AWS SDK for JavaScript/Node.js in DynamoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

What is the output of this DynamoDB query code?

Consider this AWS SDK for JavaScript code querying a DynamoDB table named Users for items where age is greater than 25.

const { DynamoDBClient, QueryCommand } = require("@aws-sdk/client-dynamodb");
const client = new DynamoDBClient({ region: "us-east-1" });

const params = {
  TableName: "Users",
  KeyConditionExpression: "#age > :ageVal",
  ExpressionAttributeNames: { "#age": "age" },
  ExpressionAttributeValues: { ":ageVal": { N: "25" } }
};

async function run() {
  try {
    const data = await client.send(new QueryCommand(params));
    console.log(data.Items.length);
  } catch (err) {
    console.error(err);
  }
}

run();

What will happen when this code runs?

DynamoDB

const { DynamoDBClient, QueryCommand } = require("@aws-sdk/client-dynamodb");
const client = new DynamoDBClient({ region: "us-east-1" });

const params = {
  TableName: "Users",
  KeyConditionExpression: "#age > :ageVal",
  ExpressionAttributeNames: { "#age": "age" },
  ExpressionAttributeValues: { ":ageVal": { N: "25" } }
};

async function run() {
  try {
    const data = await client.send(new QueryCommand(params));
    console.log(data.Items.length);
  } catch (err) {
    console.error(err);
  }
}

run();

A The code throws a ValidationException because KeyConditionExpression must use the partition key, not a non-key attribute.

B The code returns all items where age > 25 and prints the count of those items.

C The code throws a SyntaxError due to invalid ExpressionAttributeNames syntax.

D The code returns zero items because the filter condition is incorrect.

💡 Hint

Remember that Query requires the partition key in the KeyConditionExpression.

In DynamoDB, Query operations require the partition key to be specified in the KeyConditionExpression. Using a non-key attribute like age causes a ValidationException. To filter by non-key attributes, you must use a Scan with a filter expression or design your table with appropriate keys.

❓ Configuration

intermediate

Which option correctly configures AWS SDK client with credentials from environment variables?

You want to create a DynamoDB client in Node.js using AWS SDK v3 that automatically uses credentials set in environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. Which code snippet correctly achieves this?

A const client = new DynamoDBClient({ region: "us-east-1" });

B const client = new DynamoDBClient({ region: "us-east-1", credentials: { accessKeyId: process.env.AWS_ACCESS_KEY_ID, secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY } });

C const client = new DynamoDBClient({ region: "us-east-1", credentials: fromEnv() });

D const client = new DynamoDBClient({ region: "us-east-1", accessKeyId: process.env.AWS_ACCESS_KEY_ID, secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY });

💡 Hint

The AWS SDK v3 automatically loads credentials from environment variables if no explicit credentials are provided.

By default, the AWS SDK for JavaScript v3 uses the default credential provider chain, which includes environment variables. Explicitly passing credentials is optional unless you want to override them. Option A relies on this default behavior and is the simplest correct approach.

❓ Architecture

advanced

Which architecture best supports high availability for a DynamoDB-based web app using AWS SDK for JavaScript?

You are designing a web application that uses DynamoDB as its database. You want to ensure high availability and low latency globally. Which architecture choice below best supports this goal?

A Deploy the web app in a single region and use DynamoDB Streams to replicate data manually to other regions.

B Deploy the web app in a single AWS region and use DynamoDB Global Tables to replicate data to other regions.

C Deploy the web app in multiple regions but use a single DynamoDB table in one region accessed remotely.

D Deploy the web app in multiple AWS regions with separate DynamoDB tables in each region without replication.

💡 Hint

Think about automatic multi-region replication and data consistency.

DynamoDB Global Tables provide fully managed multi-region, multi-master replication. This allows your app to read and write data locally in multiple regions, improving availability and latency. Other options either lack replication or cause latency by accessing remote tables.

❓ security

advanced

Which IAM policy least privileges DynamoDB access for a Lambda function to only read items from a specific table?

You want to create an IAM policy for a Lambda function that only allows reading items from the DynamoDB table Orders. Which policy below follows the principle of least privilege?

A { "Effect": "Allow", "Action": ["dynamodb:GetItem", "dynamodb:Query", "dynamodb:Scan"], "Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/*" }

B { "Effect": "Allow", "Action": "dynamodb:*", "Resource": "*" }

C { "Effect": "Allow", "Action": ["dynamodb:GetItem", "dynamodb:Query"], "Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/Orders" }

D { "Effect": "Allow", "Action": ["dynamodb:GetItem", "dynamodb:Query", "dynamodb:Scan"], "Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/Orders" }

💡 Hint

Consider which actions are needed to read items and the resource scope.

Option D grants only the read actions (GetItem, Query, Scan) on the specific table Orders. Option D is too broad, Option D excludes Scan which is a read action, and Option D allows access to all tables, not just Orders.

✅ Best Practice

expert

What is the best practice to handle DynamoDB throttling errors in AWS SDK for JavaScript?

Your Node.js app using AWS SDK for JavaScript calls DynamoDB frequently and sometimes receives ProvisionedThroughputExceededException errors. What is the best practice to handle these errors?

A Immediately retry the request in a tight loop until it succeeds.

B Catch the error and log it without retrying to avoid extra load.

C Implement exponential backoff with jitter before retrying the request.

D Increase the provisioned throughput manually each time the error occurs.

💡 Hint

Think about how to reduce retry collisions and avoid overwhelming the service.

Exponential backoff with jitter is a recommended pattern to handle throttling errors. It gradually increases wait time between retries and adds randomness to avoid retry storms. Immediate retries or manual throughput increases are not scalable or efficient.