CCNA Data Store Management Questions — Page 5 of 5

301

MCQeasy

A retail company uses Amazon DynamoDB to store product catalog data. The table has a partition key of ProductID and a sort key of Category. The company needs to retrieve all products in a specific category, sorted by ProductID. Which operation should be used?

A.Create a global secondary index (GSI) with Category as the partition key and ProductID as the sort key, then query the GSI.

B.Scan the table with a filter expression on Category.

C.Query the table using the partition key and sort key condition.

D.Use the BatchGetItem operation with Category as a key.

AnswerA

A GSI allows querying on non-primary key attributes. By setting Category as the partition key and ProductID as the sort key, you can efficiently query all products in a category, sorted by ProductID. This is the optimal solution for this access pattern.

Why this answer

To efficiently retrieve all products in a specific category sorted by ProductID, a global secondary index (GSI) is needed. The GSI should use Category as the partition key and ProductID as the sort key. This allows Query operations on the GSI to return the desired items in sorted order, leveraging DynamoDB's indexing capabilities for performance and cost efficiency.

Exam trap

The trap here is assuming that a Scan with a filter or a Query on the base table can satisfy the access pattern, when in fact the base table's key schema does not support querying by Category with sorting by ProductID.

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302

Multi-Selectmedium

Which THREE storage classes in Amazon S3 are designed for infrequently accessed data with millisecond retrieval times? (Select THREE.)

Select 3 answers

A.S3 Glacier Flexible Retrieval

B.S3 One Zone-IA

C.S3 Glacier Deep Archive

D.S3 Intelligent-Tiering

E.S3 Standard-IA

AnswersB, D, E

S3 One Zone-IA stores data in a single Availability Zone, delivering millisecond retrieval while suiting infrequently accessed data. It satisfies the stem's latency constraint because retrieval remains immediate, unlike Glacier tiers requiring minutes to hours. Lower durability than Standard-IA is the trade-off, but the millisecond requirement is met.

Why this answer

S3 One Zone-IA (B) is correct because it is an infrequent-access class that stores data in a single Availability Zone while still providing millisecond retrieval latency. S3 Intelligent-Tiering (D) is correct because it automatically moves objects between access tiers based on changing access patterns, and its Frequent and Infrequent Access tiers both deliver millisecond retrieval times. S3 Standard-IA (E) is correct because it is explicitly designed for infrequently accessed data and provides the same low-latency, millisecond retrieval performance as S3 Standard.

S3 Glacier Flexible Retrieval (A) is not correct here because its retrieval options range from minutes to hours, not milliseconds, and S3 Glacier Deep Archive (C) is not correct because it is the lowest-cost archive class with retrieval times typically within 12 hours.

Exam trap

The trap here is that candidates often confuse S3 Glacier Flexible Retrieval or S3 Glacier Deep Archive as having millisecond retrieval times, but these classes are designed for archival access with retrieval times measured in minutes or hours, not milliseconds.

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303

MCQmedium

A company is using Amazon RDS for MySQL with Multi-AZ deployment. The primary DB instance experiences a hardware failure, causing automatic failover to the standby. After the failover, the application reports that the database endpoint is unreachable for about 60 seconds. What is the MOST likely cause?

A.The standby instance took longer than expected to promote to primary.

B.The standby instance was not in a synchronized state and required a manual promotion.

C.The application was using the wrong endpoint and needed to be reconfigured.

D.The DNS record for the DB instance endpoint needed to update to point to the new primary.

AnswerD

Multi-AZ failover promotes the standby and repoints the DB instance's DNS CNAME to the new primary; clients caching the old record cannot connect until the TTL expires, producing the roughly 60-second outage described. This satisfies the stem's constraint that the endpoint itself was unreachable, not merely slow.

Why this answer

After an automatic failover in Amazon RDS Multi-AZ, the DNS record for the DB instance endpoint is updated to point to the new primary. This DNS change can take up to 60 seconds to propagate, during which the application may receive 'unreachable' errors if it caches the old DNS resolution. The 60-second outage aligns with the typical TTL (Time To Live) of 30 seconds for RDS DNS records plus propagation delays.

Exam trap

The trap here is that candidates assume the standby promotion itself causes the delay, but AWS specifically designs the promotion to be fast, and the real bottleneck is DNS propagation and client caching.

How to eliminate wrong answers

Option A is wrong because the standby promotion itself is nearly instantaneous in RDS Multi-AZ; the delay is not due to promotion time but DNS propagation. Option B is wrong because RDS Multi-AZ automatically synchronizes the standby synchronously, and no manual promotion is required—the failover is fully automated. Option C is wrong because the application uses the same RDS endpoint (CNAME) before and after failover; no reconfiguration is needed.

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304

MCQeasy

A data engineer needs to store large amounts of data that is accessed infrequently but must be retrieved immediately when needed. Which Amazon S3 storage class is most cost-effective?

A.S3 Intelligent-Tiering

B.S3 One Zone-IA

C.S3 Standard-IA

D.S3 Glacier Deep Archive

AnswerC

S3 Standard-IA is designed for infrequent access with millisecond retrieval.

Why this answer

S3 Standard-IA (Infrequent Access) is the most cost-effective choice because it offers low per-GB storage costs for data accessed infrequently, while still providing millisecond retrieval latency for immediate access when needed. This matches the requirement of storing large amounts of data that is rarely accessed but must be available instantly.

Exam trap

The DEA-C01 exam often tests the misconception that S3 One Zone-IA is a cheaper alternative for infrequent access, but the trap is that it sacrifices durability by storing data in a single Availability Zone, which is not suitable for data that must be reliably retrieved immediately.

How to eliminate wrong answers

Option A is wrong because S3 Intelligent-Tiering automatically moves data between access tiers based on usage patterns, but it incurs a monthly monitoring and automation fee per object, making it less cost-effective for purely infrequent access patterns with no variable usage. Option B is wrong because S3 One Zone-IA stores data in a single Availability Zone, which risks data loss if that AZ fails, and it does not meet the implied durability requirement for data that must be retrievable immediately. Option D is wrong because S3 Glacier Deep Archive is designed for archival data with retrieval times of 12 to 48 hours, not immediate retrieval, and thus fails the 'retrieved immediately' requirement.

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305

MCQeasy

A company uses Amazon RDS for PostgreSQL to store customer data. The data engineer needs to ensure that the database can be restored to any point in time within the last 35 days. The engineer also wants to minimize the impact on the production database during backups. What should the engineer do?

A.Enable automated backups and set the backup retention period to 35 days.

B.Create a manual snapshot every day and retain them for 35 days.

C.Enable Multi-AZ deployment and rely on the standby replica for backups.

D.Configure a read replica and take snapshots from the read replica.

AnswerA

Amazon RDS automated backups enable point-in-time recovery to any second within the retention period, up to 35 days. They are taken during a daily backup window and continuously archive transaction logs to S3, with minimal impact on the production database. Setting the retention period to 35 days directly meets the requirement.

Why this answer

Amazon RDS automated backups provide point-in-time recovery to any second within the retention period, which can be set up to 35 days. They are managed by RDS, require no manual intervention, and have minimal performance impact. Manual snapshots, Multi-AZ, and read replicas do not offer the same continuous PITR capability.

Exam trap

The trap here is assuming that Multi-AZ or read replicas automatically provide point-in-time recovery, when only automated backups enable PITR within the retention period.

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306

MCQmedium

A company uses Amazon DynamoDB with global tables in three AWS Regions. The data engineer needs to ensure that writes to the table in us-east-1 are replicated to other regions with minimal latency. Which DynamoDB feature should be used?

A.DynamoDB Global Tables

B.DynamoDB Time to Live (TTL)

C.DynamoDB Streams

D.DynamoDB Accelerator (DAX)

AnswerA

DynamoDB global tables use multi-Region, active-active replication with a last-writer-wins conflict resolution, propagating writes from us-east-1 to the other Regions typically within a second. This directly satisfies the minimal-latency replication constraint, since replication is handled natively by DynamoDB rather than by custom streaming pipelines.

Why this answer

DynamoDB Global Tables is the correct feature because it provides multi-region, multi-master replication, automatically replicating writes from us-east-1 to other regions with sub-second latency. This is achieved through DynamoDB Streams and a last-writer-wins conflict resolution mechanism, ensuring data consistency across regions without requiring custom replication logic.

Exam trap

The trap here is that candidates may confuse DynamoDB Streams (a change capture mechanism) with Global Tables (a managed replication service), not realizing that Streams alone cannot replicate data across regions without additional custom code.

How to eliminate wrong answers

Option B is wrong because DynamoDB Time to Live (TTL) is used to automatically delete expired items based on a timestamp attribute, not for replicating data across regions. Option C is wrong because DynamoDB Streams captures item-level changes in a single table and can trigger AWS Lambda functions, but it does not natively replicate data to other regions; Global Tables uses Streams internally but the feature itself is not a replication solution. Option D is wrong because DynamoDB Accelerator (DAX) is an in-memory cache that reduces read latency for a single table, but it does not provide cross-region write replication.

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307

MCQhard

A company has a DynamoDB table with a partition key of 'user_id' and a sort key of 'timestamp'. They need to query all items for a user within a date range. Which query operation should be used?

A.BatchGetItem with multiple keys

B.Query with KeyConditionExpression on partition key and sort key

C.GetItem with both partition and sort key

D.Scan with FilterExpression

AnswerB

A Query operation targets a single partition key value and can filter the sort key with a range condition, so KeyConditionExpression on both keys returns exactly the user's items within the date range. Scan cannot filter on the sort key efficiently and reads the whole table.

Why this answer

The Query operation in DynamoDB is designed to retrieve items based on a specific partition key and an optional sort key condition. Since the table has a partition key of 'user_id' and a sort key of 'timestamp', using Query with a KeyConditionExpression that filters on the partition key (user_id) and a range condition on the sort key (timestamp) is the most efficient and correct approach to get all items for a user within a date range.

Exam trap

The trap here is that candidates often confuse BatchGetItem with Query, thinking BatchGetItem can handle range queries, but BatchGetItem only retrieves items by exact primary key values and cannot filter by sort key conditions.

How to eliminate wrong answers

Option A is wrong because BatchGetItem retrieves items by their primary key (partition key and sort key) but does not support range-based filtering on the sort key; it only fetches specific items by exact key values, not a range of timestamps. Option C is wrong because GetItem retrieves a single item by its full primary key (both partition key and sort key), so it cannot return multiple items or filter by a date range. Option D is wrong because Scan reads the entire table and then applies a FilterExpression, which is inefficient and costly for large tables, and it should be avoided when a more targeted Query operation can be used.

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308

MCQhard

A company runs an Amazon Redshift cluster with 10 RA3 nodes. The data warehouse stores 50 TB of data. The company notices that queries are slow and the cluster's storage utilization is high. The data engineer needs to improve query performance and reduce storage costs without changing the cluster's node count. Which action should the engineer take?

A.Use Redshift Spectrum to offload historical data to Amazon S3 and query it in place.

B.Change the distribution style of large tables to DISTSTYLE ALL.

C.Migrate the cluster to Dense Compute node types.

D.Enable concurrency scaling to handle more concurrent queries.

AnswerA

Redshift Spectrum queries external tables in Amazon S3 directly, so historical data leaves RA3 managed storage without altering node count. This lowers storage utilisation and cost while letting the cluster focus compute on hot data, addressing both stated constraints.

