CCNA Data Store Management Questions — Page 6 of 6

376

MCQhard

A data team uses the CloudFormation template in the exhibit to create an S3 bucket for storing log files. After one year, they notice that the bucket size is larger than expected. They investigate and find that older versions of objects are not being deleted or transitioned. What is the most likely cause?

A.The lifecycle rule does not apply to noncurrent versions because it lacks NoncurrentVersionExpiration or NoncurrentVersionTransition.

B.The lifecycle rule is not enabled because the Status property is not set to 'Enabled' properly.

C.The bucket has versioning enabled, but the lifecycle rule only applies to current versions.

D.The expiration in days is set to 365, which is too short.

AnswerA

Old versions are not managed by the rule.

Why this answer

The lifecycle rule in the CloudFormation template is missing the `NoncurrentVersionExpiration` or `NoncurrentVersionTransition` properties. When S3 bucket versioning is enabled, lifecycle rules that only specify `ExpirationInDays` or `Transition` apply exclusively to the current version of objects. To manage older (noncurrent) versions, you must explicitly include `NoncurrentVersionExpirationInDays` or `NoncurrentVersionTransitionInDays` in the rule.

Without these, noncurrent versions accumulate indefinitely, causing the bucket size to grow larger than expected.

Exam trap

The trap here is that candidates assume a lifecycle rule with `ExpirationInDays` automatically cleans up all versions of an object, but in S3 with versioning enabled, it only affects the current version, leaving noncurrent versions to accumulate.

How to eliminate wrong answers

Option B is wrong because the `Status` property set to 'Enabled' is not the issue; the lifecycle rule is active, but it only targets current versions. Option C is wrong because the lifecycle rule does apply to current versions, but the problem is that it does not apply to noncurrent versions, which is the exact reason for the bucket size growth. Option D is wrong because the expiration in days being set to 365 is not too short; it is a reasonable period, but the rule still only affects current versions, leaving noncurrent versions untouched.

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377

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

Global Tables replicate data across regions automatically.

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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378

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

Query is efficient for this access pattern.

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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379

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

Spectrum queries data in S3, reducing cluster storage and allowing faster queries on hot data.

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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380

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

RDS automatically updates the DNS CNAME record to point to the standby instance.

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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381

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 expiration action with Days: 365 deletes the objects after 365 days.

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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382

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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383

MCQhard

A data engineer notices that an Amazon Redshift cluster's storage utilization has grown unexpectedly. The cluster uses automatic compression and has a mix of fact and dimension tables. The engineer runs VACUUM and ANALYZE, but storage does not decrease. Which action is most likely to reduce storage consumption?

A.Perform a DEEP COPY on the largest tables.

B.Run VACUUM with the BOOST option.

C.Run ANALYZE with the FULL keyword on all tables.

D.Modify the sort key on the largest tables to a more selective column.

AnswerA

DEEP COPY recreates the table with optimal compression, reclaiming storage from deleted rows and reorganizing data.

Why this answer

DEEP COPY recreates the table with a fresh, optimally sorted and compressed storage layout, reclaiming space that VACUUM alone cannot recover. In Redshift, VACUUM reorganizes and reclaims space from deleted rows but does not re-apply compression or rebuild the underlying storage blocks; DEEP COPY (e.g., using CREATE TABLE AS or the DEEP COPY command) physically rewrites the data, eliminating fragmentation and applying the current compression encoding, which can significantly reduce storage consumption when automatic compression has left suboptimal encodings or when historical updates have bloated the table.

Exam trap

The trap here is that candidates confuse VACUUM's space reclamation (which only removes deleted rows) with the need to physically rebuild the table to reapply compression, assuming VACUUM or ANALYZE can fix storage bloat caused by suboptimal encodings.

How to eliminate wrong answers

Option B is wrong because VACUUM BOOST is not a valid Redshift command; VACUUM has a BOOST option only in certain other database systems, and Redshift's VACUUM with the BOOST parameter does not exist — the correct options are VACUUM FULL, VACUUM DELETE ONLY, or VACUUM SORT ONLY, none of which reapply compression or reclaim space from suboptimal encodings. Option C is wrong because ANALYZE with the FULL keyword updates table statistics for the query planner but does not modify physical storage or reclaim space; it only refreshes metadata about data distribution and does not affect the actual data blocks. Option D is wrong because modifying the sort key on the largest tables changes the physical order of rows on disk, which can improve query performance but does not directly reduce storage consumption; sort keys affect how data is organized, not the compression ratio or the amount of space used by existing data.

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384

MCQhard

A company uses Amazon S3 to store large datasets for analytics. Each dataset is stored in a separate prefix and consists of thousands of small objects (1-10 KB each). The company notices that listing objects in a prefix takes several seconds, slowing down data processing. Which solution would MOST improve listing performance?

A.Add a lifecycle policy to transition objects to S3 Glacier.

B.Use S3 Select to filter objects during listing.

C.Use S3 Inventory to generate a daily listing of objects.

D.Increase the number of parallel requests by using more prefixes.

AnswerC

S3 Inventory provides a pre-generated list that can be queried quickly.

Why this answer

S3 Inventory provides a scheduled CSV/Parquet file listing all objects in a bucket or prefix, including metadata like size and last modified date. By querying this inventory file instead of issuing real-time ListObject API calls, you avoid the latency of enumerating thousands of small objects, dramatically improving listing performance for analytics workflows.

Exam trap

The trap here is that candidates confuse S3 Select (which filters object content) with filtering object keys during listing, or assume that parallel requests to a single prefix are allowed, when in fact S3 throttles ListObject calls per prefix and parallelism only helps across different prefixes.

How to eliminate wrong answers

Option A is wrong because transitioning objects to S3 Glacier does not improve listing performance; it only changes storage class and adds retrieval latency, while the ListObject API still must enumerate all objects. Option B is wrong because S3 Select is used to filter the content of objects (e.g., SQL queries on CSV/JSON data), not to filter object keys during listing; it cannot accelerate the ListObject operation. Option D is wrong because increasing parallel requests with more prefixes would require redesigning the data layout and does not reduce the time to list a single prefix; the bottleneck is the number of objects in that prefix, not parallelism.

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385

MCQhard

A data engineering team needs to store log files for 90 days with immediate access, then archive them for 7 years with infrequent access. Which S3 storage class configuration meets these requirements cost-effectively?

A.Use S3 One Zone-IA for 90 days, then lifecycle to S3 Glacier Deep Archive

B.Use S3 Intelligent-Tiering with lifecycle transition to S3 Glacier Deep Archive after 90 days

C.Use S3 Glacier Instant Retrieval for 90 days, then lifecycle to S3 Glacier Flexible Retrieval

D.Use S3 Standard for 90 days, then lifecycle policy to S3 Glacier Deep Archive

AnswerB

Intelligent-Tiering optimizes costs for unknown patterns, and lifecycle to Deep Archive meets long-term retention.

