aligned; data must be shuffled across distributions
Why D: D is correct because in Azure Synapse Analytics dedicated SQL pools, hash distribution distributes rows across distributions based on a hash of the distribution key (CustomerID). When joining on OrderDate, which is not the distribution key, the join columns are not aligned across distributions. This forces data movement (shuffling) where rows from one or both tables must be redistributed to match the join key, causing significant performance degradation.
Variation 2. You are tuning a dedicated SQL pool in Azure Synapse Analytics. A query that joins two large tables (fact_sales and dim_product) is slow. The fact_sales table is hash-distributed on product_id, and dim_product is replicated. You notice that the query plan shows a shuffle move. What is the most likely cause?
hard- A.The fact_sales table uses clustered columnstore index.
- B.The dim_product table is replicated, causing a broadcast join.
- C.Statistics are out of date on both tables.
- ✓ D.The join condition does not include the distribution key for fact_sales.
Why D: Option D is correct because when the join condition does not include the distribution key (product_id) of the hash-distributed fact_sales table, the SQL engine cannot perform a collocated join. Instead, it must shuffle data across distributions to satisfy the join, which introduces expensive data movement. The shuffle move in the query plan directly indicates this redistribution.
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Last reviewed: Jun 24, 2026
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