Why this answer

Redshift Spectrum allows you to query data directly from Amazon S3 without loading it into the cluster. By offloading historical or less-frequently accessed data to S3, you reduce the storage utilization on the RA3 nodes, which frees up managed storage and can improve query performance. This approach also lowers storage costs because S3 is cheaper than Redshift managed storage, and it does not change the node count.

Exam trap

The trap here is that candidates often confuse concurrency scaling (which improves query throughput) with storage optimization, or they assume that changing distribution styles (like DISTSTYLE ALL) will always improve performance, ignoring the storage cost impact in a high-utilization scenario.

How to eliminate wrong answers

Option B is wrong because changing large tables to DISTSTYLE ALL replicates the entire table to every node, which increases storage utilization and can worsen the high storage issue, not reduce it. Option C is wrong because migrating to Dense Compute nodes would change the node type, which violates the constraint of not changing the cluster's node count; also, Dense Compute nodes use local SSD storage and are not designed for the same storage-to-compute ratio as RA3 nodes. Option D is wrong because concurrency scaling adds additional compute capacity to handle more concurrent queries but does not reduce storage utilization or costs; it addresses throughput, not the underlying storage pressure.

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309

MCQeasy

A company uses an Amazon RDS for MySQL DB instance with Multi-AZ deployment. The primary DB instance fails unexpectedly. What happens to the database endpoint?

A.A new endpoint is created for the standby and the application must use the new endpoint.

B.The existing endpoint continues to work and automatically points to the standby DB instance.

C.The database becomes unavailable until the primary is restored from a snapshot.

D.The existing endpoint is deleted and a new endpoint is provided after manual DNS update.

AnswerB

The DNS endpoint is a stable CNAME that Multi-AZ failover repoints to the standby, which is promoted to primary. Applications therefore keep using the same endpoint without reconfiguration, satisfying the requirement that connectivity survives the unexpected primary failure.

Why this answer

In a Multi-AZ RDS deployment, the DNS endpoint remains unchanged during a failover. When the primary DB instance fails, Amazon RDS automatically updates the DNS record to point to the standby instance in the other Availability Zone. This ensures the application can continue using the same endpoint without any manual intervention, providing high availability.

Exam trap

The trap here is that candidates may think a new endpoint is created or that manual DNS changes are required, confusing Multi-AZ failover with a manual snapshot restore or a cross-region read replica promotion.

How to eliminate wrong answers

Option A is wrong because the DNS endpoint is not recreated; it remains the same and is automatically remapped to the standby instance. Option C is wrong because Multi-AZ failover is automatic and typically completes within 1-2 minutes, so the database does not become unavailable until a snapshot restore is performed. Option D is wrong because no manual DNS update is required; the existing endpoint is automatically updated by RDS to point to the standby instance.

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310

MCQmedium

Refer to the exhibit. A data engineer configured the lifecycle policy shown. The 'logs/' prefix contains important audit logs. After 365 days, what happens to the objects?

A.Objects are permanently deleted.

B.Objects are transitioned to Glacier Deep Archive.

C.Objects are transitioned to Glacier.

D.Objects are transitioned to Standard-IA.

AnswerA

The lifecycle policy's expiration action permanently deletes objects once they reach 365 days; S3 lifecycle expiration removes the current version outright rather than transitioning it. Because the logs/ prefix falls under the rule, those audit logs are irrecoverably deleted unless versioning or Object Lock preserves them.

Why this answer

The lifecycle policy shown has a single rule that expires objects in the 'logs/' prefix after 365 days. In Amazon S3, an 'expiration' action permanently deletes the objects once the specified number of days has passed since object creation. There is no transition action configured, so objects are not moved to any storage class; they are simply deleted.

Exam trap

The DEA-C01 exam often tests the distinction between 'expiration' (permanent deletion) and 'transition' (moving to another storage class), and candidates mistakenly assume that expiration implies a transition to a cold storage class like Glacier.

How to eliminate wrong answers

Option B is wrong because the policy does not include a transition action to Glacier Deep Archive; expiration deletes objects, not transitions them. Option C is wrong because there is no transition rule to Glacier; the policy only specifies expiration. Option D is wrong because Standard-IA is a transition target, but the policy lacks any transition action and only has an expiration action.

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311

Multi-Selecteasy

Which TWO statements about Amazon Redshift data distribution are correct? (Choose two.)

Select 2 answers

A.DISTSTYLE is a distribution style option

B.AUTO distribution always chooses EVEN

C.KEY distribution places rows with the same distribution key on the same slice

D.EVEN distribution distributes rows across slices evenly

E.ALL distribution distributes data across all slices

AnswersC, D

KEY distribution colocates data by key.

Why this answer

In Amazon Redshift, KEY distribution places all rows with the same distribution key value on the same slice (compute node segment). This ensures that join operations on the distribution key are collocated, reducing data movement across the network and improving query performance.

Exam trap

The trap here is confusing distribution styles with distribution options (e.g., DISTSTYLE is a parameter, not a style) and misunderstanding that ALL distribution replicates the entire table to every node, not slices, while AUTO dynamically selects the best style rather than defaulting to EVEN.

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312

MCQmedium

A company stores application logs in Amazon S3 in JSON format. The logs are partitioned by year/month/day. A data engineer needs to create a table in the AWS Glue Data Catalog so that Amazon Athena can query the logs efficiently. The engineer wants to minimize query costs and ensure that new partitions are automatically recognized. Which combination of actions should the engineer take?

A.Create a table with partitions and configure an AWS Glue crawler to run on a schedule to discover new partitions.

B.Create a table with partitions defined manually and run ALTER TABLE ADD PARTITION for each new day.

C.Create a table with partition projection enabled for year/month/day and set the storage location to the S3 prefix.

D.Create a table without partitions and rely on Athena to scan the entire S3 prefix.

AnswerC

Partition projection in Athena allows the table to automatically infer partitions based on a defined pattern, eliminating the need to manually add partitions or run crawlers. By specifying year/month/day projection, Athena can generate partition locations on the fly, reducing metadata overhead and ensuring new partitions are immediately queryable. This minimizes query costs by enabling partition pruning.

Why this answer

Partition projection in Athena allows the table to compute partition locations dynamically based on a defined pattern, so new partitions are recognized immediately without manual intervention or crawlers. This reduces metadata operations and enables partition pruning, which lowers query costs. Manual partition management, non-partitioned tables, or scheduled crawlers either add overhead or fail to provide immediate partition recognition.

Exam trap

The trap here is assuming that a scheduled AWS Glue crawler is required to discover new partitions, when partition projection can eliminate that need entirely for a known partition scheme.

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313

MCQmedium

A data engineer manages an Amazon DynamoDB table used for a high-traffic gaming leaderboard. The table uses on-demand capacity mode and has a partition key of UserId (string) with no sort key. The leaderboard must retrieve the top 100 scores across all users. Currently, the engineer scans the entire table and sorts the results in application code, which takes several seconds and consumes large amounts of read capacity. What should the engineer do to improve the performance of retrieving the top scores?

A.Increase the table's read capacity by switching to provisioned mode with a high RCU value.

B.Add a local secondary index (LSI) on the Score attribute and query it with a limit of 100.

C.Enable DynamoDB Streams and use AWS Lambda to maintain a separate sorted list in Amazon ElastiCache for Redis.

D.Create a global secondary index (GSI) with a constant partition key and Score as the sort key, then query the index in descending order with a limit of 100.

AnswerD

A GSI with a constant partition key (e.g., 'leaderboard') and Score as sort key groups all items under one partition, allowing a Query with ScanIndexForward=false and Limit=100 to efficiently return the top scores. This avoids full table scans and leverages DynamoDB's sorted index for fast, low-cost retrieval.

Why this answer

The leaderboard requires a global, sorted view of scores. A GSI with a constant partition key and Score as sort key allows efficient Query operations that return the highest scores first using ScanIndexForward=false and a Limit. This design avoids full table scans and scales well.

Exam trap

The trap here is assuming that increasing read capacity or adding a local secondary index can solve a global sorting problem, when the core issue is the access pattern requiring a GSI with an appropriate sort key.

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314

MCQhard

A company uses Amazon DynamoDB for a gaming leaderboard. The table has a partition key of 'GameId' and a sort key of 'Score'. The application needs to query the top 10 scores for a given game. Which DynamoDB feature should be used for optimal performance?

A.Use a Query operation on the base table with ScanIndexForward set to false.

B.Enable DynamoDB Streams and use a Lambda function to compute the leaderboard.

C.Use DynamoDB Accelerator (DAX) to cache the results of a Scan operation.

D.Create a Global Secondary Index with the same partition key and sort key, then query with ScanIndexForward false.

AnswerA

A Query on the base table with ScanIndexForward false directly retrieves items in descending order by Score because the base table already has Score as sort key. This is the most efficient and cost-effective method.

Why this answer

The base table already has 'GameId' as partition key and 'Score' as sort key. A Query operation on the base table with ScanIndexForward set to false retrieves items in descending order by Score, so the first 10 items are the top scores. This is efficient without additional cost or complexity.

Option D is unnecessary and incurs extra storage cost.

Exam trap

Candidates often mistakenly think they need a GSI when the base table already has the optimal sort key. The trap is to assume that queries on the base table are inefficient, but DynamoDB can efficiently query with the sort key condition and ScanIndexForward.

How to eliminate wrong answers

Option A is wrong because a Query operation on the base table with `ScanIndexForward` set to false would require the sort key to be 'Score' (which it is), but the base table's sort key is 'Score' and the partition key is 'GameId', so a Query on the base table would work for retrieving top scores; however, the question asks for 'optimal performance' and the base table may have other attributes or be subject to throttling, but more critically, the base table's sort key is 'Score', so a Query with `ScanIndexForward` false is actually valid and efficient—this option is not incorrect in isolation, but the exam expects a GSI because the base table might have a different sort key or the question implies the need for a separate index for read-heavy workloads; however, the provided answer key marks D as correct, so A is considered wrong because the base table's sort key is 'Score', but the question's scenario likely intends that the base table has a different sort key or that a GSI is needed for optimal performance with large datasets, but technically A would work—this is a common exam trap where candidates overlook that the base table's sort key is already 'Score', making A a valid but less optimal choice due to potential hot partition issues or the need to isolate leaderboard reads. Option B is wrong because DynamoDB Streams with Lambda is an event-driven pattern for real-time processing, not for efficiently querying the top 10 scores on demand; it adds latency and complexity without improving query performance. Option C is wrong because DAX caches query results to reduce latency, but it does not eliminate the need for an efficient query pattern—using a Scan operation (even cached) is inherently inefficient for retrieving top scores, as it reads all items in the table rather than using an index to seek the highest values.

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315

MCQmedium

A company uses Amazon RDS for MySQL with Multi-AZ deployment. The primary instance fails, and automatic failover occurs. After failover, the application experiences higher latency. What is the most likely cause?

A.The read replica is now the primary and cannot handle write traffic.

B.The failover process disabled automatic backups.

C.The DNS endpoint did not update to point to the new primary.

D.The new primary instance is in a different Availability Zone, increasing network latency.

AnswerD

Cross-AZ latency can be higher than same-AZ.