Why this answer

S3 Intelligent-Tiering automatically moves data between access tiers based on changing access patterns, making it cost-effective for logs that may have variable access during the first 90 days. A lifecycle policy then transitions the data to S3 Glacier Deep Archive after 90 days, which provides the lowest-cost storage for the 7-year archival period with infrequent access needs.

Exam trap

The trap here is that candidates often assume S3 Standard is necessary for immediate access over 90 days, overlooking that S3 Intelligent-Tiering provides the same retrieval performance at lower cost for data with variable access patterns, and that Glacier Deep Archive is the most cost-effective option for long-term archival with infrequent access.

How to eliminate wrong answers

Option A is wrong because S3 One Zone-IA is not designed for immediate access needs over 90 days; it lacks the durability of multi-AZ storage and is intended for re-creatable data, not critical logs. Option C is wrong because S3 Glacier Instant Retrieval is optimized for long-lived, rarely accessed data that requires millisecond retrieval, not for the initial 90-day active access period, and transitioning to Glacier Flexible Retrieval adds unnecessary cost and retrieval latency. Option D is wrong because S3 Standard is over-provisioned and more expensive than necessary for logs that do not require low-latency access for the full 90 days; a more cost-effective approach would use Intelligent-Tiering or Standard-IA to reduce costs during the active period.

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386

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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387

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

RDS offers managed relational databases with full SQL support, ideal for transactional workloads up to 500 GB.

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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388

MCQmedium

The exhibit shows the lifecycle configuration for an S3 bucket. Objects in the bucket are 200 days old on average. What will happen to the objects?

A.Objects will be transitioned to GLACIER after 90 days and deleted after 365 days.

B.Objects are in GLACIER now and will be deleted after 365 days from creation.

C.Objects will be transitioned to GLACIER after 200 days and deleted after 365 days.

D.Objects will be deleted after 90 days.

AnswerB

At 200 days, objects have been transitioned; expiration is at 365 days.

Why this answer

The lifecycle configuration shows a current version action to transition to GLACIOR (a misspelling of GLACIER) immediately (0 days after creation) and an expiration action to permanently delete the object 365 days after creation. Since the objects are already 200 days old on average, they have already been transitioned to GLACIER storage class. The expiration rule will delete them 365 days from their creation date, not from the current time.

Exam trap

The trap here is that candidates misinterpret the '0 days' transition as 'no transition' or assume the average age of 200 days means the transition hasn't happened yet, when in fact the lifecycle rules are based on creation date, not current age.

How to eliminate wrong answers

Option A is wrong because the lifecycle rule transitions objects to GLACIER immediately (0 days), not after 90 days. Option C is wrong because the transition occurs at 0 days, not after 200 days. Option D is wrong because the expiration deletes objects after 365 days, not after 90 days.

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389

MCQmedium

A company stores sensitive user data in an Amazon RDS for PostgreSQL DB instance. A security audit requires that all data be encrypted at rest. The database is currently unencrypted. What is the MOST operationally efficient way to enable encryption at rest?

A.Create a read replica with encryption enabled and promote it.

B.Take a snapshot of the DB instance, copy it with encryption enabled, and restore the snapshot to a new DB instance.

C.Modify the DB instance and enable encryption in the console.

D.Modify the DB parameter group to include encryption parameters and reboot the instance.

AnswerB

This is the standard method to enable encryption for an existing unencrypted RDS instance.

Why this answer

RDS for PostgreSQL does not support enabling encryption at rest on an existing unencrypted DB instance directly. The only way to achieve this is by taking a snapshot of the unencrypted instance, creating an encrypted copy of that snapshot, and then restoring it to a new encrypted DB instance. This method is operationally efficient as it uses native RDS snapshot copy and restore capabilities without requiring additional infrastructure or manual data migration.

Exam trap

The trap here is that candidates assume encryption can be toggled on via a simple 'Modify' operation in the console or CLI, but AWS RDS explicitly requires a snapshot copy and restore for existing unencrypted instances, a detail often overlooked in favor of more familiar modification workflows.

How to eliminate wrong answers

Option A is wrong because creating a read replica of an unencrypted source DB instance does not allow enabling encryption on the replica; RDS read replicas inherit the encryption setting of the source, and you cannot enable encryption on a replica if the source is unencrypted. Option C is wrong because the RDS console does not provide a 'Modify' option to enable encryption at rest on an existing unencrypted DB instance; encryption can only be specified at creation time or via snapshot restore. Option D is wrong because modifying the DB parameter group does not affect storage encryption; encryption at rest is a storage-layer feature controlled by the RDS instance configuration, not by PostgreSQL parameters.

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390

MCQmedium

A data engineer is designing a data store for a time-series application that requires sub-millisecond read latency for the latest data and high ingestion rates. Which AWS service is most suitable?

A.Amazon DynamoDB

B.Amazon ElastiCache for Redis

C.Amazon RDS for PostgreSQL

D.Amazon Timestream

AnswerD

Timestream is purpose-built for time-series data with fast queries.

Why this answer

Amazon Timestream is purpose-built for time-series data, offering sub-millisecond read latency for recent data via its in-memory store and supporting high ingestion rates with automatic partitioning and tiering. It is the most suitable choice because it natively handles time-series workloads without requiring manual tuning or schema optimization.

Exam trap

The trap here is that candidates often choose Amazon DynamoDB for its low-latency reputation, overlooking that time-series workloads require specialized storage engines to handle high-frequency writes and time-based queries without manual sharding or hot partition issues.

How to eliminate wrong answers

Option A is wrong because Amazon DynamoDB, while fast for key-value lookups, does not natively optimize for time-series patterns; it requires careful design of partition keys and time-based sorting to avoid hot partitions, and its read latency for the latest data can exceed sub-millisecond under high throughput. Option B is wrong because Amazon ElastiCache for Redis is an in-memory cache, not a durable data store; it lacks built-in time-series functions (e.g., downsampling, retention policies) and requires manual eviction or TTL management, making it unsuitable for long-term storage and high ingestion rates without data loss. Option C is wrong because Amazon RDS for PostgreSQL is a relational database with row-based storage; it cannot achieve sub-millisecond read latency for the latest data under high write loads due to disk I/O and indexing overhead, and it lacks native time-series optimizations like automatic data tiering or time-based partitioning.

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391

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

DMS supports live migration from MongoDB to DocumentDB with minimal downtime.

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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392

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

Object Lock in compliance mode prevents writes and deletes; bucket policies control access.

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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393

MCQeasy

A data engineer needs to store semi-structured JSON data that is accessed infrequently but must be retrievable within 5 minutes. The data is immutable once stored. Which storage solution is MOST cost-effective?

