DEA-C01 Data Store Management Practice Test 5 — 15 Questions

Question 1

Which TWO data stores are considered fully managed, serverless, and suitable for storing JSON documents?

Accepted Answer

Amazon DocumentDB (with MongoDB compatibility). Amazon DocumentDB (with MongoDB compatibility) (C) is correct because it is a fully managed, serverless-capable document database service that natively stores JSON-like BSON documents and supports MongoDB APIs, making it suitable for JSON document workloads. Amazon DynamoDB (D) is correct because it is a fully managed, serverless NoSQL key-value and document database that can store JSON documents as attribute values and scales automatically without provisioning servers. Amazon Redshift (A) is a fully managed data warehouse optimized for SQL analytics, not a serverless JSON document store. Amazon ElastiCache for Redis (B) is a managed in-memory cache/data store, not a serverless JSON document database. Amazon RDS for MySQL (E) is a managed relational database that requires provisioned instances and is not serverless or document-oriented.

Answer

Amazon Redshift

Answer

Amazon ElastiCache for Redis

Answer

Amazon RDS for MySQL

Question 2

A company is using Amazon DynamoDB for a high-traffic web application. They notice increased read latency during peak hours. Which design change would best reduce read latency without increasing cost?

Accepted Answer

Enable DynamoDB Accelerator (DAX). DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency from single-digit milliseconds to microseconds for eventually consistent reads, without requiring any changes to provisioned capacity. Since the question specifies reducing latency without increasing cost, DAX is ideal because it offloads read traffic from the underlying table, allowing you to potentially lower read capacity units (RCUs) while maintaining performance.

Answer

Increase read capacity units

Answer

Use DynamoDB global tables

Answer

Switch to strongly consistent reads

Question 3

A data engineer is designing a data lake on Amazon S3. The data includes sensitive customer information that must be encrypted at rest. Which combination of actions meets this requirement with minimal operational overhead?

Accepted Answer

Enable default encryption on the S3 bucket using SSE-S3. SSE-S3 provides server-side encryption with Amazon S3-managed keys, which encrypts data at rest with minimal operational overhead because AWS handles key management, rotation, and encryption/decryption transparently. Enabling default encryption on the S3 bucket ensures that all objects written to the bucket are automatically encrypted without requiring any client-side changes or additional code, meeting the requirement with the least administrative effort.

Answer

Encrypt objects client-side before uploading

Answer

Use an S3 bucket policy to deny writes without encryption

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Use an S3 Lifecycle policy to transition to Glacier

Question 4

A company stores financial data in Amazon RDS for MySQL. They need to retain backups for 7 years to meet compliance. Which backup strategy meets this requirement?

Accepted Answer

Use the AWS Backup service with a 7-year retention policy. AWS Backup is the correct service for long-term retention of RDS snapshots beyond the 35-day limit of automated backups. It allows you to create backup plans with retention policies up to 100 years, making it suitable for the 7-year compliance requirement. Manual snapshots can also be retained indefinitely, but AWS Backup provides centralized management and lifecycle policies.

Answer

Use read replicas to retain data

Answer

Take daily manual snapshots and delete after 7 years

Answer

Enable automated backups with a retention period of 7 years

Question 5

Which THREE considerations are important when designing a DynamoDB table for high-traffic gaming leaderboards? (Choose three.)

Accepted Answer

Enable DynamoDB Accelerator (DAX) for low-latency reads. Option B is correct because DynamoDB Accelerator (DAX) is an in-memory caching layer that delivers microsecond read latency for read-heavy leaderboard queries, reducing the load on the underlying table. Option C is correct because Time to Live (TTL) lets you define an attribute that automatically deletes expired items at no write cost, which is ideal for purging stale scores and keeping the leaderboard table lean. Option D is correct because DynamoDB Adaptive Capacity automatically isolates and redistributes throughput to hot partitions, which is essential when a few top players or popular game events create uneven access patterns. Option A is not appropriate because strongly consistent reads cost twice as many RCUs and add latency, while leaderboards generally tolerate eventual consistency. Option E is not required for the core leaderboard design, since DynamoDB Streams is an optional change-data-capture feature for triggering downstream processing rather than a fundamental capacity or latency consideration.