Why this answer

After a Multi-AZ failover in Amazon RDS for MySQL, the new primary instance is launched in a different Availability Zone (AZ) than the original primary. If the application's compute resources (e.g., EC2 instances) remain in the original AZ, cross-AZ network traffic incurs additional latency due to the physical distance and the need to traverse the AZ boundary, which typically adds 1–2 ms of round-trip time. This increased network latency directly impacts application performance, especially for latency-sensitive queries.

Exam trap

The trap here is that candidates often assume the DNS endpoint fails to update (Option C) or that the standby cannot handle writes (Option A), but AWS explicitly ensures both are handled correctly, and the real issue is the unavoidable cross-AZ network latency introduced by the new primary's location.

How to eliminate wrong answers

Option A is wrong because in a Multi-AZ deployment, there is no read replica; the standby instance is a synchronous replica that is promoted to primary during failover, and it is fully capable of handling write traffic. Option B is wrong because the failover process does not disable automatic backups; automated backups continue to run on the new primary instance based on the same backup window and retention policy. Option C is wrong because the DNS endpoint (CNAME) for the RDS instance automatically updates to point to the new primary within 60–120 seconds after failover, so the application's connection string remains valid without manual intervention.

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316

MCQeasy

A company needs to store relational data that requires complex joins and transactional consistency. The workload is predictable and the data size is less than 500 GB. Which AWS service is MOST cost-effective for this use case?

A.Amazon Redshift

B.Amazon S3

C.Amazon RDS for PostgreSQL

D.Amazon DynamoDB

AnswerC

Amazon RDS for PostgreSQL provides full relational joins and ACID transactional consistency, which the workload demands. For predictable traffic under 500 GB, a single provisioned instance avoids the cost of distributed engines such as Aurora or DynamoDB, making it most cost-effective.

Why this answer

Amazon RDS for PostgreSQL is the most cost-effective choice because it provides a fully managed relational database service that supports complex joins and transactional consistency (ACID compliance) for predictable workloads under 500 GB. Unlike Redshift, which is optimized for petabyte-scale analytics, RDS offers lower cost for this data size and workload pattern, while DynamoDB lacks native SQL join capabilities and S3 is not a relational database.

Exam trap

The trap here is that candidates often choose Amazon Redshift for any data that involves joins or analytics, ignoring that it is cost-prohibitive and architecturally mismatched for transactional, sub-500 GB workloads, while RDS is the correct relational database service for this scale.

How to eliminate wrong answers

Option A is wrong because Amazon Redshift is a columnar data warehouse designed for large-scale analytical queries (petabytes), not for transactional workloads requiring complex joins and ACID compliance; it is over-provisioned and cost-inefficient for sub-500 GB data. Option B is wrong because Amazon S3 is an object store that does not support relational queries, joins, or transactional consistency natively; it lacks a SQL engine and ACID guarantees. Option D is wrong because Amazon DynamoDB is a NoSQL key-value and document database that does not support complex joins or relational operations; it is optimized for high-throughput, low-latency access patterns, not transactional consistency across multiple tables.

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317

MCQeasy

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The data engineer needs to ensure minimal downtime during migration. Which AWS service should be used to facilitate the migration?

A.AWS Glue

B.AWS Snowball

C.AWS Database Migration Service (DMS)

D.Amazon S3 Transfer Acceleration

AnswerC

AWS Database Migration Service replicates ongoing changes from the on-premises MongoDB source to Amazon DocumentDB using change data capture, enabling near-zero-downtime cutover. Continuous replication keeps the target synchronised until you switch applications over, directly satisfying the stem's minimal-downtime constraint.

Why this answer

AWS Database Migration Service (DMS) supports continuous replication from MongoDB to Amazon DocumentDB using change data capture (CDC), enabling near-zero downtime migration. DMS can perform a full load followed by ongoing replication to keep the target synchronized until cutover.

Exam trap

The trap here is that candidates may confuse AWS Glue's ETL capabilities with database migration, overlooking that DMS is the only service purpose-built for live database migrations with minimal downtime and CDC support.

How to eliminate wrong answers

Option A is wrong because AWS Glue is a serverless data integration service for ETL (Extract, Transform, Load) jobs, not designed for live database migration with minimal downtime. Option B is wrong because AWS Snowball is a physical data transfer device for moving large volumes of data offline, which introduces significant downtime and is unsuitable for a live migration requiring minimal interruption. Option D is wrong because Amazon S3 Transfer Acceleration is a feature that speeds up uploads to S3 over the internet, not a database migration tool and cannot handle schema conversion or ongoing replication.

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318

MCQeasy

A data engineer is designing a data lake on Amazon S3. The data lake will store raw data, transformed data, and curated datasets. The engineer needs to ensure that raw data is immutable (never overwritten or deleted) and that only authorized users can access the transformed data. Which combination of S3 features should the engineer use?

A.Use S3 Lifecycle policies to archive raw data to S3 Glacier and set bucket policies for transformed data.

B.Enable S3 Versioning and use S3 Access Points for each prefix.

C.Enable default encryption with SSE-KMS and use S3 bucket policies to restrict access.

D.Enable S3 Object Lock in compliance mode on the raw data prefix and use bucket policies to restrict access to transformed data prefix.

AnswerD

S3 Object Lock in compliance mode enforces WORM protection, preventing any user, including the root account, from overwriting or deleting objects for the retention period — satisfying the immutability constraint on raw data. Bucket policies then restrict access to the transformed prefix, meeting the authorisation requirement.

Why this answer

S3 Object Lock in compliance mode enforces a write-once-read-many (WORM) model, preventing any user—including the root user—from overwriting or deleting raw data. Bucket policies then provide granular access control to restrict the transformed data prefix to authorized users only, meeting both immutability and access control requirements.

Exam trap

The DEA-C01 exam often tests the distinction between versioning (which preserves history but allows overwrites) and Object Lock (which enforces immutability), leading candidates to choose versioning when immutability is explicitly required.

How to eliminate wrong answers

Option A is wrong because S3 Lifecycle policies only automate data transitions and deletions; they do not prevent overwrites or deletions, so raw data would not be immutable. Option B is wrong because S3 Versioning preserves previous versions but does not prevent deletion or overwrite of the current version; users can still delete or overwrite objects, and Access Points alone do not enforce immutability. Option C is wrong because SSE-KMS provides encryption at rest but does not prevent data from being overwritten or deleted; bucket policies control access but do not enforce immutability.

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319

MCQhard

A data engineer is designing a data warehouse on Amazon Redshift. The workload includes many ad-hoc queries that filter on a high-cardinality column, such as customer_id, and join large dimension tables. The engineer wants to improve query performance by choosing an appropriate distribution style and sort key. Which combination should the engineer use?

A.Use KEY distribution on customer_id and set the sort key to customer_id.

B.Use AUTO distribution and set the sort key to the date column with a compound sort key.

C.Use EVEN distribution and set the sort key to the date column.

D.Use ALL distribution on the fact table and set the sort key to the join key of the largest dimension.

AnswerA

KEY distribution on customer_id colocates matching rows on the same slice, minimizing data movement during joins on that column. Setting the sort key to customer_id also enables efficient range-restricted scans and merge joins on that column. This directly addresses the high-cardinality filter and join performance, making it the best choice for this workload.

Why this answer

For a workload that frequently filters and joins on a high-cardinality column like customer_id, distributing the fact table by that key colocates matching rows and minimizes network traffic during joins. Using the same column as the sort key further speeds up range scans and merge joins. Together, they reduce data movement and I/O, improving ad-hoc query performance.

Exam trap

The trap here is assuming that sorting by date or using AUTO distribution will optimize all queries, when the key is to align distribution and sort keys with the most common join and filter columns.

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320

Multi-Selectmedium

A data engineer is configuring an Amazon Redshift cluster for a workload that runs large nightly ELT jobs loading data from Amazon S3 and then executes complex analytical queries. The team wants to improve query performance and reduce the time the cluster spends on data loading. Which TWO configuration choices should the engineer make? (Choose two.)

Select 2 answers

A.Use the COPY command with a manifest file and parallelism to load data from multiple S3 objects.

B.Enable automatic vacuum and analyze only during the nightly load window.

C.Define distribution keys on the largest fact tables to co-locate join rows on the same slice.

D.Load data using single-row INSERT statements in a loop from the application.

E.Set the cluster's distribution style to ALL for every table to avoid joins.

AnswersA, C

COPY is the native, massively parallel load path in Redshift and automatically splits work across slices when multiple files or a manifest are provided. Using a manifest ensures a consistent, complete set of files is loaded, and parallelism exploits the cluster's MPP architecture to shorten load time. This directly addresses the goal of reducing time spent loading data.

Why this answer

Bulk loading with COPY and a manifest, combined with parallelism, uses Redshift's MPP architecture to shorten load time. Choosing an appropriate distribution key on large fact tables reduces inter-slice data movement during complex joins, improving query performance. Row-by-row inserts, restricting maintenance to the load window, and applying ALL distribution to every table all degrade performance or increase load time.

Exam trap

The trap here is treating maintenance commands like VACUUM and broad distribution styles like ALL as universal performance fixes, when they can lengthen loads and inflate storage.

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321

MCQeasy

A data engineer is setting up an Amazon DynamoDB table to store user session data. The table must handle sudden spikes in read traffic during peak hours, and the engineer wants to minimize operational overhead while ensuring consistent performance. The table's read capacity mode should automatically adjust to traffic changes. Which capacity mode should the engineer choose?

A.On-demand capacity mode.

B.Provisioned capacity mode with global secondary indexes.

C.Provisioned capacity mode with auto scaling enabled.

D.Provisioned capacity mode with reserved capacity.

AnswerA

On-demand capacity mode automatically scales read and write capacity to handle traffic changes without requiring any capacity planning. It instantly accommodates sudden spikes in traffic and charges per request, making it ideal for unpredictable workloads with minimal operational overhead. This directly meets the requirement for automatic adjustment and consistent performance.

Why this answer

On-demand capacity mode is designed for workloads with unpredictable traffic patterns. It automatically scales to handle sudden spikes in read and write traffic, eliminating the need for capacity planning and reducing operational overhead. This makes it the best choice for session data with variable demand.

Exam trap

The trap here is assuming that provisioned mode with auto scaling is equivalent to on-demand; auto scaling has a delay and requires configuration, while on-demand provides immediate, automatic scaling.

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322

Multi-Selectmedium

Which TWO statements are true about Amazon Redshift distribution styles? (Choose TWO.)

Select 2 answers

A.KEY distribution is always the best choice to minimize data skew.

B.AUTO distribution always selects EVEN distribution.

C.ALL distribution copies the entire table to every node.

D.Redshift automatically assigns a ROUND ROBIN distribution style by default.

E.EVEN distribution distributes rows across slices in a round-robin fashion.

AnswersC, E

ALL distribution is useful for small tables that are frequently joined.

Why this answer

The ALL distribution style in Amazon Redshift copies the entire table to every node in the cluster. This is ideal for small, slowly changing dimension tables (like date or location tables) that need to be joined with large fact tables, as it eliminates the need to redistribute data across nodes during query execution.

Exam trap

The trap here is that candidates often confuse AUTO distribution with ROUND ROBIN, or assume that KEY distribution is always optimal, when in fact poor key selection can lead to severe data skew and performance degradation.

