DEA-C01 Data Store Management Practice Test 35 — 15 Questions

Question 1

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

Accepted Answer

Amazon RDS automated backups. Amazon RDS automated backups (C) are a native RDS feature that automatically takes daily snapshots of the DB instance during the backup window and continuously archives transaction logs, enabling point-in-time recovery for RDS for SQL Server. AWS Backup (E) is a fully managed, policy-driven backup service that supports RDS as a protected resource, allowing you to define backup plans with schedules and retention rules that trigger automated RDS snapshots. AWS DMS (A) is a replication and migration service, not a backup mechanism, so it does not create restorable backups. AWS Data Pipeline (B) is an orchestration service for data movement and transformation, not a backup tool for RDS. Amazon S3 (D) is object storage that can hold exported backups, but it does not itself automatically back up an RDS for SQL Server DB instance.

Answer

AWS Database Migration Service (DMS)

Answer

AWS Data Pipeline

Answer

Amazon S3

Question 2

A data engineer needs to store semi-structured data (JSON logs) from thousands of IoT devices. The data must be schema-less, highly scalable, and support low-latency queries by device ID and timestamp. Which AWS service should the engineer use?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the correct choice because it is a fully managed NoSQL key-value and document database that natively supports semi-structured JSON data, schema-less design, and automatic scaling. Its partition key (device ID) and sort key (timestamp) enable low-latency, single-millisecond queries by device ID and timestamp, making it ideal for high-throughput IoT log ingestion.

Answer

Amazon RDS for PostgreSQL

Answer

Amazon Redshift

Answer

Amazon S3

Question 3

A company stores sensitive data in an Amazon S3 bucket. A compliance requirement mandates that all data must be encrypted at rest with a key that is automatically rotated every year. The company also needs to maintain an audit trail of who used the key. Which solution meets these requirements?

Accepted Answer

Use AWS KMS customer managed keys (SSE-KMS) with automatic key rotation enabled.. AWS KMS customer managed keys (SSE-KMS) with automatic key rotation enabled satisfies both requirements: it encrypts data at rest in S3 and automatically rotates the KMS key every year. Additionally, KMS integrates with AWS CloudTrail to log every API call (e.g., Decrypt, GenerateDataKey) that uses the key, providing an audit trail of who used the key and when.

Answer

Use customer-provided encryption keys (SSE-C) and rotate keys manually.

Answer

Use S3 managed keys (SSE-S3) and enable S3 server access logs.

Answer

Configure a bucket policy to enforce encryption using the 'aws:SecureTransport' condition.

Question 4

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?

Accepted Answer

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

Answer

Change the distribution style of large tables to DISTSTYLE ALL.

Answer

Migrate the cluster to Dense Compute node types.

Answer

Enable concurrency scaling to handle more concurrent queries.

Question 5

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?

Accepted Answer

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

Answer

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

Answer

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

Answer

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

Question 6

A company runs an Amazon RDS for PostgreSQL instance that stores financial data. The company requires point-in-time recovery (PITR) with a retention period of 35 days. Additionally, the company needs to create a new database from a specific snapshot every night for testing. Which combination of actions should the data engineer take to meet these requirements?

Accepted Answer

Enable automated backups with a 35-day retention period and create a manual snapshot each night for testing.. Automated backups in Amazon RDS for PostgreSQL support a maximum retention period of 35 days, which satisfies the PITR requirement. Additionally, creating a manual snapshot each night provides a stable, independent copy for testing without interfering with the automated backup schedule or the source database's performance.

Answer

Create a read replica and promote it to a new instance for testing each night.

Answer

Enable Multi-AZ and use the standby instance for testing.

Answer

Disable automated backups to reduce storage costs and take manual snapshots with 35-day retention.

