When Scan is acceptable in DynamoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

When is using Scan in DynamoDB acceptable?

Which situation is the best reason to use a Scan operation in DynamoDB?

A When you want to query items using a specific partition key.

B When you want to get a single item by its primary key.

C When you need to retrieve all items from a small table without filters.

D When you want to update an item conditionally.

💡 Hint

Think about when scanning the whole table is not costly or inefficient.

Scan reads every item in the table, so it is acceptable only when the table is small and you need all data without filtering by keys.

🧠 Conceptual

intermediate

Why avoid Scan on large DynamoDB tables?

What is the main reason to avoid using Scan on large DynamoDB tables?

A Scan does not support filtering items.

B Scan can cause high latency and consume a lot of read capacity units.

C Scan only returns items sorted by primary key.

D Scan cannot read all attributes of an item.

💡 Hint

Think about the cost and speed of reading many items.

Scan reads every item, which can be slow and expensive on large tables because it consumes many read capacity units and increases latency.

❓ query_result

advanced

Output of Scan on a small DynamoDB table

Given a DynamoDB table with 3 items, what will be the output of a Scan operation without filters?

DynamoDB

Table items:
1. {"id": "1", "name": "Alice"}
2. {"id": "2", "name": "Bob"}
3. {"id": "3", "name": "Carol"}

Scan operation: scan the whole table without any filter.

A SyntaxError

B [{"id": "1", "name": "Alice"}]

C []

D [{"id": "1", "name": "Alice"}, {"id": "2", "name": "Bob"}, {"id": "3", "name": "Carol"}]

💡 Hint

Scan returns all items if no filter is applied.

Scan reads all items in the table and returns them as a list when no filter is used.

❓ schema

advanced

Design choice for Scan usage

You have a DynamoDB table expected to grow very large. You need to occasionally retrieve all items for a report. What is the best design choice to allow efficient Scan usage?

A Keep the table small by archiving old data to another storage regularly.

B Use Scan frequently on the large table without changes.

C Use Query with a random partition key to simulate Scan.

D Avoid any data retrieval to prevent Scan.

💡 Hint

Think about how to keep Scan efficient by controlling table size.

Archiving old data keeps the table small, making Scan efficient and acceptable for occasional full reads.

🔧 Debug

expert

Why does this Scan operation cause high latency?

You run a Scan on a large DynamoDB table and notice very high latency and throttling. Which option explains the cause?

DynamoDB

Scan operation without any filter or pagination on a table with millions of items.

A Scan reads the entire table at once, consuming too many read capacity units and causing throttling.

B Scan only reads items with a specific partition key, so it should be fast.

C Scan automatically uses indexes to speed up reading, so latency is unrelated.

D Scan only reads the first 100 items, so latency should be low.

💡 Hint

Consider how Scan works internally on large tables.

Scan reads every item in the table, so on large tables it consumes many read capacity units at once, causing throttling and high latency.

Practice

1. Which situation is best suited for using a Scan operation in DynamoDB?

easy

A. When you want to update an item quickly.

B. When you want to retrieve a single item by its primary key.

C. When you need to read all items from a small table.

D. When you want to delete an item by its key.

Solution

1. Step 1: Understand Scan operation purpose

   Scan reads every item in the table, which is simple but slow for large tables.

2. Step 2: Match Scan use case

   Scan is acceptable when the table is small and you need all data, not a specific item by key.

3. Final Answer:

   When you need to read all items from a small table. -> Option C

4. Quick Check:

   Scan = small table full read [OK]

Hint: Use Scan only for small tables or full data needs [OK]

Common Mistakes:

• Using Scan to get a single item by key
• Using Scan for updates or deletes
• Ignoring performance impact on large tables

2. Which of the following is the correct syntax to perform a Scan operation with a filter expression in DynamoDB?

easy

A. Scan(TableName='Users', ConditionExpression='Age > :age')

B. Scan(TableName='Users', KeyConditionExpression='Age > :age', ExpressionAttributeValues={':age': 30})

C. Scan(TableName='Users', Filter='Age > 30')

D. Scan(TableName='Users', FilterExpression='Age > :age', ExpressionAttributeValues={':age': 30})

Solution

1. Step 1: Identify correct Scan parameters

   Scan uses FilterExpression to filter results after scanning all items.