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323

MCQmedium

A data engineer is configuring an AWS Glue ETL job to read from an Amazon S3 bucket that contains Apache Parquet files partitioned by year, month, and day. The engineer wants the job to only process data for the year 2023 and month 10, and to minimize the amount of data scanned. The Glue job uses the Glue Data Catalog table `sales_data` with the correct partition structure. What is the MOST efficient way to configure the job to read only the required partitions?

A.Read the entire `sales_data` table into a DynamicFrame and then apply a `Filter` transform with the condition `year=='2023' and month=='10'`.

B.Use the Glue `create_dynamic_frame.from_options` with the S3 path `s3://bucket/sales_data/year=2023/month=10/` and format `parquet`.

C.Use the Glue `create_dynamic_frame.from_catalog` with `push_down_predicate` set to `year=='2023' and month=='10'`.

D.Create a new Glue Data Catalog table that points only to the `year=2023/month=10` prefix, and then read from that table.

AnswerC

Using `push_down_predicate` with `create_dynamic_frame.from_catalog` allows Glue to filter partitions at the catalog level, so only the specified partitions are read from S3. This reduces data scanned and improves performance. The predicate syntax uses SQL-like expressions on partition columns, and Glue leverages the partition metadata to avoid listing and reading unnecessary partitions. This is the recommended approach for partitioned data in Glue ETL jobs.

Why this answer

The correct approach is to use `push_down_predicate` with `create_dynamic_frame.from_catalog`. This pushes the filter down to the Glue Data Catalog, so only the relevant partitions are read from Amazon S3. It minimizes data scanned, reduces cost, and improves job performance.

Other methods either read all data first or bypass the catalog, which is less efficient and harder to maintain.

Exam trap

The trap here is assuming that filtering after reading the data is equivalent to partition pruning, but it still scans all data.

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324

MCQeasy

A data engineer needs to store JSON documents that are frequently updated and require ACID transactions. Which AWS database service is most appropriate?

A.Amazon Neptune

B.Amazon DocumentDB

C.Amazon DynamoDB

D.Amazon S3

AnswerB

DocumentDB is for MongoDB workloads, ACID transactions are not fully supported.

Why this answer

Amazon DocumentDB (with MongoDB compatibility) is the correct choice because it is a fully managed document database designed to store, query, and index JSON-like documents. It supports multi-document ACID transactions, providing atomicity, consistency, isolation, and durability for frequently updated JSON documents. DocumentDB also offers a MongoDB-compatible API, making it a natural fit for JSON document workloads.

While Amazon DynamoDB supports ACID transactions via TransactWriteItems and TransactGetItems and can store JSON-like items, it is primarily a key-value and document store with a different data model and is not the AWS service purpose-built for general-purpose JSON document storage with complex querying and multi-document transactions. Amazon Neptune is a graph database, and Amazon S3 is object storage without ACID transactional semantics, so neither is appropriate.

Exam trap

Candidates may choose DynamoDB because it supports ACID transactions and can store JSON-like items. However, DynamoDB is a key-value/document store optimized for single-digit millisecond performance at scale, not for general-purpose JSON document storage with rich querying and multi-document transactions. DocumentDB is the AWS service specifically designed for JSON document workloads with ACID transaction support.

How to eliminate wrong answers

Option A is wrong because Amazon Neptune is a graph database designed for highly connected data (e.g., social networks, recommendation engines) and does not support ACID transactions across multiple documents in the same way DynamoDB does; it uses a property graph model and SPARQL/Gremlin, not a document store. Option B is wrong because Amazon DocumentDB is a MongoDB-compatible document database that supports ACID transactions only at the document level (single-document atomicity), not multi-document transactions, and its JSON handling is optimized for MongoDB workloads, not the high-frequency updates with full ACID guarantees required here. Option D is wrong because Amazon S3 is an object storage service that does not support ACID transactions; it offers eventual consistency for overwrite PUTS and lacks atomic multi-key operations, making it unsuitable for frequently updated JSON documents requiring transactional integrity.

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325

MCQmedium

A company is migrating an on-premises MySQL database to Amazon RDS for MySQL. The database is 500 GB and has a 24/7 uptime requirement. The migration must minimize downtime. Which approach should be used?

A.Take a snapshot of the on-premises database, convert it to a volume, and restore to RDS.

B.Use AWS Database Migration Service (DMS) with ongoing replication to migrate the data.

C.Export the database using mysqldump and import it into RDS using mysql command.

D.Create an RDS MySQL read replica from the on-premises database using native replication.

AnswerB

AWS DMS with ongoing replication performs a full load then continuously applies change data capture from the source MySQL binlog, keeping the target synchronised so cutover downtime is limited to a brief switchover rather than the whole 500 GB transfer.

Why this answer

AWS DMS with ongoing replication (change data capture) allows you to perform a full load of the 500 GB database and then continuously replicate changes from the on-premises MySQL source to the Amazon RDS target. This minimizes downtime because you can cut over to RDS in seconds after the target is synchronized, rather than taking the source offline for an extended period.

Exam trap

The trap here is that candidates often choose mysqldump (Option C) because it is a familiar tool, but they overlook the requirement for minimal downtime and the fact that a 500 GB dump/import would take hours, violating the 24/7 uptime requirement.

How to eliminate wrong answers

Option A is wrong because taking a snapshot of an on-premises database and converting it to a volume is not a supported method for migrating to RDS; snapshots are native to AWS block storage and cannot be directly created from an on-premises database. Option C is wrong because using mysqldump and mysql import requires the source database to be read-locked or offline during the export/import process, causing significant downtime for a 500 GB database with a 24/7 uptime requirement. Option D is wrong because RDS cannot be configured as a read replica of an on-premises MySQL database using native replication; native MySQL replication requires the replica to have direct network access to the source, and RDS does not support being a replica of an external source—only the reverse (RDS as source to external replica) is possible.

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326

MCQmedium

A data engineer applies the following IAM policy to an IAM user: ```json { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "s3:GetObject", "Resource": "arn:aws:s3:::example-bucket/*", "Condition": { "StringEquals": { "s3:x-amz-server-side-encryption": "AES256" } } } ] } ``` The user attempts to download an object from the bucket 'example-bucket' that is encrypted with SSE-S3 (AES256). Will the request succeed?

A.Yes, but only if the user also has s3:ListBucket permission.

B.No, because the policy requires the encryption to be specified in the request.

C.Yes, because the object is encrypted with SSE-S3 which uses AES256.

D.No, because the policy does not allow the s3:GetObject action for encrypted objects.

AnswerB

The condition key `s3:x-amz-server-side-encryption` evaluates the request header, not the object's stored encryption state. SSE-S3 encrypts objects automatically without requiring that header, so a plain GetObject request carries no matching key and the condition fails. The download is denied despite the object being AES256-encrypted.

Why this answer

The IAM policy includes a condition that requires the request to include the `x-amz-server-side-encryption` header with value `AES256`. Even though the object is encrypted with SSE-S3, the policy condition evaluates the request headers, not the object's encryption state. Since the user does not specify the encryption header in the download request, the condition fails, and the request is denied.

Exam trap

The trap is that candidates assume SSE-S3 is transparent and always allows access, overlooking that the IAM policy condition explicitly requires the encryption header in the request. The condition applies to the request, not the object's encryption-at-rest.

How to eliminate wrong answers

Option A is wrong because s3:ListBucket permission is not required to download an object; s3:GetObject alone suffices, and the policy does not reference ListBucket. Option B is wrong because the policy requires encryption to be specified in the request only for objects encrypted with SSE-KMS or SSE-C, not for SSE-S3 objects, which are automatically handled by S3 without client-side encryption headers. Option D is wrong because the policy does allow s3:GetAction for encrypted objects; the condition only denies requests that fail to include encryption headers, and SSE-S3 objects do not require such headers.

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327

MCQeasy

A company stores sensitive data in Amazon S3 and needs to ensure that data is encrypted at rest. The security team requires that the company manage its own encryption keys and have the ability to audit key usage. Which S3 encryption option should the data engineer choose?

A.Client-side encryption with an AWS KMS key

B.Server-side encryption with customer-provided keys (SSE-C)

C.Server-side encryption with Amazon S3 managed keys (SSE-S3)

D.Server-side encryption with AWS KMS keys (SSE-KMS)

AnswerD

SSE-KMS allows the company to use AWS KMS customer managed keys, giving them control over key management and the ability to audit key usage via AWS CloudTrail. This meets the requirements for self-managed keys and auditability. It also supports key rotation and granular access policies, making it the correct choice for sensitive data with strict security requirements.

Why this answer

SSE-KMS allows the use of AWS KMS customer managed keys, providing control over key management and auditability through CloudTrail. This satisfies the requirements for self-managed keys and auditing key usage. Other options either do not provide key control or lack auditability, making SSE-KMS the correct choice.

Exam trap

The trap here is confusing SSE-C with SSE-KMS; SSE-C lets you provide keys but does not offer built-in auditability of key usage.

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328

MCQeasy

A company needs to store JSON documents that are frequently read and written by a web application. The data must be highly available and durable across multiple Availability Zones. Which AWS database service meets these requirements?

A.Amazon RDS for PostgreSQL

B.Amazon S3

C.Amazon DynamoDB

D.Amazon ElastiCache for Redis

AnswerC

DynamoDB is a fully managed NoSQL store holding JSON as native items, replicating synchronously across multiple Availability Zones for durability and high availability. Its key-value and document model suits frequent reads and writes from a web application without schema migrations.

Why this answer

Amazon DynamoDB is a fully managed NoSQL key-value and document database that provides single-digit millisecond performance at any scale. It stores JSON documents natively, supports frequent reads and writes, and offers built-in high availability and durability by automatically replicating data across multiple Availability Zones (AZs) in an AWS Region. This makes it the ideal choice for the described web application workload.

Exam trap

The trap here is that candidates often confuse Amazon S3's high durability and availability with database capabilities, overlooking that S3 is an object store with higher latency and no native query support, while DynamoDB is purpose-built for low-latency, high-throughput document storage with ACID transactions via DynamoDB Transactions.

How to eliminate wrong answers

Option A is wrong because Amazon RDS for PostgreSQL is a relational database that stores data in tables with a fixed schema, not as JSON documents natively, and while it can be deployed in a Multi-AZ configuration for high availability, it does not provide the same level of automatic, seamless scaling and native JSON document support as DynamoDB. Option B is wrong because Amazon S3 is an object storage service, not a database; it can store JSON files but is not designed for frequent, low-latency read/write operations from a web application, and it lacks features like atomic transactions and query capabilities that a database provides. Option D is wrong because Amazon ElastiCache for Redis is an in-memory cache, not a durable database; while it can store JSON documents using the RedisJSON module, data is primarily stored in memory and is not durable by default across AZs, making it unsuitable for the durability and persistence requirements of a primary data store.

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329

MCQeasy

A company runs a MySQL database on Amazon RDS. The database size is 500 GB and is experiencing high read traffic. The team wants to improve read performance with minimal operational overhead. Which action should they take?

A.Create a read replica in the same region

B.Enable Multi-AZ deployment

C.Implement Amazon ElastiCache for caching

D.Upgrade to a larger instance class

AnswerA

A read replica offloads read traffic from the primary MySQL instance via asynchronous replication, directly addressing the high read volume. It requires no application rewrite and minimal operational effort, satisfying the minimal-overhead constraint. Multi-AZ standby would not serve reads, and sharding adds substantial complexity.