Answer

Use strongly consistent reads for all queries

Answer

Enable DynamoDB Streams for real-time updates

Question 6

Refer to the exhibit. An IAM policy is attached to a user. The user cannot upload objects to the S3 bucket 'example-bucket' using the AWS CLI. What is the most likely cause?

Accepted Answer

The user is not using HTTPS for API calls. The IAM policy explicitly denies all actions unless the request uses HTTPS (via the `aws:SecureTransport` condition key). Since the AWS CLI by default can use HTTP if not explicitly configured to use HTTPS, the user's upload attempt fails. The `s3:PutObject` action is allowed in the policy, but the condition block overrides that permission when the request is not made over HTTPS.

Answer

The policy does not allow s3:PutObject

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The user is not in the same AWS region

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The resource ARN does not include the bucket itself

Question 7

Refer to the exhibit. A DynamoDB table 'Orders' has a GSI 'CustomerDateIndex'. A developer tries to query the GSI for all orders of a customer between two dates. The query fails. What is the most likely reason?

Accepted Answer

The query uses a date format that is not lexicographically sortable as a string. DynamoDB's Query operation requires the sort key to be lexicographically sortable when using comparison operators like BETWEEN. If the 'order_date' attribute is stored as a non-lexicographically sortable string format (e.g., 'MM-DD-YYYY' instead of 'YYYY-MM-DD'), the BETWEEN condition will fail to return correct results or may throw an error. The GSI's sort key must be in a format that supports string comparison for range queries to work properly.

Answer

The GSI does not include 'order_id' in the key schema

Answer

The 'customer_id' attribute is not a partition key in the GSI

Answer

The 'order_date' attribute is not a sort key in the GSI

Question 8

A data engineer is designing a data lake on Amazon S3. The data is accessed frequently for the first 30 days, then rarely after that. Compliance requires that data be retained for 7 years. What is the MOST cost-effective storage strategy?

Accepted Answer

Use S3 Standard for 30 days, then transition to S3 Standard-IA, then to S3 Glacier Deep Archive after 90 days.. The most cost-effective because it matches the access pattern: S3 Standard for the first 30 days of frequent access, then S3 Standard-IA for the next 60 days (reduced storage cost with a retrieval fee), and finally S3 Glacier Deep Archive for the remaining ~6.9 years to meet the 7-year retention requirement at the lowest possible storage cost. This lifecycle policy minimizes total cost by transitioning data to progressively cheaper storage tiers as access frequency drops, while still meeting compliance.

Answer

Use S3 Intelligent-Tiering for the entire 7 years.

Answer

Store all data in S3 Standard for 7 years.

Answer

Use S3 Standard for 30 days, then S3 One Zone-IA for 7 years.

Question 9

A company runs an Amazon RDS for PostgreSQL database. To meet disaster recovery requirements, they set up a cross-Region read replica. The replica has been lagging by several minutes. Which action is MOST effective to reduce the replica lag?

Accepted Answer

Increase the instance size (memory and CPU) of the replica.. Increasing the instance size (memory and CPU) of the cross-Region read replica is the most effective action because replica lag in Amazon RDS for PostgreSQL is often caused by the replica being unable to keep up with the volume of write-ahead log (WAL) data arriving from the primary. A larger replica instance provides more compute and memory resources to apply WAL changes faster, reducing the replay lag. This directly addresses the bottleneck at the replica side without impacting the primary database.

Answer

Enable Multi-AZ on the primary database.

Answer

Increase the instance size of the primary database.

Answer

Decrease the instance size of the replica to reduce cost.

Question 10

A data engineer is migrating an on-premises Apache HBase workload to Amazon DynamoDB. The application requires strongly consistent reads and the ability to query by a composite key (partition key + sort key). Which DynamoDB table design should be used?

Accepted Answer

Create a table with a partition key and sort key, and use ConsistentRead parameter.. DynamoDB natively supports strongly consistent reads when you use the `ConsistentRead` parameter set to `true` on GetItem, Query, or Scan operations. By defining a table with a partition key and sort key, you can directly query by the composite key (partition key + sort key) with strong consistency, meeting both requirements without additional infrastructure.