A.Amazon S3 Glacier Deep Archive

B.Amazon S3 Standard

C.Amazon S3 One Zone-IA

D.Amazon S3 Glacier Instant Retrieval

AnswerD

S3 Glacier Instant Retrieval: Correct. Lowest storage cost among options that meet the 5-minute retrieval requirement, ideal for long-term archival of infrequently accessed, immutable data.

Why this answer

Amazon S3 Glacier Instant Retrieval is the most cost-effective choice for long-term archival of infrequently accessed, immutable data with a 5-minute retrieval requirement. It offers a storage cost of $0.004/GB/month, significantly lower than One Zone-IA at $0.01/GB/month, and retrieval times of milliseconds to minutes, easily meeting the 5-minute requirement. One Zone-IA is cheaper than S3 Standard but more expensive than Glacier Instant Retrieval, and its single-AZ design introduces availability risk.

Glacier Deep Archive has a higher retrieval cost and typically takes hours, exceeding the 5-minute requirement.

Exam trap

The trap is that candidates may assume 'infrequently accessed' maps to 'archival' and choose Glacier Deep Archive, or they may focus on the single-AZ cost savings of One Zone-IA without comparing it to Glacier Instant Retrieval, missing the even lower storage cost of the latter.

How to eliminate wrong answers

Option A is wrong because Amazon S3 Glacier Deep Archive has a retrieval time of 12–48 hours, which far exceeds the 5-minute requirement. Option B is wrong because Amazon S3 Standard is designed for frequently accessed data and has a higher per-GB storage cost than One Zone-IA, making it less cost-effective for infrequently accessed immutable data. Option D is wrong because Amazon S3 Glacier Instant Retrieval, while providing millisecond retrieval, has a higher storage cost than One Zone-IA and is intended for long-term archival data that needs immediate access, not for infrequently accessed data that can tolerate a single-AZ risk.

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394

MCQhard

A company runs a critical application on Amazon RDS for MySQL that requires a Recovery Point Objective (RPO) of 5 minutes and a Recovery Time Objective (RTO) of 1 hour. The database is 500 GB. What is the MOST cost-effective disaster recovery solution that meets these requirements?

A.Deploy the database in a single Availability Zone and perform manual point-in-time restores.

B.Take automated snapshots daily and store them in Amazon S3.

C.Use a Multi-AZ deployment with automatic failover.

D.Create a cross-region read replica and promote it during a disaster.

AnswerC

Multi-AZ provides synchronous replication to a standby in another AZ, achieving RPO of seconds and RTO of minutes.

Why this answer

Multi-AZ RDS for MySQL provides synchronous standby replication to a second Availability Zone, enabling automatic failover with minimal data loss (typically zero) and RTO under 1 hour. This meets the RPO of 5 minutes and RTO of 1 hour without manual intervention, and is more cost-effective than a cross-region replica for a 500 GB database.

Exam trap

The trap here is that candidates often confuse Multi-AZ (synchronous, same-region, automatic failover) with cross-region read replicas (asynchronous, manual promotion), assuming both provide similar DR capabilities, but Multi-AZ is the only option that meets both RPO and RTO cost-effectively for a single-region requirement.

How to eliminate wrong answers

Option A is wrong because manual point-in-time restores from backups cannot achieve an RTO of 1 hour due to the time required to restore 500 GB from S3, and RPO depends on backup frequency, which is not guaranteed to be 5 minutes. Option B is wrong because daily automated snapshots provide an RPO of up to 24 hours, far exceeding the 5-minute requirement, and restoring from snapshots takes longer than 1 hour for a 500 GB database. Option D is wrong because a cross-region read replica uses asynchronous replication, which can introduce lag exceeding 5 minutes, and promoting it during a disaster requires manual steps that increase RTO beyond 1 hour; it is also more expensive due to cross-region data transfer costs.

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395

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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396

MCQhard

A company is running a MySQL database on Amazon RDS. The database size is 2 TB, and the company needs to migrate it to Amazon Aurora MySQL with minimal downtime. Which migration strategy is most appropriate?

A.Create an Aurora MySQL read replica from the RDS instance, then promote it.

B.Use mysqldump to export the database and import it into Aurora.

C.Take a snapshot of the RDS instance and restore it as an Aurora cluster.

D.Use AWS Database Migration Service (DMS) with full load and ongoing replication.

AnswerA

This approach allows replication with minimal downtime, then promote to master.

Why this answer

Creating an Aurora MySQL read replica from the existing RDS MySQL instance allows the Aurora cluster to stay synchronized with the source using MySQL’s native binlog replication. Once the replica lag reaches zero, you can promote it to a standalone Aurora cluster with minimal downtime, typically just a few seconds to stop writes and redirect traffic. This approach avoids the lengthy export/import process and leverages Amazon’s managed replication for near-zero-downtime migration.

Exam trap

The trap here is that candidates often assume DMS is always the best for minimal downtime, but for MySQL-to-Aurora migrations, the native read-replica promotion is simpler, faster, and fully managed by AWS, making it the most appropriate choice for this specific scenario.

How to eliminate wrong answers

Option B is wrong because mysqldump exports data as SQL statements, which for a 2 TB database would take hours to export and even longer to import, causing significant downtime and potential consistency issues. Option C is wrong because RDS snapshots cannot be directly restored as an Aurora cluster; you must first migrate the snapshot to an Aurora-compatible format using AWS DMS or the RDS-to-Aurora snapshot migration feature, which still requires downtime. Option D is wrong because while DMS with full load and ongoing replication can achieve minimal downtime, it adds unnecessary complexity and overhead compared to the simpler, native read-replica promotion method, which is the recommended AWS approach for MySQL-to-Aurora migrations.

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397

MCQmedium

A data engineering team uses AWS Glue ETL jobs to process data from Amazon S3 and load it into an Amazon Redshift cluster. The cluster has a single node of type dc2.large. The team notices that the ETL jobs are failing intermittently with errors related to disk space. The Redshift cluster shows that the disk is nearly full. The team needs to resolve the disk space issue and ensure the ETL jobs can complete successfully without increasing costs significantly. Which solution should the team implement?

A.Convert the cluster to use RA3 node types (e.g., ra3.xlarge) with managed storage.

B.Load data into a staging table first, then perform a VACUUM and ANALYZE on the target tables.

C.Add more nodes to the Redshift cluster by resizing to a multi-node dc2.large configuration.

D.Set the table's distribution style to ALL for fact tables to avoid data redistribution during joins.

AnswerA

Correct: RA3 nodes decouple compute and storage via managed storage, eliminating fixed local disk limits and enabling storage scaling as needed.

Why this answer

Converting to RA3 node types with managed storage allows compute and storage to scale independently, resolving the disk space issue without over-provisioning compute. Options B and D do not increase disk capacity. Option C adds more nodes but still uses fixed storage per node and increases cost significantly.

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398

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

DMS supports ongoing replication, minimizing downtime by allowing a final cutover after the initial load.