Why this answer

Creating a read replica in the same region offloads read traffic from the primary RDS instance by providing a separate, read-only copy of the database. This directly addresses high read traffic with minimal operational overhead, as RDS manages the asynchronous replication using MySQL's native binlog-based replication. The replica can serve SELECT queries, reducing load on the primary instance without requiring application changes.

Exam trap

The trap here is confusing Multi-AZ (high availability) with read scaling, leading candidates to select Multi-AZ deployment thinking it improves read performance, when in fact the standby is not accessible for reads.

How to eliminate wrong answers

Option B is wrong because Multi-AZ deployment provides high availability and automatic failover, not read scaling; the standby instance cannot serve read traffic. Option C is wrong because while ElastiCache can improve read performance for cached data, it requires application-level caching logic and does not offload database read queries for all data, adding operational complexity. Option D is wrong because upgrading to a larger instance class scales vertically, which can improve performance but incurs higher cost and downtime during scaling, and does not distribute read load as efficiently as a read replica.

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330

MCQmedium

A data engineer notices that an Amazon Redshift cluster is running low on disk space. The cluster has three nodes of type dc2.large. Which action will increase the available storage capacity?

A.Increase the number of nodes in the cluster.

B.Mount an Amazon S3 bucket as a file system to store data.

C.Change the volume type to Provisioned IOPS SSD (io1) to increase capacity.

D.Enable automatic compression on the tables.

AnswerA

Adding nodes to the dc2.large cluster increases total storage because each node contributes its own local disk capacity; Redshift distributes data across all nodes. Resizing node type or other settings does not add storage as directly.

Why this answer

Amazon Redshift stores data on the local instance store volumes attached to each node in the cluster. With a dc2.large cluster, each node provides approximately 160 GB of SSD storage. Adding nodes increases the total available storage linearly because each new node contributes its local storage to the cluster.

Therefore, increasing the number of nodes is the correct way to expand disk capacity.

Exam trap

The trap here is that candidates may confuse Redshift's local storage model with EBS-backed storage, leading them to think they can change volume types or mount external storage like S3, when in fact Redshift relies solely on the aggregate of each node's local instance store for persistent data.

How to eliminate wrong answers

Option B is wrong because Amazon S3 is an object storage service and cannot be mounted as a file system directly to Redshift; Redshift can only load data from S3 via COPY commands or external tables using Redshift Spectrum, but S3 does not expand the local disk space of the cluster. Option C is wrong because Provisioned IOPS SSD (io1) is an EBS volume type used for Amazon EC2 instances, not for Redshift nodes; Redshift dc2.large nodes use local instance store SSDs, and volume type cannot be changed. Option D is wrong because enabling automatic compression on tables optimizes storage efficiency by reducing the size of data on disk, but it does not increase the total available storage capacity of the cluster; it only helps use existing space more efficiently.

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331

MCQmedium

A data engineer is designing a data lake on Amazon S3. Data is ingested from multiple sources in JSON format. The engineer needs to optimize query performance for Amazon Athena while minimizing storage costs. Which storage strategy should the engineer use?

A.Store data as CSV files in a single S3 bucket without prefixes.

B.Convert data to Parquet format and partition by date.

C.Store data as JSON files in a single prefix without partitioning.

D.Store compressed JSON files in Amazon S3 Glacier.

AnswerB

Parquet is columnar and compressed, so Athena scans and bills far less data than JSON. Partitioning by date enables partition pruning, restricting each query to relevant prefixes. Together these cut query latency and S3 storage cost.

Why this answer

Parquet is a columnar storage format that significantly reduces data scan volume in Amazon Athena, which charges per byte scanned. Partitioning by date further limits the data scanned to only relevant partitions, optimizing both query performance and cost. JSON and CSV are row-based formats that require full scans, and Glacier is unsuitable for interactive querying.

Exam trap

The trap here is that candidates assume JSON or CSV are acceptable for Athena due to their simplicity, overlooking that columnar formats like Parquet are required for cost-efficient querying in AWS's pay-per-scan model.

How to eliminate wrong answers

Option A is wrong because CSV files are row-based and lack compression, leading to higher storage costs and larger data scans in Athena, and storing them without prefixes prevents partition pruning. Option C is wrong because JSON files are also row-based and verbose, resulting in inefficient queries and higher costs, and a single prefix without partitioning forces full table scans. Option D is wrong because Amazon S3 Glacier is designed for archival storage with retrieval times of minutes to hours, making it incompatible with Athena's requirement for immediate data access.

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332

Multi-Selecteasy

Which TWO AWS services can be used to automatically back up an Amazon RDS for SQL Server DB instance? (Choose TWO.)

Select 2 answers

A.AWS Database Migration Service (DMS)

B.AWS Data Pipeline

C.Amazon RDS automated backups

D.Amazon S3

E.AWS Backup

AnswersC, E

RDS automated backups capture daily snapshots plus transaction logs for SQL Server, enabling point-in-time recovery within the retention window. This is native, automatic and requires no additional service, directly satisfying the requirement to back up the instance without manual intervention.

Why this answer

Amazon RDS automated backups (C) are a native RDS feature that automatically takes daily snapshots of the DB instance during the backup window and continuously archives transaction logs, enabling point-in-time recovery for RDS for SQL Server. AWS Backup (E) is a fully managed, policy-driven backup service that supports RDS as a protected resource, allowing you to define backup plans with schedules and retention rules that trigger automated RDS snapshots. AWS DMS (A) is a replication and migration service, not a backup mechanism, so it does not create restorable backups.

AWS Data Pipeline (B) is an orchestration service for data movement and transformation, not a backup tool for RDS. Amazon S3 (D) is object storage that can hold exported backups, but it does not itself automatically back up an RDS for SQL Server DB instance.

Exam trap

The trap here is that candidates often confuse AWS Backup with a service that only works for on-premises or EC2 backups, or mistakenly think DMS or Data Pipeline can handle automated backups, when in fact only RDS automated backups and AWS Backup provide native, automatic backup capabilities for RDS for SQL Server.

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333

MCQmedium

A data engineer is using AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by year, month, and day. The engineer needs to ensure that AWS Glue crawlers correctly identify the partitions and that Amazon Athena queries can efficiently prune partitions. Which action should the engineer take?

A.Store the data in a directory structure like s3://bucket/year=2023/month=01/day=01/, and run the crawler with the default settings to automatically detect partitions.

B.Enable AWS Glue Data Catalog encryption and set the 'classification' property to 'parquet' in the table definition.

C.Configure the crawler to use a custom classifier that recognizes the partition structure, and set the table property 'partition_filtering.enabled' to true.

D.Manually create the table in the AWS Glue Data Catalog with partition keys, and use AWS Glue ETL jobs to add partitions as new data arrives.

AnswerA

AWS Glue crawlers automatically detect Hive-style partitions when the S3 path follows the key=value format, such as year=2023/month=01/day=01. This allows the crawler to populate the AWS Glue Data Catalog with partition metadata. Athena can then use this metadata to prune partitions during queries, improving performance and reducing cost by scanning only relevant data.

Why this answer

The correct action is to store data using Hive-style partition paths (e.g., year=2023/month=01/day=01/) and run the AWS Glue crawler with default settings. The crawler will automatically detect the partitions and update the Data Catalog. Athena can then use partition pruning to optimize queries.

Other options either use unnecessary custom classifiers, manual partition management, or irrelevant settings.

Exam trap

The trap here is assuming that custom classifiers or manual partition creation are needed for standard Hive-style partitions, when the crawler handles them automatically.

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334

MCQeasy

A company is using Amazon S3 to store critical data and needs to ensure that objects are automatically transitioned to S3 Glacier Deep Archive after 180 days to reduce costs. Which S3 lifecycle action should be configured?

A.Expiration

B.Transition

C.AbortIncompleteMultipartUpload

D.NoncurrentVersionTransition

AnswerB

The Transition lifecycle action moves objects to another storage class after a specified age, so configuring it with a 180-day threshold and the Glacier Deep Archive target automates the cost reduction. Expiration would delete objects instead of archiving them.

Why this answer

The S3 lifecycle 'Transition' action is specifically designed to move objects between storage classes after a specified number of days. To reduce costs by moving objects to S3 Glacier Deep Archive after 180 days, you configure a lifecycle rule with a Transition action that targets the 'DEEP_ARCHIVE' storage class at the 180-day mark.

Exam trap

The trap here is that candidates often confuse 'Expiration' (deletion) with 'Transition' (storage class change), or incorrectly apply 'NoncurrentVersionTransition' when the question does not mention versioning or noncurrent versions.

How to eliminate wrong answers

Option A is wrong because 'Expiration' is used to permanently delete objects after a set period, not to transition them to a different storage class. Option C is wrong because 'AbortIncompleteMultipartUpload' is used to clean up incomplete multipart uploads after a specified number of days, not to transition objects between storage classes. Option D is wrong because 'NoncurrentVersionTransition' applies only to noncurrent versions of versioned objects, not to current versions, and the question does not specify versioning or noncurrent versions.

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335

MCQeasy

A company needs to store archival logs that must be retained for 10 years. The logs are accessed infrequently, but when accessed, retrieval must occur within 12 hours. Which storage class is MOST cost-effective?

A.Amazon S3 Glacier Deep Archive

B.Amazon S3 Intelligent-Tiering

C.Amazon S3 Standard

D.Amazon S3 One Zone-Infrequent Access

AnswerA

S3 Glacier Deep Archive is the lowest-cost archival class, with a standard retrieval time within 12 hours, matching the stated access requirement. It suits 10-year retention of infrequently accessed logs where cost minimisation outweighs retrieval speed.

Why this answer

Amazon S3 Glacier Deep Archive is the most cost-effective storage class for archival logs that must be retained for 10 years with infrequent access and a 12-hour retrieval window. It offers the lowest storage cost among S3 classes, with retrieval times typically within 12 hours for standard retrievals, making it ideal for long-term archival data that is rarely accessed.

Exam trap

The trap here is that candidates often confuse retrieval time with cost, assuming that any class with faster retrieval is better, but the 12-hour retrieval window explicitly allows the use of the lowest-cost archival tier, making Glacier Deep Archive the correct choice despite its slower retrieval speed.

How to eliminate wrong answers

Option B (S3 Intelligent-Tiering) is wrong because it is designed for data with unknown or changing access patterns and automatically moves objects between tiers, but it incurs monitoring and automation fees that make it less cost-effective than Glacier Deep Archive for purely archival data with a known 10-year retention. Option C (S3 Standard) is wrong because it is optimized for frequently accessed data with millisecond retrieval times and has a much higher storage cost, making it prohibitively expensive for 10 years of archival logs. Option D (S3 One Zone-Infrequent Access) is wrong because it is intended for infrequently accessed data that can be recreated if lost, but it does not provide the durability or cost savings of Glacier Deep Archive for long-term archival, and its retrieval times are faster than needed, leading to unnecessary cost.

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336

MCQmedium

A data engineer manages an Amazon S3 data lake that holds sensitive customer transaction logs. Compliance requires that all objects be encrypted at rest with keys that the company rotates every 90 days and fully controls, including the ability to immediately revoke access and audit key usage separately from other AWS accounts. The engineer must choose an encryption method that meets these requirements with minimal operational overhead. Which solution should the engineer implement?

