AWS Console and DynamoDB setup - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

DynamoDB Table Read Capacity Behavior

You create a DynamoDB table with a provisioned read capacity of 5 units. What happens if your application tries to read 10 strongly consistent items per second?

A The table will throttle the extra read requests, causing some reads to fail with a ProvisionedThroughputExceededException.

B DynamoDB will automatically increase the read capacity to 10 units to handle the load without errors.

C All read requests will succeed without any throttling because DynamoDB queues them internally.

D The table will switch to eventually consistent reads automatically to handle the extra load.

💡 Hint

Think about how provisioned capacity limits affect request throughput and what happens when limits are exceeded.

DynamoDB enforces the provisioned read capacity. If your application exceeds it, extra requests are throttled and may fail with a ProvisionedThroughputExceededException. DynamoDB does not auto-scale capacity unless configured, and it does not automatically switch consistency models.

❓ Architecture

intermediate

Choosing Partition Key for DynamoDB Table

You want to create a DynamoDB table to store user sessions. Each user can have multiple sessions. Which partition key design will best distribute data and avoid hot partitions?

A Use a constant string as the partition key for all items.

B Use the session ID as the partition key only.

C Use the user ID as the partition key and session ID as the sort key.

D Use the user ID concatenated with the session ID as the partition key.

💡 Hint

Consider how partition keys affect data distribution and query patterns.

Using user ID as partition key and session ID as sort key groups sessions by user and distributes data across partitions. Using session ID alone or a constant key can cause uneven data distribution or hot partitions.

❓ security

advanced

IAM Policy for DynamoDB Table Access

You want to create an IAM policy that allows a Lambda function to read items from a specific DynamoDB table named 'UserData'. Which policy statement correctly grants read-only access to this table?

A {"Effect": "Deny", "Action": ["dynamodb:GetItem"], "Resource": "arn:aws:dynamodb:*:*:table/UserData"}

B {"Effect": "Allow", "Action": ["dynamodb:PutItem"], "Resource": "arn:aws:dynamodb:*:*:table/UserData"}

C {"Effect": "Allow", "Action": ["dynamodb:*"], "Resource": "arn:aws:dynamodb:*:*:table/*"}

D {"Effect": "Allow", "Action": ["dynamodb:GetItem", "dynamodb:Query", "dynamodb:Scan"], "Resource": "arn:aws:dynamodb:*:*:table/UserData"}

💡 Hint

Focus on actions that allow reading data and the resource ARN for the specific table.

Option D grants read-only actions (GetItem, Query, Scan) on the specific table. Option D allows only writing. Option D allows all actions on all tables, which is too broad. Option D denies read access.

✅ Best Practice

advanced

DynamoDB Table Backup Strategy

Your company requires daily backups of a DynamoDB table with minimal impact on performance and cost. Which backup method is best suited for this requirement?

A Use DynamoDB point-in-time recovery (PITR) to continuously back up data and restore to any second in the last 35 days.

B Export the table data to S3 every day using a custom script.

C Enable on-demand backups manually each day during off-peak hours.

D Create a global table in another region and use it as a backup.

💡 Hint

Consider backup automation, recovery options, and cost efficiency.

PITR provides continuous backups with minimal performance impact and allows restoring to any point in the last 35 days. Manual on-demand backups require manual effort and may miss backups. Exporting data daily requires custom work and may impact performance. Global tables are for multi-region replication, not backups.

🧠 Conceptual

expert

DynamoDB Consistency Models Impact

You have a DynamoDB table with eventually consistent reads enabled. Your application requires the most up-to-date data immediately after a write. What is the impact of using eventually consistent reads in this scenario?

A DynamoDB automatically upgrades eventually consistent reads to strongly consistent when needed.

B Reads may return stale data for a short time after a write, causing potential inconsistency.

C Eventually consistent reads are slower but always accurate.

D Reads will always return the latest data immediately after a write.

💡 Hint

Think about the difference between eventually consistent and strongly consistent reads.

Eventually consistent reads may return stale data shortly after a write because replication across storage nodes is asynchronous. Strongly consistent reads guarantee the latest data but consume more capacity.