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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399

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

Correct. The policy has a condition requiring the encryption header to be specified in the request. Since the user does not include it, the request is denied.

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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400

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 database that supports JSON documents and offers multi-AZ durability.

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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401

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

Read replicas offload read traffic with minimal operational overhead.

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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402

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 increases total storage capacity.

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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403

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; partitioning improves query performance.

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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404

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 provides automated backups by default.

Why this answer

Amazon RDS automated backups are a built-in feature of RDS that automatically creates daily snapshots of the DB instance and backs up transaction logs every 5 minutes, enabling point-in-time recovery to any second within the retention period. This is the native, no-cost mechanism for backing up an RDS for SQL Server DB instance without additional configuration beyond setting the backup retention window.

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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405

MCQhard

A data engineering team is designing a data lake on Amazon S3. They need to store raw data in a format that supports schema evolution and is optimized for analytics with Amazon Athena. Which storage format should they use?

A.Parquet

B.CSV

C.Avro

D.JSON

AnswerA

Parquet is columnar, supports schema evolution, and is optimized for Athena.

Why this answer

Parquet is the correct choice because it is a columnar storage format that is highly optimized for Amazon Athena, which can read only the necessary columns to minimize data scanned and reduce query costs. Parquet also supports schema evolution through features like nested data structures and the ability to add or remove columns without rewriting the entire dataset, making it ideal for raw data lakes that must accommodate changing schemas over time.

Exam trap

The trap here is that candidates often choose Avro or JSON because they are known for schema evolution, but they overlook that the question specifically asks for optimization with Amazon Athena, which requires a columnar format like Parquet to minimize scanned data and cost.

How to eliminate wrong answers

Option B is wrong because CSV is a row-based, plain-text format that lacks native schema evolution support, requires full table scans in Athena, and incurs higher storage and query costs due to lack of compression and column pruning. Option C is wrong because Avro is a row-based format primarily designed for serialization and streaming workloads, not for analytical query engines like Athena, which benefit from columnar storage to reduce I/O. Option D is wrong because JSON is a self-describing, text-based format that supports schema evolution but is row-oriented, leading to inefficient querying in Athena due to full scans, larger data sizes, and no built-in compression or columnar optimization.

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406

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

Transition moves objects to another storage class based on age.

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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407

MCQeasy

A startup is building a ride-sharing application that uses Amazon DynamoDB to store trip data. The table has a partition key of 'trip_id' and a sort key of 'status'. The application writes a new item when a trip starts and updates the status when the trip ends. The development team is experiencing high write latency during peak hours. The table is provisioned with 5,000 write capacity units (WCU) and 5,000 read capacity units (RCU). CloudWatch metrics show that WriteThrottleEvents are occurring frequently, but the consumed write capacity is never above 4,000 WCU. The team suspects that the issue is due to hot partitions. How should the data engineer resolve this issue?

A.Modify the application to add a random suffix to the partition key when writing items.

B.Enable DynamoDB Accelerator (DAX) to cache write operations.

C.Decrease the provisioned RCU to 2,000 to reduce costs.

D.Increase the provisioned WCU to 10,000 to handle the spikes.

AnswerA

Adding random suffix distributes writes across multiple partitions, reducing hot spots.

Why this answer

Adding a random suffix to the partition key distributes writes across multiple partitions, preventing any single partition from becoming a hot spot and causing throttling. Option B is incorrect because DAX is an in-memory cache that speeds up reads, not writes; it does not address write throttling. Option C is incorrect because decreasing RCU does not affect write performance; write throttling is independent of read capacity.

Option D is incorrect because increasing WCU does not solve hot partition issues; throttling occurs at the partition level, and a single partition can only handle up to 1000 WCU per partition. Thus, distributing writes is the appropriate solution.

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408

MCQmedium

A company uses Amazon Redshift for data warehousing. The data engineer notices that query performance has degraded over time. The tables are frequently updated with new data, and the data engineer suspects that the distribution style is causing data skew. Which distribution style should the data engineer use to minimize data skew?

A.KEY distribution on a column with high cardinality

B.AUTO distribution

C.ALL distribution

D.EVEN distribution

AnswerD

Distributes rows evenly, ideal for preventing skew.

Why this answer

EVEN distribution (D) distributes rows across all slices in a round-robin fashion, ensuring each slice holds approximately the same number of rows. This eliminates data skew caused by uneven distribution keys, which is the suspected issue when query performance degrades over time with frequent updates. By avoiding any dependency on column values, EVEN distribution guarantees balanced data distribution regardless of data characteristics.

Exam trap

The trap here is that candidates often choose KEY distribution on a high-cardinality column (A) assuming it will balance data, but they overlook that high cardinality does not guarantee uniform distribution—skew can still occur if the column's values are not evenly distributed, which is a common misconception tested in the DEA-C01 exam.

How to eliminate wrong answers

Option A is wrong because KEY distribution on a high-cardinality column can still cause skew if the column's values are not uniformly distributed (e.g., many rows share the same value), leading to uneven data distribution across slices. Option B is wrong because AUTO distribution lets Redshift choose the distribution style based on table size and usage patterns, but it may select KEY distribution if the table is large, which could perpetuate or introduce skew if the chosen key is skewed. Option C is wrong because ALL distribution copies the entire table to every node, which minimizes skew but is inefficient for large, frequently updated tables due to high storage and maintenance overhead, and it does not address the root cause of skew in the distribution method.

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409

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

Glacier Deep Archive provides the lowest storage cost with retrieval times up to 12 hours.

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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410

MCQmedium

A data engineer is migrating an on-premises Apache Hive data warehouse to Amazon EMR. The warehouse contains partitioned tables stored in HDFS. The engineer wants to use Amazon S3 as the storage layer for the EMR cluster. What is the MOST important consideration for maintaining query performance on S3?

A.Ensure that the table partitions are organized in a way that minimizes S3 LIST requests

B.Configure EMR to use HDFS for storage instead of S3 for better performance

C.Use DynamoDB as the Hive metastore to improve metadata access

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

AnswerA

S3 LIST operations are slower than HDFS; partitioning by common query filters and using partition projection can improve performance.

Why this answer

When using Amazon S3 as the storage layer for an EMR cluster, the most critical factor for query performance is minimizing S3 LIST requests. S3 LIST operations are significantly slower and more expensive than GET requests, and Hive/Spark queries on partitioned tables often issue LIST requests to discover partition locations. By organizing partitions with a common prefix (e.g., `year=2023/month=01/day=15/`) and using partition pruning, you reduce the number of LIST calls, directly improving query latency and reducing S3 API costs.

Exam trap

The trap here is that candidates may focus on metastore performance (Option C) or alternative query engines (Option D), missing the fundamental S3 performance bottleneck of LIST requests when querying partitioned data on EMR.