Answer

Use a secondary index with strongly consistent reads.

Answer

Use a local secondary index (LSI) with the composite key.

Answer

Create a global secondary index (GSI) with the composite key.

Question 11

A company is using Amazon DynamoDB for an e-commerce application. The application experiences sudden spikes in traffic, causing throttling errors. The data engineer needs to handle the spikes cost-effectively. Which solution should be used?

Accepted Answer

Use DynamoDB auto scaling with a target utilization of 70%.. DynamoDB auto scaling with a target utilization of 70% allows the table to dynamically adjust provisioned read/write capacity based on actual traffic patterns, handling sudden spikes without manual intervention while avoiding over-provisioning. This balances performance and cost by scaling up during spikes and scaling down during low traffic, preventing throttling errors cost-effectively.

Answer

Implement DynamoDB Accelerator (DAX) to cache reads.

Answer

Switch to DynamoDB on-demand capacity mode.

Answer

Provision high read and write capacity units to handle peak traffic.

Question 12

A data engineer reviews this IAM policy attached to an S3 bucket. What is the effect of this policy?

Accepted Answer

Allows PutObject only when the object is encrypted with SSE-KMS.. The IAM policy uses a Deny effect with a StringNotEquals condition on s3:x-amz-server-side-encryption for aws:kms. This means that if the encryption header is anything other than aws:kms (i.e., not SSE-KMS), the request is denied. The net effect is that PutObject is allowed only when the object IS encrypted with SSE-KMS.

Answer

Denies PutObject when encryption is not SSE-KMS.

Answer

Denies all PutObject requests.

Answer

Allows PutObject only when the object is NOT encrypted with SSE-KMS.

Question 13

A data engineer runs this CLI command. Which query is MOST efficient against this table?

Accepted Answer

Query the CustomerIndex GSI by CustomerID and OrderDate.. The CLI command likely created a global secondary index (GSI) named CustomerIndex on CustomerID and OrderDate. Querying this GSI directly is the most efficient because it uses the index's sort key to retrieve only the relevant items without scanning the entire table, minimizing read capacity consumption.

Answer

Scan the table to find all orders for a CustomerID.

Answer

Create a local secondary index on CustomerID.

Answer

Query by OrderID and filter by OrderDate.

Question 14

A data engineer is designing a data lake on Amazon S3. The data is partitioned by year, month, day, and hour. The engineer needs to ensure that queries using Amazon Athena are cost-effective and performant. The data is written in Parquet format, and the total volume is 50 TB. Which approach minimizes query costs?

Accepted Answer

Partition the data by year, month, day, and hour. Partitioning by year, month, day, and hour allows Athena to use partition pruning, reading only the relevant S3 prefixes instead of scanning the entire 50 TB dataset. This drastically reduces the amount of data scanned per query, which directly lowers query costs (Athena charges per TB scanned). The existing Parquet format further optimizes performance through columnar storage and compression.

Answer

Use AWS Glue Data Catalog to catalog the data

Answer

Convert data to CSV format

Answer

Use S3 Intelligent-Tiering storage class

Question 15

A data engineer ran the command shown in the exhibit on the bucket 'my-data-lake'. The engineer then tries to delete an object version but receives an 'AccessDenied' error. The engineer has full S3 permissions via IAM. What is the most likely reason for the error?

Accepted Answer

MFA Delete is enabled, requiring multi-factor authentication. The command shown in the exhibit (likely `aws s3api put-bucket-versioning --bucket my-data-lake --versioning-configuration Status=Enabled,MFADelete=Enabled`) enables both versioning and MFA Delete on the bucket. When MFA Delete is enabled, any operation that permanently deletes an object version or changes the versioning state requires the request to include a multi-factor authentication token. Even though the engineer has full S3 permissions via IAM, the missing MFA token causes the 'AccessDenied' error. This is a bucket-level setting that overrides IAM permissions for these specific operations.

Answer

Versioning is suspended on the bucket

Answer

The bucket policy denies s3:DeleteObjectVersion

Answer

An S3 Object Lock retention policy is in effect