Question 7

A media company stores video files in an S3 bucket. The files are processed by a fleet of EC2 instances that read the files, add watermarks, and write the output back to the same bucket. Recently, the processing jobs have been failing with '500 Internal Server Error' and '503 Slow Down' errors. The data engineer checks the S3 bucket metrics and sees that the PUT/GET request rate is consistently above 5,500 requests per second for a single prefix. The engineer needs to resolve the errors with minimal changes to the application code. Which course of action should the engineer take?

Accepted Answer

Modify the application to add a random hash prefix to the object keys to distribute load across multiple prefixes.. S3 automatically partitions request traffic by key prefix, and each partition supports roughly 3,500 PUT/COPY/POST/DELETE or 5,500 GET/HEAD requests per second. When a single prefix exceeds these limits, S3 returns 503 Slow Down errors. Adding a random hash prefix to object keys distributes the load across many prefixes, each with its own request rate limit, thereby eliminating throttling without changing the core application logic. This is the recommended best practice for high-throughput workloads.

Answer

Use S3 Batch Operations to process the files.

Answer

Increase the number of EC2 instances to process files in parallel.

Answer

Enable S3 Transfer Acceleration on the bucket to improve throughput.

Question 8

A company stores its application logs in an Amazon S3 bucket. The logs are accessed frequently for the first 30 days, after which they are rarely accessed but must be retained for 7 years for compliance. The company wants to optimize storage costs while maintaining immediate retrieval availability for the first 30 days and the ability to retrieve logs within 12 hours after that. Which lifecycle policy should the data engineer configure?

Accepted Answer

Transition objects to S3 Standard-IA after 30 days and then to S3 Glacier Deep Archive after 1 year.. It uses S3 Standard-IA for the first 30 days (frequent access, immediate retrieval) and then transitions to S3 Glacier Deep Archive after 1 year, which provides retrieval within 12 hours at the lowest cost for long-term retention. This meets the compliance requirement of 7-year retention while optimizing costs by moving data to progressively cheaper storage classes based on access patterns.

Answer

Delete objects after 30 days to minimize storage costs.

Answer

Transition objects to S3 One Zone-IA after 30 days and delete after 7 years.

Answer

Transition objects to S3 Glacier Flexible Retrieval after 30 days and delete after 7 years.

Question 9

A company is migrating an on-premises MySQL database to Amazon RDS for MySQL. The database is 500 GB in size. The migration must have minimal downtime and must be completed within a week. Which AWS service should the data engineer use to perform the migration?

Accepted Answer

AWS Database Migration Service (DMS).. AWS Database Migration Service (DMS) is the correct choice because it is specifically designed for migrating databases to AWS with minimal downtime. DMS can perform a full load of the 500 GB MySQL database and then continuously replicate ongoing changes from the on-premises source to the Amazon RDS target using change data capture (CDC), allowing the migration to complete within a week with near-zero downtime.

Answer

Amazon S3 Transfer Acceleration.

Answer

AWS Snowball Edge.

Answer

AWS DataSync.

Question 10

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.)

Accepted Answer

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

Answer

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

Answer

Enable S3 Transfer Acceleration for the bucket.

Answer

Enable object-level access control lists (ACLs).

Question 11

A company runs an Amazon RDS for PostgreSQL instance for an OLTP application. The database size is 500 GB. The company wants to minimize downtime during backups and ensure point-in-time recovery (PITR) for the last 7 days. Which TWO features should the company use? (Choose TWO.)

Accepted Answer

Enable Multi-AZ deployment for high availability.. Multi-AZ deployment for Amazon RDS provides high availability by automatically provisioning and maintaining a synchronous standby replica in a different Availability Zone. This minimizes downtime during backups by allowing automated backups to be taken from the standby instance, eliminating I/O suspension on the primary. Option C is correct because enabling automated backups with a retention period of 7 days enables point-in-time recovery (PITR) within that window, restoring the database to any second within the retention period using transaction logs.

Answer

Create a read replica in a different Availability Zone.

Answer

Create daily manual snapshots and copy them to another region.