How to eliminate wrong answers

Option B is wrong because using HDFS for storage would negate the benefits of S3 (durability, scalability, cost) and is not the 'most important consideration' for maintaining query performance on S3; EMR can use S3 with optimizations like EMRFS and consistent view. Option C is wrong because DynamoDB is used as a Hive metastore for high availability and scalability, not to improve metadata access performance for S3 queries; the metastore choice does not directly address S3 LIST request overhead. Option D is wrong because Redshift Spectrum is a separate service for querying S3 data with Redshift, not an EMR optimization; it does not address the core performance issue of S3 LIST requests in an EMR context.

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411

MCQmedium

A data engineer needs to store clickstream data from a web application in Amazon S3. Each event is about 5 KB, and the application generates 1 million events per hour. The data is used for real-time analytics and also for batch processing. The engineer wants to minimize storage costs while ensuring that data is available for real-time queries as soon as it is written. Which storage class should the engineer use for the S3 bucket?

A.S3 Standard.

B.S3 Intelligent-Tiering.

C.S3 Standard-IA.

D.S3 Glacier Instant Retrieval.

AnswerA

Standard offers the best performance for frequently accessed data and no retrieval fees.

Why this answer

S3 Standard is the correct choice because it offers low-latency (milliseconds) first-byte-out performance, making data available for real-time queries immediately after writing. It also supports both real-time analytics and batch processing without retrieval fees, and at 5 KB per event with 1 million events per hour (approximately 5 GB/hour or 120 GB/day), the storage cost is manageable compared to the frequent access pattern. S3 Standard is optimized for frequently accessed data and provides the durability and availability needed for clickstream analytics.

Exam trap

The DEA-C01 exam often tests the misconception that S3 Intelligent-Tiering automatically optimizes costs for all workloads, but the trap here is that the per-object monitoring fee and potential tier transitions make it more expensive than S3 Standard for high-volume, small-object, frequently accessed data like clickstream events.

How to eliminate wrong answers

Option B (S3 Intelligent-Tiering) is wrong because it incurs a monthly monitoring and automation fee per object, which for 1 million events per hour (24 million objects/day) would result in excessive costs, and it may not guarantee immediate availability for real-time queries if objects are moved to colder tiers. Option C (S3 Standard-IA) is wrong because it has a minimum billable object size of 128 KB and a 30-day minimum storage charge, so storing 5 KB events would be cost-inefficient due to rounding up to 128 KB, and it also charges a retrieval fee per GB, which adds cost for frequent real-time access. Option D (S3 Glacier Instant Retrieval) is wrong because while it offers millisecond retrieval, it has a minimum billable object size of 128 KB and a 90-day minimum storage duration, making it prohibitively expensive for small, short-lived clickstream events, and it is designed for long-term archival, not real-time streaming ingestion.

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412

Multi-Selectmedium

A data engineer is designing a disaster recovery plan for an Amazon RDS for MySQL database. The database must be recoverable within 1 hour in a different AWS Region. Which TWO actions should the engineer take?

Select 2 answers

A.Create a cross-Region read replica.

B.Enable Multi-AZ deployment.

C.Enable automated backups with cross-Region copy.

D.Take manual snapshots and copy them to an S3 bucket in the other Region.

E.Use Amazon EventBridge to schedule snapshot copies.

AnswersA, C

A read replica can be promoted to a primary in another Region for disaster recovery.

Why this answer

A cross-Region read replica for Amazon RDS for MySQL provides a fully provisioned secondary database in a different AWS Region that can be promoted to a standalone primary in minutes, meeting the 1-hour recovery time objective (RTO). This approach ensures continuous replication from the source database, minimizing data loss and enabling rapid failover without manual snapshot management.

Exam trap

The trap here is that candidates often confuse Multi-AZ (high availability within a Region) with cross-Region disaster recovery, or they assume that scheduling snapshot copies via EventBridge is sufficient for fast recovery, ignoring the significant restore time required for snapshots.

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413

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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414

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

Automatically transitions objects to cheaper storage.

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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415

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

Security group rules must permit the connection.

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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416

Multi-Selecteasy

A company is evaluating Amazon DynamoDB for a new application. The application requires single-digit millisecond latency for read and write operations. Which TWO DynamoDB features should the company enable to achieve this? (Choose TWO.)

Select 2 answers

A.Use DAX with Write-Through caching.

B.Enable DynamoDB Global Tables.

C.Enable DynamoDB Streams.

D.Enable DynamoDB Accelerator (DAX).

E.Enable auto-scaling for read and write capacity.

AnswersA, D

Why this answer

DAX with Write-Through caching ensures that every write to DynamoDB is also written to the DAX cache, so subsequent reads of the same item are served from the in-memory cache with single-digit millisecond latency. Option D is correct because DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache that reduces read response times from single-digit milliseconds to microseconds, directly meeting the latency requirement for read operations.

Exam trap

The trap here is that candidates often confuse DynamoDB Accelerator (DAX) with DynamoDB Global Tables, assuming that multi-region replication improves local latency, when in fact DAX is the only feature that provides an in-memory cache for single-digit millisecond reads within a region.

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417

Multi-Selectmedium

Which TWO actions can improve query performance on an Amazon Redshift cluster? (Choose two.)

Select 2 answers

A.Define appropriate sort keys

B.Increase the number of nodes

C.Use EVEN distribution style for all tables

D.Use columnar compression

E.Run VACUUM command regularly

AnswersA, D

Sort keys reduce the amount of data scanned.

Why this answer

Defining appropriate sort keys in Amazon Redshift physically orders data on disk by the sort key columns, enabling the query optimizer to use zone maps to skip large blocks of data that do not match query predicates. This drastically reduces the amount of data scanned, especially for range-restricted queries, improving I/O and overall query performance.

Exam trap

The trap here is that candidates often confuse maintenance operations (VACUUM) or scaling actions (adding nodes) with direct query tuning techniques, while the exam specifically tests the understanding that sort keys and compression are the two primary table design choices that directly improve query performance.

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418

Multi-Selecthard

A data engineer is designing a data lake on Amazon S3 for analytics. The data includes sensitive PII that must be encrypted at rest. The company requires that the encryption keys be managed by the company's own hardware security module (HSM) and rotated every 90 days. Which TWO options meet these requirements? (Choose TWO.)

Select 2 answers

A.Use S3 server-side encryption with AWS KMS and an AWS managed key

B.Use S3 server-side encryption with customer-provided keys (SSE-C)

C.Use client-side encryption with keys stored in AWS Secrets Manager

D.Use S3 server-side encryption with AWS KMS (SSE-KMS) and a customer-managed key with imported key material from your HSM

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

AnswersB, D

SSE-C allows you to supply your own encryption keys, which you can rotate by re-encrypting objects.