A.Use server-side encryption with Amazon S3 managed keys (SSE-S3)

B.Use client-side encryption with a customer-provided key stored in AWS Secrets Manager

C.Use server-side encryption with AWS KMS customer managed keys (SSE-KMS)

D.Use server-side encryption with customer-provided keys (SSE-C)

AnswerC

SSE-KMS with customer managed keys gives the organization full control over the key, allows a custom rotation period (including 90 days), supports immediate revocation via key policy changes, and logs every key use in AWS CloudTrail. This directly satisfies the compliance requirements with minimal operational overhead because S3 handles encryption transparently.

Why this answer

The requirement for customer-controlled keys, custom 90-day rotation, immediate revocation, and separate key usage audit points directly to AWS KMS customer managed keys with SSE-KMS. S3 manages the encryption process, so operational overhead remains low, while the key policy and CloudTrail integration provide the necessary control and visibility. Other encryption options either lack customer control or shift too much operational responsibility to the application.

Exam trap

The trap here is assuming that any server-side encryption option provides the same level of key control and auditability, when only SSE-KMS with customer managed keys meets the specific rotation and revocation requirements.

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337

Multi-Selecthard

A data engineer is using Amazon Athena to query data stored in an S3 bucket. The queries are running slowly. Which THREE actions can improve query performance?

Select 3 answers

A.Partition the data on commonly filtered columns.

B.Convert the data to JSON format for better schema evolution.

C.Move the data to S3 Standard-IA storage class.

D.Convert the data to a columnar format such as Parquet or ORC.

E.Use compression (e.g., Snappy, Gzip) on the data files.

AnswersA, D, E

Partition pruning reduces amount of data scanned.

Why this answer

Partitioning data on commonly filtered columns (Option A) improves Athena query performance by reducing the amount of data scanned. Athena uses Hive-style partitioning (e.g., `s3://bucket/table/year=2023/month=01/`), and when a query includes a filter on the partition column, Athena prunes partitions and only reads the relevant S3 prefixes. This directly reduces I/O and query cost, as Athena charges per TB of data scanned.

Exam trap

The trap here is that candidates may think S3 storage class (Standard-IA) affects query performance, but Athena's performance is independent of storage class; the key levers are data format, partitioning, and compression.

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338

MCQeasy

A data engineer is designing a data lake on Amazon S3. Which feature should be used to manage the lifecycle of objects and move them to cheaper storage classes automatically?

A.S3 Lifecycle policies

B.S3 Object Lock

C.S3 Storage Class Analysis

D.S3 Inventory

AnswerA

S3 Lifecycle policies define rules that transition objects to cheaper storage classes such as Standard-IA, Intelligent-Tiering or Glacier, and expire them, based on age or prefixes. This automates cost optimisation across the data lake without manual intervention.

Why this answer

S3 Lifecycle policies are the correct feature for automatically managing object lifecycles and transitioning objects to cheaper storage classes (e.g., from S3 Standard to S3 Glacier Deep Archive) based on age or other rules. This directly meets the requirement to move objects to cost-optimized storage without manual intervention.

Exam trap

The trap here is confusing S3 Storage Class Analysis (which only recommends transitions) with S3 Lifecycle policies (which actually execute them), leading candidates to pick Option C thinking it automates the move.

How to eliminate wrong answers

Option B is wrong because S3 Object Lock is designed to prevent object deletion or overwrites for compliance or retention purposes, not to automate storage class transitions. Option C is wrong because S3 Storage Class Analysis provides recommendations and visibility into access patterns to help decide when to transition objects, but it does not automatically move objects—it only generates reports. Option D is wrong because S3 Inventory provides a flat-file list of objects and their metadata for auditing or sync, but it has no capability to trigger lifecycle actions.

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339

MCQmedium

Refer to the exhibit. A data engineer needs to connect to the Redshift cluster from an EC2 instance in the same VPC. The engineer can ping the EC2 instance but cannot connect to Redshift using the endpoint address and port 5439. What is the most likely cause?

A.The security group for the Redshift cluster does not allow inbound traffic on port 5439 from the EC2 instance.

B.The Redshift cluster is in a different VPC.

C.The Redshift cluster is not in an available state.

D.The Redshift cluster is publicly accessible and requires an internet gateway.

AnswerA

Reachability via ping only proves ICMP is permitted; Redshift listens on TCP 5439, so the cluster's security group must allow inbound 5439 from the EC2 instance's security group or private IP. Without that rule, the endpoint connection times out.

Why this answer

The most likely cause is that the security group associated with the Redshift cluster does not have an inbound rule allowing TCP traffic on port 5439 from the security group or IP address of the EC2 instance. Since the engineer can ping the EC2 instance (ICMP works), but cannot connect to Redshift on port 5439, this points to a firewall or security group rule blocking the specific port, not a network reachability issue.

Exam trap

AWS often tests the distinction between ICMP reachability (ping) and TCP port-level connectivity, leading candidates to overlook security group rules when they see successful ping results.

How to eliminate wrong answers

Option B is wrong because if the Redshift cluster were in a different VPC, the engineer would not be able to ping the EC2 instance from the same VPC context, and VPC peering or transit gateway would be required; the question states they are in the same VPC. Option C is wrong because if the cluster were not in an available state, the engineer would likely receive a different error (e.g., 'cluster not found' or connection timeout), and the question does not indicate any cluster status issues. Option D is wrong because the Redshift cluster is in the same VPC as the EC2 instance, so public accessibility and an internet gateway are not required; traffic stays within the VPC and uses private IPs.

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340

MCQhard

A data engineer is designing a real-time analytics pipeline that ingests clickstream data into Amazon Kinesis Data Streams. The data must be stored in Amazon S3 for later analysis with Amazon Athena. The engineer needs the data to be queryable with minimal latency and wants to avoid managing complex ETL jobs. Which solution should the engineer use?

A.Use Amazon Kinesis Data Analytics to run SQL queries on the stream and write results to Amazon S3.

B.Use Amazon Kinesis Data Firehose to deliver data to Amazon S3 with record format conversion to Parquet using an AWS Glue table.

C.Use AWS Lambda to read from Kinesis Data Streams and write JSON files to Amazon S3.

D.Use AWS Glue streaming ETL jobs to read from Kinesis Data Streams and write Parquet to Amazon S3.

AnswerB

Kinesis Data Firehose can deliver streaming data to Amazon S3 and perform record format conversion to Parquet using a schema from the AWS Glue Data Catalog. This eliminates the need for custom ETL jobs and makes the data immediately queryable by Athena with columnar performance. It provides near-real-time delivery and automatic partitioning, meeting the latency and simplicity requirements.

Why this answer

Amazon Kinesis Data Firehose can ingest streaming data and deliver it to Amazon S3 with automatic record format conversion to Parquet using an AWS Glue table. This provides near-real-time, queryable data in a columnar format without custom ETL code. Other options require writing and managing Lambda functions, Kinesis Data Analytics applications, or Glue streaming jobs, which add complexity and do not offer the same level of managed simplicity.

Exam trap

The trap here is overlooking that Kinesis Data Firehose can perform schema-based record format conversion to Parquet, which is often assumed to require a separate ETL tool.

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341

MCQeasy

A data engineer is building a data lake on Amazon S3. The engineer needs to store structured data that will be queried by Amazon Athena. The data is currently in CSV format and is partitioned by date. The engineer wants to improve query performance and reduce the amount of data scanned. Which action should the engineer take?

A.Use Amazon Redshift Spectrum to query the CSV data directly from S3.

B.Compress the CSV files using gzip and update the table metadata in the AWS Glue Data Catalog.

C.Convert the data to Apache Parquet format and use AWS Glue to update the table metadata in the AWS Glue Data Catalog.

D.Increase the number of partitions by adding a partition for each hour of the day.

AnswerC

Parquet is a columnar format that enables Athena to read only the columns needed, reducing data scanned and improving performance. Updating the AWS Glue Data Catalog ensures Athena can query the new format. This is a best practice for optimizing Athena queries on S3 data lakes.

Why this answer

Converting data to a columnar format like Parquet significantly reduces the amount of data scanned by Athena because only the required columns are read. Updating the AWS Glue Data Catalog ensures the table metadata reflects the new format. This combination improves query performance and lowers cost.

Exam trap

The trap here is assuming that compression alone will provide the same performance benefits as converting to a columnar format like Parquet.

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342

MCQeasy

A data engineer needs to store semi-structured JSON log files from multiple sources and query them using SQL. The data is rarely updated and access frequency is low. Which storage solution is MOST cost-effective?

A.Amazon Redshift with JSON ingestion and compression.

B.Amazon DynamoDB with JSON documents.

C.Amazon S3 with Amazon Athena for querying.

D.Amazon RDS for PostgreSQL with JSONB columns.

AnswerC

Amazon S3 provides low-cost durable storage for semi-structured JSON, and Athena queries it in place using SQL without loading or provisioning servers. For rarely updated, infrequently accessed logs, this serverless combination avoids the ongoing cost of a data warehouse or database.

Why this answer

Amazon S3 with Athena is the most cost-effective solution because the data is semi-structured JSON, rarely updated, and accessed infrequently. S3 provides low-cost storage for static data, and Athena uses a serverless, pay-per-query model, eliminating the need for a running cluster or provisioned capacity. This combination avoids the fixed costs of Redshift, DynamoDB, or RDS, making it ideal for low-frequency SQL querying of archival logs.

Exam trap

The trap here is that candidates often choose Redshift or RDS because they associate SQL querying with traditional databases, overlooking that Athena's serverless, pay-per-query model is far more cost-effective for infrequent access to static data stored in S3.

How to eliminate wrong answers

Option A is wrong because Amazon Redshift requires a provisioned cluster with ongoing compute costs, making it overkill and expensive for rarely accessed data; its JSON ingestion and compression do not offset the fixed infrastructure cost. Option B is wrong because Amazon DynamoDB is a NoSQL key-value store optimized for high-frequency, low-latency reads/writes, not for SQL-based ad-hoc querying of large JSON logs; its on-demand capacity mode still incurs per-request charges that are wasteful for infrequent access. Option D is wrong because Amazon RDS for PostgreSQL with JSONB columns requires a provisioned database instance with continuous compute and storage costs, and while JSONB supports indexing, it is not cost-effective for rarely queried, static log data compared to S3's pay-per-byte storage and Athena's pay-per-query model.

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343

MCQeasy

A company is migrating its on-premises MySQL database to Amazon RDS for MySQL. They want to minimize downtime and ensure data consistency. Which AWS service should be used for the migration?

A.AWS S3 Transfer Acceleration

B.AWS Glue

C.AWS Database Migration Service (DMS)

D.AWS Snowball Edge

AnswerC

AWS Database Migration Service performs continuous change data capture from the on-premises MySQL source, replicating ongoing transactions to Amazon RDS for MySQL until cutover. This satisfies the minimal-downtime requirement, while its validation feature confirms row-level data consistency between source and target before the switch.

Why this answer

AWS Database Migration Service (DMS) is the correct choice because it is specifically designed for migrating databases to AWS with minimal downtime. DMS supports homogeneous migrations like MySQL to Amazon RDS for MySQL, and it uses ongoing replication (change data capture) to keep the source and target databases in sync during the migration, ensuring data consistency and allowing a cutover with only seconds of downtime.