Answer

Enable Enhanced Monitoring to track backup progress.

Question 12

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.)

Accepted Answer

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

Answer

Convert the data to JSON format for better schema evolution.

Answer

Use Glue DataBrew to clean the data before querying.

Question 13

A large e-commerce company uses Amazon DynamoDB to store shopping cart data. The table has a partition key of 'user_id' and a sort key of 'item_id'. The application performs frequent updates to the 'quantity' attribute for items in a user's cart. Recently, the operations team noticed that write requests are being throttled during peak shopping hours. The table is provisioned with 10,000 write capacity units (WCUs) and uses DynamoDB Accelerator (DAX) for read caching. The data engineer suspects that the throttling is due to hot partitions. The application uses a single AWS SDK client configured with retries. After reviewing the Amazon CloudWatch metrics, the engineer sees that the WriteThrottleEvents metric spikes for a few partition keys. The table has a high number of partitions. What should the data engineer do to resolve the throttling issue with minimal application changes?

Accepted Answer

Configure DynamoDB Auto Scaling with a maximum WCU setting of 20,000 and a target utilization of 70%.. DynamoDB Auto Scaling can dynamically adjust write capacity in response to traffic patterns, reducing throttling on hot partitions without requiring application changes. Option A is incorrect because permanently increasing WCUs does not adapt to variable demand and may lead to over-provisioning. Option B (Global Tables) replicates data across regions but does not increase write capacity for a single table, so it does not resolve hot partition throttling. Option C (DAX) is a read cache and does not offload write traffic; it only improves read performance.

Answer

Increase the provisioned write capacity to 20,000 WCUs permanently.

Answer

Enable DynamoDB Global Tables to distribute writes across regions.

Answer

Add more nodes to the DAX cluster to offload write traffic.

Question 14

A data engineer is designing a data lake on Amazon S3. The team wants to optimize query performance and reduce storage costs for a large dataset of JSON logs that are queried frequently by Amazon Athena. The logs are currently stored as uncompressed JSON files, each around 1 GB, in a single prefix. The engineer needs to improve query performance and reduce costs without changing the data format. Which action should the engineer take?

Accepted Answer

Compress the JSON files with gzip and split them into smaller files of approximately 128 MB.. Compressing the JSON files with gzip reduces the storage footprint and the amount of data scanned by Athena, lowering costs. Splitting the large files into smaller, more manageable sizes allows Athena to process them in parallel, significantly improving query performance. This solution respects the requirement to keep the data in JSON format and directly addresses both performance and cost concerns.

Answer

Convert the JSON files to Apache Parquet and partition the data by date.

Answer

Move the data to Amazon Redshift and query it using Redshift Spectrum.

Answer

Enable S3 Transfer Acceleration on the bucket to speed up data retrieval.

Question 15

A data engineer manages a large Amazon S3 data lake with millions of small JSON files ingested daily. Amazon Athena queries against this data lake are slow and costly due to high per-query data scanned. The engineer wants to optimize the storage layout to improve query performance and reduce cost, while keeping the data queryable in place. Which solution should the engineer implement?

Accepted Answer

Use AWS Glue ETL to compact the small JSON files into larger Parquet files partitioned by commonly filtered columns, then update the AWS Glue Data Catalog.. Compacting small files into larger Parquet files and partitioning by frequently filtered columns directly reduces the amount of data scanned by Athena, improving performance and lowering cost. Parquet's columnar format allows Athena to read only the columns needed, and partitioning enables partition pruning. Updating the Data Catalog ensures queries use the optimized layout. The other options do not address the core issues of small files and inefficient format.

Answer

Enable S3 Transfer Acceleration on the bucket to speed up data retrieval by Athena.

Answer

Convert the data to CSV format and add more columns to the AWS Glue Data Catalog table.

Answer

Move the data to Amazon Redshift and query it using Redshift Spectrum.