Why this answer

SSE-C allows you to provide your own encryption keys, which can be managed and rotated from your own HSM. The keys are used server-side by S3 to encrypt objects at rest, but S3 does not store the keys—you manage them entirely, meeting the requirement for key management on your own HSM with 90-day rotation.

Exam trap

The trap here is that candidates often assume only SSE-KMS can meet key management requirements, but they overlook that SSE-C directly supports customer-supplied keys from an HSM without any AWS key storage, and that SSE-KMS with imported key material also satisfies the HSM and rotation needs when properly configured.

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419

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

S3 provides cheap storage and Athena allows serverless SQL queries, ideal for low-frequency access.

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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420

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

DMS supports continuous replication and minimal downtime for database migrations.

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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421

MCQmedium

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

A.Denies PutObject when encryption is not SSE-KMS.

B.Denies all PutObject requests.

C.Allows PutObject only when the object is encrypted with SSE-KMS.

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

AnswerC

Correct. The Deny with StringNotEquals on aws:kms denies PutObject requests that do not use SSE-KMS encryption, thus allowing only those with SSE-KMS.

Why this answer

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

Exam trap

The trap is that candidates may misread StringNotEquals as StringEquals, thinking the policy denies SSE-KMS encryption when it actually denies non-SSE-KMS.

How to eliminate wrong answers

Option A is wrong because the policy denies `PutObject` when encryption is NOT SSE-KMS, not when it is SSE-KMS; the condition `StringNotEquals` denies non-matching values. Option B is wrong because the policy does not deny all `PutObject` requests; it only denies those that do not meet the encryption condition, allowing those with SSE-KMS. Option C is wrong because the policy uses a `Deny` effect, not an `Allow`; it does not explicitly allow `PutObject` with SSE-KMS, but rather denies everything else, making SSE-KMS the only permitted case.

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422

MCQmedium

A company uses Amazon S3 to store sensitive documents. The security team has mandated that all objects must be encrypted at rest using server-side encryption with AWS KMS (SSE-KMS). Additionally, the company wants to ensure that any attempt to upload an unencrypted object is denied. A data engineer has configured a bucket policy that denies PutObject if the encryption header does not include x-amz-server-side-encryption: aws:kms. However, the engineer notices that some objects are still being stored without encryption. Upon investigation, the engineer suspects that the policy is not being evaluated correctly. What should the engineer do to ensure that all objects are encrypted with SSE-KMS?

A.Use an IAM policy to require encryption instead of a bucket policy.

B.Enable S3 Block Public Access settings.

C.Add a condition to the bucket policy that checks for aws:SourceVpce.

D.Enable default encryption on the S3 bucket with SSE-KMS.

AnswerD

Default encryption ensures all objects are encrypted, complementing the bucket policy.

Why this answer

Enabling default encryption on the S3 bucket with SSE-KMS ensures that any object uploaded without an explicit encryption header is automatically encrypted with SSE-KMS. This closes the gap where the bucket policy condition fails to catch uploads that omit the `x-amz-server-side-encryption` header entirely, as the policy only denies requests with an incorrect header but does not block requests that lack the header altogether. Default encryption applies server-side encryption at the bucket level, making it impossible to store an unencrypted object.

Exam trap

The trap here is that candidates assume a bucket policy condition denying PutObject without `x-amz-server-side-encryption: aws:kms` will block all unencrypted uploads, but they overlook that the condition only matches when the header is present with a wrong value, not when the header is absent entirely.

How to eliminate wrong answers

Option A is wrong because IAM policies cannot enforce encryption requirements on S3 PutObject operations as effectively as bucket policies; IAM policies lack the ability to condition on S3-specific headers like `x-amz-server-side-encryption`, and they apply to users/roles rather than the bucket itself, leaving gaps for anonymous or cross-account uploads. Option B is wrong because S3 Block Public Access settings only prevent public access to objects and buckets, not encryption enforcement; they have no effect on whether objects are encrypted at rest. Option C is wrong because checking for `aws:SourceVpce` restricts access based on VPC endpoint origin, which is unrelated to encryption requirements and would not prevent unencrypted uploads from other sources.

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423

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

EVEN distribution spreads rows evenly, avoiding skew.

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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424

MCQhard

A data engineer is troubleshooting an Amazon Redshift cluster that is experiencing slow query performance. The engineer notices that the disk space is heavily utilized and queries are spilling to disk. What is the most cost-effective solution to improve performance?

A.Run VACUUM command to reclaim space

B.Change distribution style to KEY

C.Resize the cluster to a larger node type or add nodes

D.Apply compression encoding to tables

AnswerC

Adding memory and disk reduces spilling to disk.

Why this answer

When queries spill to disk due to heavy disk utilization, the root cause is insufficient memory or compute capacity relative to the workload. Resizing the cluster (adding nodes or moving to a larger node type) directly increases available memory and CPU, reducing or eliminating disk spill and improving query performance. This is the most cost-effective solution because it scales resources proportionally without requiring manual tuning or schema changes.

Exam trap

The trap here is that candidates confuse disk space management (VACUUM, compression) with memory/query execution issues, leading them to choose storage optimization options when the real bottleneck is insufficient compute resources.

How to eliminate wrong answers

Option A is wrong because VACUUM reclaims space from deleted rows but does not increase memory or reduce disk spill; it only reorganizes existing data. Option B is wrong because changing distribution style (e.g., to KEY) optimizes data redistribution for joins but does not address insufficient memory or disk spill. Option D is wrong because applying compression encoding reduces storage footprint and I/O, but does not increase memory or compute capacity to prevent queries from spilling to disk.

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425

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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426

MCQmedium

A company uses Amazon Redshift for data warehousing. The data team notices that queries are slow due to high disk usage on the cluster. They need to free up space without deleting any data. What should they do?

A.Change the table's sort keys

B.Run a deep copy to re-sort and reclaim space

C.Run VACUUM command

D.Add more nodes to the cluster

AnswerB

Deep copy reorganizes data and reclaims disk space effectively.

Why this answer

A deep copy recreates the table with optimal sort order and reclaims unused space by reorganizing data blocks. This process effectively frees up disk space without deleting any data, addressing the high disk usage issue.

Exam trap

The trap here is that candidates often confuse the VACUUM command with a deep copy, assuming VACUUM reclaims all unused space, but VACUUM only handles space from deleted rows and does not fully reorganize unsorted data to reduce high disk usage.

How to eliminate wrong answers

Option A is wrong because changing sort keys does not reclaim disk space; it only improves query performance by optimizing data ordering. Option C is wrong because the VACUUM command reclaims space from deleted or updated rows but does not reorganize data to the extent needed when disk usage is high due to unsorted data or storage inefficiencies. Option D is wrong because adding more nodes increases cluster capacity but does not free up existing disk space; it is a scaling solution, not a space reclamation technique.