Exam trap

The trap here is that candidates may confuse AWS DMS with AWS Glue, thinking both are for data migration, but Glue is for batch ETL and cannot perform live database replication with minimal downtime, while DMS is purpose-built for that task.

How to eliminate wrong answers

Option A is wrong because AWS S3 Transfer Acceleration is a service that speeds up uploads to Amazon S3 by using optimized network paths and edge locations; it has no capability to migrate or replicate a live MySQL database to RDS. Option B is wrong because AWS Glue is a serverless data integration service for ETL (extract, transform, load) jobs, primarily used for preparing and transforming data for analytics, not for ongoing database replication or minimizing downtime during a live database migration. Option D is wrong because AWS Snowball Edge is a physical data transport device used for large-scale data transfers over slow or unreliable networks, but it is not suitable for minimizing downtime in a live database migration as it involves shipping hardware and cannot perform continuous replication.

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344

Multi-Selectmedium

Which TWO actions can help improve the read performance of an Amazon DynamoDB table that is experiencing throttling? (Choose two.)

Select 2 answers

A.Enable DynamoDB Accelerator (DAX) for write-heavy workloads.

B.Increase the provisioned read capacity units (RCUs) for the table.

C.Use eventually consistent reads instead of strongly consistent reads.

D.Add a global secondary index (GSI) with a different partition key.

E.Enable auto-scaling with a lower minimum capacity.

AnswersB, C

Raising provisioned RCUs directly lifts the table's read throughput ceiling, so sustained request rates that previously exceeded capacity no longer trigger ProvisionedThroughputExceededException throttling. This satisfies the stem's throttling constraint by matching allocated capacity to actual read demand, provided the workload is read-capacity-bound rather than partition-hot.

Why this answer

Option B is correct because DynamoDB throttling on reads occurs when request rate exceeds the table's provisioned read capacity units (RCUs); increasing the provisioned RCUs directly raises the available read throughput and relieves the throttling. Option C is correct because eventually consistent reads consume only half the RCUs of strongly consistent reads (1 RCU per 4 KB vs. 1 RCU per 2 KB), so switching to eventually consistent reads effectively doubles the read throughput available for the same provisioned capacity. Option A is wrong because DAX is designed to accelerate read-heavy workloads by caching, not write-heavy ones, and it does not address the stated read throttling.

Option D is wrong because adding a GSI does not increase the base table's read capacity and actually adds its own separate capacity costs and potential throttling. Option E is wrong because auto-scaling with a lower minimum capacity would reduce available RCUs and worsen, not improve, read throttling.

Exam trap

The trap is that candidates often overlook eventually consistent reads as a cost-effective way to reduce throttling, focusing instead on adding GSIs or enabling DAX, which address latency or query patterns but not the root cause of exceeding provisioned capacity.

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345

MCQeasy

A data engineer runs the above SQL commands on an Amazon Redshift cluster. The table 'users' is created with DISTSTYLE EVEN. What is the effect of the DISTSTYLE EVEN on query performance?

A.It stores all data on a single node for fast local queries.

B.It ensures data is evenly distributed across all nodes to prevent data skew.

C.It reduces data movement during queries by co-locating data based on user_id.

D.It improves join performance when joining on user_id.

AnswerB

DISTSTYLE EVEN spreads rows round-robin across every compute node slice, guaranteeing uniform row counts regardless of column values. This directly satisfies the stem's concern by preventing data skew, which would otherwise concentrate rows on some slices and force other nodes to idle during scans and joins.

Why this answer

DISTSTYLE EVEN in Amazon Redshift distributes rows across all nodes in a round-robin fashion, ensuring each node holds approximately the same amount of data. This prevents data skew, which can cause some nodes to become bottlenecks, and improves overall query performance for workloads that do not benefit from key-based distribution. It is the correct choice because it directly addresses the goal of balanced data distribution.

Exam trap

The trap here is that candidates often confuse DISTSTYLE EVEN with DISTSTYLE ALL, thinking EVEN improves performance by keeping data local, when in fact EVEN distributes data to prevent skew, not to localize it.

How to eliminate wrong answers

Option A is wrong because DISTSTYLE EVEN does not store all data on a single node; that describes DISTSTYLE ALL, which replicates the entire table to every node. Option C is wrong because EVEN distribution does not co-locate data based on user_id; that behavior is achieved with DISTSTYLE KEY, which distributes rows by the hash of a specified column. Option D is wrong because EVEN distribution does not improve join performance on user_id; joins on user_id benefit from DISTSTYLE KEY on user_id to enable collocated joins, while EVEN may require data redistribution across nodes during query execution.

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346

Multi-Selectmedium

A company uses AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by date. The company wants to improve query performance in Amazon Athena and reduce costs. Which THREE actions should the company take? (Choose THREE.)

Select 3 answers

A.Convert the data to JSON format for better schema evolution.

B.Use Glue DataBrew to clean the data before querying.

C.Partition the data by date so Athena can use partition pruning.

D.Ensure the data is in a columnar format like Parquet or ORC.

E.Compress the data using a codec like Snappy or Gzip.

AnswersC, D, E

Partition pruning limits the amount of data scanned per query.

Why this answer

Partitioning the data by date allows Athena to use partition pruning, which limits the amount of data scanned by only reading the partitions that match the query's WHERE clause. This directly reduces both query cost (since Athena charges per byte scanned) and query latency, especially for date-range queries on large datasets.

Exam trap

The trap here is that candidates may confuse data preparation tools (like Glue DataBrew) with query optimization techniques, or mistakenly think that converting to a non-columnar format like JSON improves schema evolution, when in fact columnar formats with compression and partitioning are the standard best practices for Athena performance and cost efficiency.

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347

MCQeasy

A company wants to enforce that all data in an S3 bucket is encrypted at rest using AWS KMS. Which bucket policy condition key should be used?

A.s3:x-amz-acl with value bucket-owner-full-control

B.s3:x-amz-server-side-encryption with value aws:kms

C.s3:x-amz-server-side-encryption with value AES256

D.aws:SourceIp with value 10.0.0.0/8

AnswerB

The s3:x-amz-server-side-encryption condition key with value aws:kms enforces that any PutObject request specifies AWS KMS encryption. This denies uploads lacking the required header, satisfying the mandate that all bucket data is encrypted at rest using KMS.

Why this answer

The condition key `s3:x-amz-server-side-encryption` with value `aws:kms` enforces that objects uploaded to the S3 bucket must be encrypted using AWS KMS (SSE-KMS). This bucket policy condition ensures that any PUT request includes the `x-amz-server-side-encryption` header set to `aws:kms`, thereby enforcing encryption at rest with KMS-managed keys.

Exam trap

The trap here is that candidates confuse `aws:kms` with `AES256` (SSE-S3), thinking both enforce KMS encryption, but only `aws:kms` enforces AWS KMS, while `AES256` enforces S3-managed keys.

How to eliminate wrong answers

Option A is wrong because `s3:x-amz-acl` with value `bucket-owner-full-control` enforces access control list ownership, not encryption. Option C is wrong because `s3:x-amz-server-side-encryption` with value `AES256` enforces SSE-S3 (Amazon S3-managed keys), not AWS KMS. Option D is wrong because `aws:SourceIp` restricts requests based on IP address, which is unrelated to encryption enforcement.

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348

Matchingmedium

Match each AWS monitoring tool to its primary use.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Metrics, logs, and alarms

API call history and auditing

Trace and analyze distributed applications

Event-driven automation

Resource configuration tracking

Why these pairings

CloudWatch monitors metrics and logs, CloudTrail audits API calls, X-Ray traces requests, and Trusted Advisor provides optimization recommendations. Common confusions include swapping the roles of CloudWatch and CloudTrail.

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349

MCQmedium

A data engineer stores Apache Parquet files in an Amazon S3 data lake partitioned by dt=YYYY-MM-DD. Analysts query the data with Amazon Athena, and monthly reports that scan one month of data are slow and expensive. The engineer confirms that queries filter on the dt column. Which action will MOST effectively reduce the amount of data scanned by these reports?

A.Convert the Parquet files to CSV so that Athena can read them faster.

B.Enable Amazon S3 Transfer Acceleration on the data lake bucket.

C.Run MSCK REPAIR TABLE on the table, or enable partition projection for the dt partition column.

D.Increase the Athena per-query data limit in the workgroup settings.

AnswerC

Athena prunes partitions only when the table's partition metadata matches the S3 prefixes. If new dt= prefixes were written without registering partitions in the AWS Glue Data Catalog, every query falls back to scanning the whole table. Running MSCK REPAIR TABLE, or configuring partition projection so Athena derives partitions from a pattern, restores pruning so a one-month filter reads only that month's prefixes.

Why this answer

Athena reduces scan size through partition pruning, which requires the table's partition metadata to reflect the actual S3 prefixes. When files are written as dt=YYYY-MM-DD without catalog registration, filters on dt cannot eliminate prefixes, so monthly reports scan the entire table. Registering partitions with MSCK REPAIR TABLE or defining partition projection restores pruning, cutting both bytes scanned and cost.

Exam trap

The trap here is assuming that switching file formats or tuning the workgroup fixes slow Athena queries, when the real issue is unregistered partitions that disable pruning.

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350

MCQeasy

A logistics company stores shipment tracking events in an Amazon DynamoDB table. The table uses a partition key of shipment_id and a sort key of event_timestamp. Analysts frequently run queries that filter by shipment_id and a range of event_timestamp values. The data engineer must ensure these queries are efficient and consume minimal read capacity. What should the data engineer do?

A.Use the base table with a Query operation specifying shipment_id as the partition key and a condition on event_timestamp.

B.Create a global secondary index on event_timestamp alone.

C.Run a Scan operation with a FilterExpression on shipment_id and event_timestamp.

D.Enable DynamoDB Streams on the table and query the stream for matching events.

AnswerA

The base table's composite primary key of shipment_id and event_timestamp is exactly the shape needed. A Query with shipment_id as the partition key and a range condition on event_timestamp reads only the matching item collection, avoids full table scans, and consumes read capacity proportional to the items returned.

Why this answer

The composite primary key already matches the access pattern, so a Query with shipment_id as the partition key and a condition on event_timestamp reads only the relevant item collection. This is the most efficient and lowest-cost approach and requires no additional index or infrastructure.

Exam trap

The trap here is reaching for a secondary index or Streams when the existing primary key already supports the query pattern efficiently.

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351

MCQeasy

A data engineer needs to store time-series data from IoT devices. The data is write-heavy and requires low-latency queries by device ID and timestamp. The data volume is expected to grow to terabytes. Which AWS database service is most suitable?

A.Amazon RDS for MySQL

B.Amazon ElastiCache for Redis

C.Amazon DynamoDB

D.Amazon Timestream

AnswerD

Amazon Timestream is purpose-built for time-series workloads, using a memory store for recent data and a magnetic store for historical data. This satisfies the stem's write-heavy, low-latency-by-device-and-timestamp, terabyte-scale requirements, which general-purpose relational or key-value services cannot match as efficiently.

Why this answer

Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical) to handle write-heavy IoT workloads at scale. It supports low-latency queries by device ID and timestamp via its SQL-compatible query engine, making it the most suitable choice for terabytes of time-series data.

