The answer is to replicate the ProductDim and DateDim tables to all compute nodes. This is correct because replicating small dimension tables, such as ProductDim at 100 MB and DateDim at 5 MB, eliminates the need for data movement during joins with the hash-distributed SalesFact table. In Azure Synapse Analytics, replicated tables are copied to every distribution, so when the fact table joins on ProductID and DateID, no shuffling of the dimension data occurs, directly reducing the high data movement and long execution times for aggregation queries by product category and date. On the DP-203 exam, this scenario tests your understanding of Synapse dedicated SQL pool table distribution strategies, specifically when to use replication versus hash or round-robin distribution. A common trap is attempting to change the fact table’s distribution key or using round-robin for the dimensions, but the correct approach is to replicate any dimension table under 1 GB. Memory tip: “Replicate the small, hash the tall”—replicate tables under 1 GB to avoid shuffling, and keep hash distribution for large fact tables.
DP-203 Design and implement data storage Practice Question
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 a data engineer for a multinational e-commerce company. The company uses Azure Synapse Analytics as its data warehouse. The current fact table, SalesFact, is distributed using hash distribution on the CustomerID column. It has 2 billion rows and is 2 TB in size. Recently, the business team has been running many queries that aggregate sales by product category and date, and these queries are experiencing high data movement and long execution times. The product dimension table (ProductDim) has 100,000 rows and is 100 MB. The date dimension table (DateDim) has 5,000 rows and is 5 MB. You need to redesign the storage to minimize data movement for these aggregation queries. You cannot change the fact table distribution key to ProductID because of other critical queries that rely on CustomerID. What should you do?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Replicate the ProductDim and DateDim tables to all compute nodes
Option B is correct because replicating small dimension tables (ProductDim at 100 MB and DateDim at 5 MB) to all compute nodes eliminates the need to shuffle these tables across nodes during joins. In Azure Synapse Analytics, replicated tables are copied to each distribution, so when the fact table (hash-distributed on CustomerID) joins with ProductDim and DateDim on ProductID and DateID, no data movement occurs for the dimension tables. This directly reduces the high data movement and long execution times for aggregation queries by product category and date.
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.
✗
Create materialized views on the fact table that aggregate by product category and date
Why it's wrong here
Materialized views do not eliminate data movement; they store pre-aggregated results.
✓
Replicate the ProductDim and DateDim tables to all compute nodes
Why this is correct
Replication eliminates data movement for small dimension tables.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Partition the fact table by date and keep the same distribution
Why it's wrong here
Partitioning helps with partition elimination but not data movement.
✗
Change the fact table distribution to round-robin and create non-clustered indexes on ProductID and DateID
Why it's wrong here
Round-robin would increase data movement for all queries.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose materialized views (Option A) thinking they solve all aggregation performance issues, but they overlook that data movement from joins with non-replicated dimension tables remains the bottleneck, whereas table replication directly addresses the shuffle cost for small dimension tables.
Detailed technical explanation
How to think about this question
Replicated tables in Azure Synapse Analytics work by copying the entire table to the first distribution of each compute node, allowing joins with distributed fact tables to occur locally without shuffling. The threshold for replication is typically tables under 60 GB (or 48 GB after compression), making ProductDim (100 MB) and DateDim (5 MB) ideal candidates. Under the hood, Synapse uses a round-robin copy to each distribution, and the optimizer automatically chooses replicated table join strategies when the table is small enough, drastically reducing query execution time for star-schema queries.
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.
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: Replicate the ProductDim and DateDim tables to all compute nodes — Option B is correct because replicating small dimension tables (ProductDim at 100 MB and DateDim at 5 MB) to all compute nodes eliminates the need to shuffle these tables across nodes during joins. In Azure Synapse Analytics, replicated tables are copied to each distribution, so when the fact table (hash-distributed on CustomerID) joins with ProductDim and DateDim on ProductID and DateID, no data movement occurs for the dimension tables. This directly reduces the high data movement and long execution times for aggregation queries by product category and date.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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