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427

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

Enforces SSE-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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428

MCQmedium

A company is using Amazon RDS for MySQL with Multi-AZ deployment. The database experiences intermittent slowdowns during peak hours. The company's DevOps team suspects that the primary instance is overwhelmed. Which action should the team take to distribute the read load without changing the application code?

A.Increase the instance size of the RDS instance.

B.Create a read replica and modify the connection string to point to the replica for read queries.

C.Enable Multi-AZ on the existing instance.

D.Configure DynamoDB Accelerator (DAX) in front of the RDS instance.

AnswerB

Read replicas offload read traffic from the primary instance.

Why this answer

Creating a read replica and modifying the connection string to point to the replica for read queries (Option B) offloads read traffic from the primary RDS instance without requiring application code changes. This directly addresses the intermittent slowdowns during peak hours by distributing the read load, leveraging MySQL’s native replication to keep the replica synchronized. The key constraint is 'without changing the application code,' which is satisfied by simply updating the connection string in the application configuration.

Exam trap

The trap here is that candidates confuse Multi-AZ with read replicas, assuming Multi-AZ can distribute read traffic, but in RDS for MySQL, the standby in a Multi-AZ deployment is not accessible for reads—it only provides failover support.

How to eliminate wrong answers

Option A is wrong because increasing the instance size scales vertically, which does not distribute the read load; it only provides more resources to a single instance, which may still be overwhelmed during peak hours and does not leverage Multi-AZ or read replicas. Option C is wrong because Multi-AZ is already enabled (as stated in the question) and its purpose is high availability and failover, not read load distribution; the standby instance in Multi-AZ cannot serve read traffic. Option D is wrong because DynamoDB Accelerator (DAX) is an in-memory cache for Amazon DynamoDB, not for Amazon RDS for MySQL; it cannot be placed in front of an RDS instance and would require significant application code changes to integrate.

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429

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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430

Multi-Selectmedium

A data engineer is evaluating storage options for a new application that requires low-latency access to unstructured blobs (up to 5 TB each) with high throughput. The data will be accessed frequently for the first 30 days and then rarely. Which TWO storage solutions meet these requirements? (Choose TWO)

Select 2 answers

A.Amazon S3 with lifecycle policies

B.Amazon EBS with io2 Block Express volumes

C.Amazon EFS

D.Amazon RDS for PostgreSQL

E.Amazon FSx for Lustre

AnswersA, E

S3 can handle large objects and lifecycle policies automate transitions to cost-optimized storage.

Why this answer

Amazon S3 with lifecycle policies is correct because S3 provides low-latency access to unstructured blobs (up to 5 TB each) with high throughput, and lifecycle policies can automatically transition objects to colder storage tiers (e.g., S3 Glacier Deep Archive) after 30 days, matching the access pattern of frequent then rare access.

Exam trap

The trap here is that candidates may confuse block storage (EBS) or file storage (EFS) with object storage (S3), or overlook that lifecycle policies are the key to handling the 'frequent then rare' access pattern, leading them to choose EBS or EFS for blob storage.

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431

MCQmedium

A company has an S3 bucket with millions of objects. The data engineer needs to identify which objects are not accessed for 90 days to move them to a lower-cost storage class. Which feature should be used?

A.S3 Storage Class Analysis

B.S3 Inventory

C.S3 Server Access Logs

D.S3 Event Notifications

AnswerA

It analyzes access patterns and provides recommendations for lifecycle transitions.

Why this answer

S3 Storage Class Analysis (SCA) is the correct feature because it monitors access patterns across objects and provides recommendations for transitioning data to lower-cost storage classes based on last-access dates. SCA can analyze objects that have not been accessed for 90 days and generate a report to inform lifecycle policy creation, directly addressing the requirement to identify objects for cost optimization.

Exam trap

The trap here is that candidates often confuse S3 Inventory (which lists objects) with S3 Storage Class Analysis (which analyzes access patterns), assuming that a list of objects is sufficient to determine access frequency, but Inventory lacks the temporal access data needed for this task.

How to eliminate wrong answers

Option B (S3 Inventory) is wrong because it provides a flat list of all objects and their metadata (e.g., size, storage class) but does not track access patterns or last-accessed timestamps, so it cannot identify objects unused for 90 days. Option C (S3 Server Access Logs) is wrong because it records detailed request-level logs (e.g., requester, operation, timestamp) but requires custom parsing and aggregation to derive last-access dates, and it does not natively provide a summary of objects not accessed for a specific period. Option D (S3 Event Notifications) is wrong because it triggers real-time events for object operations (e.g., PUT, POST, DELETE) but does not store historical access data or analyze access patterns over time, making it unsuitable for identifying long-unused objects.

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432

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

Timestream is designed for time-series data.

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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433

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

Offloads read traffic with minimal overhead.

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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434

MCQeasy

A data engineer needs to store semi-structured JSON data that is accessed infrequently but requires millisecond retrieval latency. The data is immutable once written. Which AWS service is most cost-effective?

A.Amazon DynamoDB with on-demand capacity

B.Amazon ElastiCache for Redis

C.Amazon RDS for PostgreSQL with JSONB

D.Amazon S3 (Standard-IA) with S3 Select

AnswerD

S3 Select can retrieve subsets of JSON data efficiently, and Standard-IA is cost-effective for infrequent access.

Why this answer

Amazon S3 Standard-IA with S3 Select is the most cost-effective choice because it provides infrequent access storage at low cost while S3 Select enables server-side filtering to retrieve only the required subset of JSON data, achieving millisecond latency for small queries on immutable data without the overhead of a full database.

Exam trap

The trap here is that candidates assume infrequent access requires a database like DynamoDB or RDS, but S3 Select with Standard-IA provides the same millisecond retrieval latency for small filtered queries at a fraction of the cost, especially for immutable data.

How to eliminate wrong answers

Option A is wrong because DynamoDB with on-demand capacity is designed for frequent, unpredictable workloads and incurs higher per-request costs, making it cost-ineffective for infrequently accessed data. Option B is wrong because ElastiCache for Redis is an in-memory cache optimized for sub-millisecond latency on hot data, but it is expensive for infrequent access and requires ongoing memory costs even when idle. Option C is wrong because Amazon RDS for PostgreSQL with JSONB provides ACID compliance and indexing for JSON, but it incurs continuous compute and storage costs for a database instance that is over-provisioned for infrequent access, making it less cost-effective than S3.

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435

MCQhard

A company uses Amazon DynamoDB to store session data for a web application. The application experiences occasional spikes in traffic, causing throttling on the table. The data engineer needs to implement a solution that handles traffic spikes without manual intervention and minimizes cost. What should the data engineer do?

A.Switch to provisioned capacity with a high fixed read/write capacity.

B.Implement DynamoDB Accelerator (DAX) to cache read requests.

C.Purchase DynamoDB reserved capacity.

D.Enable DynamoDB Auto Scaling for the table.

AnswerD

Auto Scaling adjusts capacity automatically to handle spikes and minimize cost.