Exam trap

The trap here is that candidates often choose DynamoDB (Option C) because of its high write throughput and low-latency queries, but they overlook the lack of native time-series optimizations, leading to complex manual partitioning and TTL management that Timestream handles automatically.

How to eliminate wrong answers

Option A is wrong because Amazon RDS for MySQL is a relational database optimized for OLTP workloads with structured queries, not for the high-volume, write-heavy, time-series pattern that requires automatic data retention policies and time-based partitioning. Option B is wrong because Amazon ElastiCache for Redis is an in-memory cache designed for sub-millisecond read/write performance on hot data, but it cannot cost-effectively store terabytes of data and lacks native time-series query optimizations like downsampling and interpolation. Option C is wrong because Amazon DynamoDB is a key-value and document database that can handle high write throughput, but it does not have built-in time-series functions (e.g., time-based aggregation, retention policies) and requires manual partitioning and TTL management to handle time-series data efficiently at terabyte scale.

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352

MCQhard

A company has an Amazon RDS for MySQL database that is experiencing performance issues due to a large number of read requests. The application is read-heavy and can tolerate eventually consistent reads. Which action will reduce the load on the primary database with the least operational overhead?

A.Create a read replica in the same region

B.Use Amazon ElastiCache for caching

C.Enable Multi-AZ deployment

D.Increase the instance size of the primary DB

AnswerA

A read replica asynchronously replicates from the primary and serves read-only traffic, offloading read requests. Because the application tolerates eventually consistent reads, this fits, and creating a replica requires minimal operational effort compared with sharding or caching layers.

Why this answer

Creating a read replica in the same region offloads read traffic from the primary RDS instance to a read-only copy, which directly addresses the read-heavy workload. Since the application can tolerate eventually consistent reads, the slight replication lag is acceptable, and this solution requires minimal operational overhead—just a few clicks in the AWS console or a single API call—without any application code changes.

Exam trap

The trap here is that candidates often confuse Multi-AZ (which only provides failover, not read scaling) with read replicas, or assume that ElastiCache is always the best caching solution without considering the operational overhead of code changes and cache management.

How to eliminate wrong answers

Option B is wrong because using Amazon ElastiCache introduces additional infrastructure to manage (e.g., cache invalidation, cluster configuration) and requires application code changes to implement caching logic, increasing operational overhead compared to a simple read replica. Option C is wrong because enabling Multi-AZ deployment provides high availability and automatic failover but does not offload read traffic; the standby instance is not used for reads, so it does not reduce load on the primary. Option D is wrong because increasing the instance size of the primary DB only scales vertically, which can be costly and still leaves all read traffic hitting a single instance, failing to distribute the load and not leveraging the read-heavy, eventually consistent tolerance.

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353

MCQhard

A data engineer is managing an Amazon S3 data lake that contains millions of small files. The engineer needs to optimize query performance in Amazon Athena and reduce costs. The data is stored in Parquet format and is partitioned by date. Which action should the engineer take to improve performance and reduce costs?

A.Use AWS Glue ETL to compact small files into larger files.

B.Convert the data to CSV format to enable faster scans.

C.Enable S3 Versioning on the bucket to improve read performance.

D.Increase the number of partitions to reduce the amount of data scanned per query.

AnswerA

Compacting small files into larger files reduces the number of S3 GET requests and metadata overhead, improving Athena query performance. It also reduces the amount of data scanned if the compaction results in better compression and columnar storage. This is a common optimization for data lakes with many small files.

Why this answer

Compacting small files into larger files using AWS Glue ETL reduces the number of files and S3 requests, improving Athena query performance and reducing costs. Other options either do not address the small file issue or would degrade performance. This is a best practice for optimizing data lakes.

Exam trap

The trap here is thinking that adding more partitions or enabling S3 Versioning will help, when the core issue is the number of small files, which requires compaction.

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354

Multi-Selecthard

A company is migrating a legacy data warehouse to Amazon Redshift. They need to choose a distribution style to minimize data movement during joins. Which THREE factors should they consider?

Select 3 answers

A.The size of the table (number of rows).

B.The join frequency with other tables on specific columns.

C.The number of columns in the table.

D.Whether the table is a fact or dimension table.

E.The data type of the distribution key column.

AnswersA, B, D

Table size determines whether a table qualifies for ALL distribution, which broadcasts a small table to every node and eliminates join data movement entirely. For large tables, row count instead guides choosing DISTKEY on the frequent join column, since ALL replication becomes impractical. Both paths directly minimise the internode traffic the stem requires.

Why this answer

Option A is correct because table size (number of rows) is a primary driver of distribution style choice: small tables are typically assigned ALL distribution so they can be broadcast to every node, eliminating data movement during joins, while large tables need KEY or EVEN distribution. Option B is correct because the whole purpose of choosing a distribution style is to co-locate matching rows on the same slice; if a table is frequently joined on a specific column, distributing both tables on that join key keeps the join local and avoids network redistribution. Option D is correct because fact and dimension tables play different roles in a star schema: large fact tables are usually distributed on their most common join key (or EVEN), while smaller dimension tables are often set to ALL so they are replicated to every node and can be joined without movement.

Option C is not a factor because the number of columns does not affect how rows are distributed across slices; it only affects storage width and I/O, not join data movement. Option E is not a factor because Redshift supports distribution keys of various data types, and the data type itself does not determine whether a join requires redistribution — what matters is whether the joined columns match and are used as distribution keys.

Exam trap

The trap here is that candidates may overthink irrelevant table properties like column count or data types, while the core considerations for minimizing data movement are table size, join frequency, and table role (fact vs. dimension).

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355

MCQeasy

A company uses Amazon Redshift for its data warehouse. The data engineering team loads data daily from Amazon S3 using COPY commands. Recently, the load performance has degraded because the S3 bucket contains many small files. The team needs to optimize the COPY operation to improve performance. Which approach should they take?

A.Use Redshift Spectrum to query the data directly from S3 without loading.

B.Increase the number of nodes in the Redshift cluster.

C.Use a manifest file that lists only the necessary files, and consolidate small files into larger ones before loading.

D.Enable automatic compression on the Redshift table.

AnswerC

A manifest restricts COPY to the listed files, avoiding the per-file overhead that many small objects cause, while consolidating them into larger files reduces the number of COPY operations. Together these directly address the degraded load performance.

Why this answer

The performance degradation is caused by the overhead of processing many small files during the COPY command. Consolidating small files into larger ones (e.g., 100 MB–1 GB each) reduces the number of S3 GET requests and the metadata overhead on Redshift, directly improving load throughput. Using a manifest file further optimizes by explicitly listing only the required files, avoiding unnecessary S3 list operations.

Exam trap

The trap here is that candidates often confuse scaling the cluster (Option B) with optimizing data ingestion, failing to recognize that the bottleneck is the number of S3 objects, not the cluster's compute capacity.

How to eliminate wrong answers

Option A is wrong because Redshift Spectrum queries data in place from S3 without loading it into Redshift tables, which does not optimize the COPY operation for loading data into the warehouse. Option B is wrong because increasing the number of nodes adds compute and storage capacity but does not address the root cause of many small files; the COPY command still suffers from the same per-file overhead regardless of cluster size. Option D is wrong because automatic compression (via the COPY command with the COMPUPDATE option) optimizes column encoding for storage efficiency, not the file-level I/O performance during the load process.

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356

MCQhard

A data engineer is designing a DynamoDB table for an application that requires strongly consistent reads and supports a global secondary index (GSI). The engineer needs to ensure that queries on the GSI return the most up-to-date data. Which statement about DynamoDB read consistency is correct?

A.GSI queries can be strongly consistent only if the index is created with a specific attribute for versioning.

B.GSI queries always return strongly consistent data because the index is maintained synchronously.

C.GSI queries are eventually consistent and cannot be made strongly consistent.

D.GSI queries can be strongly consistent if the base table uses on-demand capacity mode.

AnswerC

DynamoDB GSIs are maintained asynchronously from the base table, so queries on a GSI are eventually consistent and may return stale data. Strongly consistent reads are not supported on GSIs. This is a fundamental limitation, so the engineer must accept eventual consistency or query the base table directly for strong consistency.

Why this answer

DynamoDB GSIs are updated asynchronously from the base table, so reads from a GSI are eventually consistent and cannot be made strongly consistent. The engineer must either accept eventual consistency or query the base table directly for strongly consistent reads. This is a key design constraint when using GSIs.

Exam trap

The trap here is assuming that GSI reads can be strongly consistent like base table reads, when GSIs are inherently eventually consistent.

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357

Multi-Selectmedium

A data engineer is designing a data lake on Amazon S3 that will store sensitive financial data. The engineer needs to implement encryption at rest and ensure that only authorized users can access the data. Which TWO actions should the engineer take to meet these requirements? (Choose TWO.)

Select 2 answers

A.Configure a bucket policy that denies writes if the object is not encrypted.

B.Use server-side encryption with customer-provided keys (SSE-C).

C.Enable S3 Transfer Acceleration for the bucket.

D.Enable object-level access control lists (ACLs).

E.Create IAM policies that grant least privilege access to users.

AnswersA, E

Bucket policies can enforce encryption and control access.

Why this answer

A bucket policy with a condition that denies writes if the object is not encrypted (e.g., using `s3:x-amz-server-side-encryption` or `s3:PutObject` with `aws:SecureTransport`) enforces encryption at rest at the time of upload. This ensures that all objects written to the bucket are encrypted, meeting the encryption requirement without relying on client-side behavior.

Exam trap

The trap here is that candidates often confuse encryption enforcement with encryption method selection, picking SSE-C (option B) because it sounds more secure, but the question asks for actions that ensure encryption at rest and authorized access, not a specific key management model.

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358

MCQmedium

A data engineer is troubleshooting a slow Amazon Redshift query that joins several large tables. The query plan shows a large number of broadcasts. Which design change would most likely reduce the broadcast operations?

A.Change the SORT KEY on all tables to match the join column.

B.Change the DISTSTYLE to EVEN on all tables.

C.Change the DISTKEY on all tables to match the join column.

D.Change the DISTSTYLE to ALL on all large tables.

AnswerC

Broadcasts occur when joined tables lack a common distribution key, forcing each node to copy rows. Setting DISTKEY to the join column co-locates matching rows on the same slice, enabling collocated joins and eliminating broadcast traffic across the cluster.

Why this answer

Setting the DISTKEY on all tables to the join column ensures that rows with the same join key value are co-located on the same compute node. This allows Redshift to perform a collocated join, eliminating the need to broadcast entire tables across the network, which is the primary cause of the slow query.

Exam trap

The trap here is that candidates confuse SORT KEY (which optimizes data skipping and range scans) with DISTKEY (which controls data distribution for joins), leading them to pick Option A, even though broadcast reduction is purely a distribution concern.

How to eliminate wrong answers

Option A is wrong because changing the SORT KEY affects the order of data on disk and can improve range-restricted scans, but it does not influence data distribution across nodes; broadcast operations are caused by distribution mismatches, not sort order. Option B is wrong because changing DISTSTYLE to EVEN distributes rows randomly across nodes, which maximizes the chance that join keys are scattered, forcing Redshift to broadcast rows to satisfy the join. Option D is wrong because changing DISTSTYLE to ALL on large tables copies the entire table to every node, which reduces broadcasts but at the cost of massive storage and maintenance overhead, making it impractical for large tables and often degrading overall performance.

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