2. Step 2: Check syntax correctness

   Scan(TableName='Users', FilterExpression='Age > :age', ExpressionAttributeValues={':age': 30}) uses FilterExpression and ExpressionAttributeValues correctly for filtering.

3. Final Answer:

   Scan(TableName='Users', FilterExpression='Age > :age', ExpressionAttributeValues={':age': 30}) -> Option D

4. Quick Check:

   FilterExpression syntax = Scan(TableName='Users', FilterExpression='Age > :age', ExpressionAttributeValues={':age': 30}) [OK]

Hint: FilterExpression filters after Scan, not KeyConditionExpression [OK]

Common Mistakes:

• Using KeyConditionExpression with Scan
• Using wrong parameter names like Filter or ConditionExpression
• Not providing ExpressionAttributeValues for placeholders

3. Given a DynamoDB table 'Products' with 3 items: {"ID":1, "Category":"Book"}, {"ID":2, "Category":"Toy"}, {"ID":3, "Category":"Book"}, what will be the result of this Scan operation?

Scan(TableName='Products', FilterExpression='Category = :cat', ExpressionAttributeValues={':cat': 'Book'})

medium

A. [{"ID":1, "Category":"Book"}, {"ID":3, "Category":"Book"}]

B. [{"ID":2, "Category":"Toy"}]

C. [{"ID":1, "Category":"Book"}, {"ID":2, "Category":"Toy"}, {"ID":3, "Category":"Book"}]

D. []

Solution

1. Step 1: Understand Scan with FilterExpression

   Scan reads all items, then returns only those matching the filter Category = 'Book'.

2. Step 2: Apply filter to items

   Items with Category 'Book' are ID 1 and ID 3, so only these are returned.

3. Final Answer:

   [{"ID":1, "Category":"Book"}, {"ID":3, "Category":"Book"}] -> Option A

4. Quick Check:

   Filter returns matching items only [OK]

Hint: Scan returns all, filter narrows results after [OK]

Common Mistakes:

• Expecting Scan to return only filtered items without FilterExpression
• Confusing Scan with Query operation
• Ignoring that Scan reads all items first

4. You wrote this Scan code but it returns all items without filtering:

Scan(TableName='Orders', FilterExpression='Status = :s')

What is the most likely error?

medium

A. Missing ExpressionAttributeValues for ':s'.

B. Using FilterExpression instead of KeyConditionExpression.

C. Scan does not support FilterExpression.

D. Table name is incorrect.

Solution

1. Step 1: Check FilterExpression usage

   FilterExpression uses placeholders like ':s' which must be defined in ExpressionAttributeValues.

2. Step 2: Identify missing ExpressionAttributeValues

   Code lacks ExpressionAttributeValues, so filter cannot apply and returns all items.

3. Final Answer:

   Missing ExpressionAttributeValues for ':s'. -> Option A

4. Quick Check:

   Filter needs ExpressionAttributeValues [OK]

Hint: Always provide ExpressionAttributeValues for FilterExpression [OK]

Common Mistakes:

• Confusing FilterExpression with KeyConditionExpression
• Forgetting ExpressionAttributeValues
• Assuming Scan ignores missing values silently

5. You have a large DynamoDB table with millions of items but no suitable key for your query. You need to find all items where Status = 'Active'. Which approach is best?

hard

A. Use Scan with FilterExpression 'Status = :status' and paginate results.

B. Create a Global Secondary Index on 'Status' and use Query.

C. Use Query with KeyConditionExpression on 'Status'.

D. Use Scan without any filter to get all items.

Solution

1. Step 1: Understand limitations of Scan on large tables

   Scan reads all items and is slow and costly on large tables, even with filters.

2. Step 2: Use Global Secondary Index (GSI) for efficient queries

   Creating a GSI on 'Status' allows Query operation, which is fast and efficient for filtering by 'Status'.

3. Final Answer:

   Create a Global Secondary Index on 'Status' and use Query. -> Option B

4. Quick Check:

   GSI + Query = efficient large table filter [OK]

Hint: Use GSI for filtering large tables, not Scan [OK]

Common Mistakes:

• Using Scan on large tables causing slow performance
• Trying Query without suitable key or index
• Ignoring cost and speed implications