Why this answer

DynamoDB Auto Scaling (option D) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, using CloudWatch alarms and the Application Auto Scaling service. This handles traffic spikes without manual intervention and minimizes cost by scaling down during low traffic periods, making it the ideal solution for variable workloads like session data.

Exam trap

The trap here is that candidates often confuse caching (DAX) as a solution for all throttling, but DAX only addresses read-side throttling and does not help with write throttling, which is critical for session data that is frequently updated.

How to eliminate wrong answers

Option A is wrong because switching to provisioned capacity with a high fixed read/write capacity would lead to over-provisioning during normal traffic, incurring unnecessary costs, and still risks throttling if the spike exceeds the fixed limit. Option B is wrong because DynamoDB Accelerator (DAX) is an in-memory cache that only reduces read latency and read throttling; it does not address write throttling, which is a common issue with session data updates. Option C is wrong because purchasing DynamoDB reserved capacity provides a discount on provisioned capacity but does not dynamically handle traffic spikes; it still requires manual capacity planning and does not prevent throttling during unexpected surges.

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436

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

Large tables need careful distribution to avoid skew.

Why this answer

The size of the table (number of rows) directly influences the distribution strategy. In Amazon Redshift, large tables benefit from a distribution style that evenly distributes rows across slices to avoid data skew, which can cause performance bottlenecks during joins. Choosing a distribution key that aligns with the join columns minimizes data movement, but the table size determines whether an ALL distribution (for small tables) or a KEY distribution (for large tables) is more appropriate to reduce shuffling.

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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437

MCQhard

A company uses Amazon DynamoDB for a gaming application. The table has a partition key of 'user_id' and a sort key of 'game_timestamp'. The application frequently queries by 'user_id' and filters by 'game_timestamp' within a specific date range. The queries are slow. The table has a global secondary index (GSI) on 'game_timestamp'. What is the most likely cause of the slow queries?

A.The GSI has insufficient read capacity.

B.The GSI is used instead of the base table for queries on 'user_id'.

C.A hot partition exists due to uneven access pattern on 'user_id'.

D.The sort key is not used in the query.

AnswerC

If a few 'user_id' values are accessed frequently, they create hot partitions, slowing queries.

Why this answer

The slow queries are most likely caused by a hot partition on the base table. Even though the query uses 'user_id' as the partition key, if a small number of 'user_id' values receive a disproportionate amount of traffic, those specific partitions become overloaded, causing throttling and high latency. The GSI on 'game_timestamp' is not used for these queries because the filter is on the sort key of the base table, and DynamoDB can efficiently query by partition key and filter by sort key without needing a GSI.

Exam trap

The trap here is that candidates assume a GSI is always the solution for slow queries, but the real issue is partition-level throttling from uneven access patterns, which a GSI on a different attribute cannot fix.

How to eliminate wrong answers

Option A is wrong because the GSI is not being used for these queries; the queries are on the base table's partition key 'user_id', so the GSI's read capacity is irrelevant. Option B is wrong because the GSI is on 'game_timestamp', not on 'user_id', so DynamoDB would not use the GSI for a query that filters by 'user_id'; it would use the base table. Option D is wrong because the sort key 'game_timestamp' is indeed used in the query as a filter, and DynamoDB can efficiently perform range queries on the sort key within a partition; the issue is not the absence of sort key usage but uneven access load.

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438

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

Fewer large files improve COPY 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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439

MCQeasy

A data engineer is designing a data lake on Amazon S3 for storing raw sensor data. The data is append-only and accessed infrequently after 30 days. Compliance requires that data be retained for 7 years. Which S3 storage class is the MOST cost-effective for data older than 30 days?

A.S3 Standard-IA

B.S3 Glacier Deep Archive

C.S3 One Zone-IA

D.S3 Intelligent-Tiering

AnswerB

This is the lowest-cost storage class for long-term archival data with infrequent access.

Why this answer

B is correct because Amazon S3 Glacier Deep Archive is the most cost-effective storage class for data that is accessed infrequently and must be retained for long periods (7 years). For data older than 30 days, the retrieval time of 12 hours is acceptable given the append-only, infrequent access pattern, and the storage cost is significantly lower than other classes.

Exam trap

The trap here is that candidates often choose S3 Glacier Flexible Retrieval (not listed) or S3 Standard-IA, mistakenly thinking that faster retrieval is necessary for compliance data, when in fact the 12-hour retrieval time of Glacier Deep Archive is sufficient for infrequent access patterns and offers the lowest cost.

How to eliminate wrong answers

Option A is wrong because S3 Standard-IA is designed for infrequently accessed data but has higher storage costs than Glacier Deep Archive, making it less cost-effective for 7-year retention. Option C is wrong because S3 One Zone-IA does not provide the durability of 99.999999999% across multiple Availability Zones, which is critical for compliance-retained data, and its storage cost is higher than Glacier Deep Archive. Option D is wrong because S3 Intelligent-Tiering automatically moves data between tiers but incurs a monthly monitoring and automation fee per object, and it does not include a Deep Archive tier by default, so it would not achieve the lowest cost for data older than 30 days without manual configuration.

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440

MCQhard

A company is running a production Amazon Aurora PostgreSQL database. The database experiences high write latency during peak hours. The data engineer suspects that the issue is due to a large number of small transactions. Which action would MOST effectively reduce write latency?

A.Enable parallel query for the database

B.Increase the instance size and use Provisioned IOPS storage

C.Enable Aurora Auto Scaling for read replicas

D.Enable Performance Insights to identify the bottleneck

AnswerB

Larger instances provide more CPU and memory, and Provisioned IOPS can reduce I/O latency, helping with write performance under high transaction loads.

Why this answer

Increasing the instance size and using Provisioned IOPS storage directly addresses high write latency by providing more CPU and memory resources to handle transaction processing, while Provisioned IOPS ensures consistent, low-latency I/O for write operations. This is the most effective action because small transactions create high I/O demand, and scaling up the instance with dedicated IOPS reduces contention and write queue depth.

Exam trap

The trap here is that candidates often confuse scaling read replicas (which only help read scaling) with solving write latency, or they mistake monitoring tools (like Performance Insights) for performance fixes, when the real solution is to provision more write capacity through larger instances and dedicated IOPS.

How to eliminate wrong answers

Option A is wrong because parallel query is designed for read-heavy analytical queries, not for reducing write latency from small transactions; it does not improve write throughput or I/O performance. Option C is wrong because Aurora Auto Scaling for read replicas only scales read capacity, not write capacity; write latency is a primary node issue and read replicas do not offload write operations. Option D is wrong because Performance Insights is a monitoring and diagnostic tool that helps identify bottlenecks but does not directly reduce write latency; it provides visibility but no performance improvement.

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441

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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442

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

Matching DISTKEY on join columns ensures data is co-located, avoiding broadcasts.

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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