DEA-C01 Data Store Management Practice Test 21 — 15 Questions

Question 1

A data engineer needs to store semi-structured JSON data from IoT devices. The data is written once, read rarely, but must be queryable using SQL. The storage cost must be minimized. Which storage solution should the engineer choose?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Store JSON files in Amazon S3 and use Amazon Athena for queries. Amazon S3 provides the lowest-cost storage for data that is written once and rarely read, while Amazon Athena enables serverless SQL querying directly on JSON files stored in S3. This combination minimizes storage costs because S3 charges only for the data stored and retrieval, with no minimum fees or provisioning required, and Athena charges only for the data scanned per query. The workload's write-once, read-rarely pattern aligns perfectly with S3's durability and lifecycle policies, making it the most cost-effective choice.

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Store JSON in Amazon Redshift as SUPER data type

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Store JSON in an Amazon RDS for MySQL table

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Store JSON documents in Amazon DynamoDB and use PartiQL for queries

Question 2

A company uses Amazon DynamoDB as the primary data store for a web application. The application experiences occasional throttling on write requests. The data engineer needs to implement a solution that handles throttling gracefully without losing data. Which approach should the engineer use?

Accepted Answer

Implement exponential backoff in the application's write retry logic. Implementing exponential backoff in the application's write retry logic is the standard AWS-recommended approach for handling DynamoDB throttling (ProvisionedThroughputExceededException). Exponential backoff gradually increases the wait time between retries, reducing the retry rate and allowing the throttling condition to subside, while ensuring no write data is lost as long as the retries eventually succeed. This approach is lightweight, requires no additional AWS services, and aligns with best practices for building resilient applications against DynamoDB throttling.

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Increase the provisioned write capacity to a higher value

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Use an Amazon SQS queue to buffer write requests before sending to DynamoDB

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Enable DynamoDB Accelerator (DAX) to cache writes

Question 3

A financial services company stores transaction data in Amazon RDS for PostgreSQL. The company requires that all changes to the database be logged for audit purposes, including before and after images of updated rows. Which feature should the data engineer enable?

Accepted Answer

Set up logical replication using pglogical or native publication/subscription. Logical replication, using either pglogical or native PostgreSQL publication/subscription, captures row-level changes (INSERT, UPDATE, DELETE) and can include both the old and new values of updated rows. This meets the audit requirement for before-and-after images, as logical replication decodes the write-ahead log (WAL) to produce a change stream that includes full row snapshots.

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Enable automated backups and export logs to Amazon S3

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Enable Enhanced Monitoring and publish logs to CloudWatch Logs

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Enable Multi-AZ deployment and read replicas

Question 4

A data engineer is setting up Amazon S3 bucket policies for a data lake. Which TWO statements are true regarding S3 bucket policies? (Choose TWO.)

Accepted Answer

Bucket policies can grant access to accounts in other AWS Organizations. S3 bucket policies can grant cross-account access to principals in other AWS accounts, including those in different AWS Organizations, by specifying the target account ID or organization ID in the Principal element. This enables centralized data lake access management across organizational boundaries without requiring IAM roles or resource-based policies in each account.

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Bucket policies are the only way to control access to S3

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Bucket policies can be applied to individual objects

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The Principal element in a bucket policy is optional

Question 5

A data engineer applies the above IAM policy to a user. The user attempts to upload an object to the bucket 'my-data-lake' without specifying server-side encryption. What will happen?

Accepted Answer

The upload fails because the condition requires encryption. The IAM policy includes a condition that requires the `s3:x-amz-server-side-encryption` header to be present with a value of `AES256`. When the user attempts to upload an object without specifying server-side encryption, the condition is not satisfied, so the `s3:PutObject` permission is denied. This causes the upload to fail, regardless of any bucket default encryption settings or bucket policies.

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The upload fails only if the bucket has a default encryption setting

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The upload succeeds if the bucket policy allows unencrypted uploads

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The upload succeeds because the policy allows s3:PutObject

Question 6

A data engineer runs the above CLI command to describe the DynamoDB table 'Orders'. The table has a partition key 'OrderID' and sort key 'CustomerID'. Which query operation is most efficient for retrieving all orders for a specific customer?

Accepted Answer

Create a Global Secondary Index on CustomerID and query the index. A Global Secondary Index (GSI) on CustomerID allows you to query efficiently using CustomerID as the partition key, avoiding a full table scan. Since the base table's primary key is (OrderID, CustomerID), you cannot directly query by CustomerID alone; a GSI provides an alternative access pattern optimized for this query.

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Query the table using CustomerID as the partition key

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Scan the table and filter by CustomerID

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Use GetItem with CustomerID as the key

Question 7

A data engineer is troubleshooting an access denied error when an AWS Lambda function tries to decrypt an object encrypted with the KMS key 'abc123'. The Lambda function's execution role has the above policy attached. What is the likely cause of the error?

Accepted Answer

The KMS key policy does not grant the Lambda role decrypt permission. The error occurs because KMS requires both the IAM policy and the key policy to grant the necessary permissions. While the IAM policy attached to the Lambda execution role includes kms:Decrypt, the KMS key policy for 'abc123' does not explicitly grant the Lambda role permission to call kms:Decrypt. Since KMS key policies act as a resource-based policy, they must allow the principal (the Lambda role) to perform the action; otherwise, the request is denied even if the IAM policy allows it.

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The Deny statement blocks all decrypt requests

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The Lambda function does not have permission to call kms:GenerateDataKey

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The IAM policy does not include kms:Decrypt permission

Question 8

A company wants to store data from thousands of IoT devices with varying data rates. The data must be stored in a schema-on-read fashion and support SQL queries. Which AWS service should be used?

Accepted Answer

Amazon S3 with Amazon Athena. Amazon S3 stores data in its native format (e.g., JSON, Parquet) without requiring a predefined schema, enabling schema-on-read. Amazon Athena uses Presto-based SQL to query data directly from S3, making it ideal for IoT data with varying rates and ad-hoc SQL analysis without provisioning servers.

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Amazon RDS for MySQL

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Amazon DynamoDB

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Amazon Redshift

Question 9

A company runs a data warehouse on Amazon Redshift. Queries are slow, and the team suspects data distribution is skewed. Which approach would best help identify distribution skew?

Accepted Answer

Query the SVV_DISKUSAGE table to examine data distribution across slices. The SVV_DISKUSAGE table provides per-slice data distribution information, allowing you to identify skew by comparing the number of blocks allocated to each slice for a given table. In Amazon Redshift, data is distributed across slices based on the distribution key, and significant variation in block counts across slices indicates distribution skew, which can cause query performance degradation due to uneven workload distribution.

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Check the STL_LOAD_ERRORS table for load failures

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Query the SVV_TABLE_INFO table to see table size

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Review the WLM configuration in the parameter group

Question 10

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?

Accepted Answer

Amazon DynamoDB. 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.

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Amazon RDS for PostgreSQL

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Amazon S3

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Amazon ElastiCache for Redis

Question 11

A company is migrating an on-premises MongoDB database to Amazon DocumentDB. The migration must have minimal downtime. Which service should be used to perform the migration?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is the correct choice because it supports continuous replication from MongoDB to Amazon DocumentDB using change data capture (CDC), enabling near-zero downtime migrations. DMS can perform a full load of existing data and then apply ongoing changes from the source MongoDB oplog, keeping the target DocumentDB synchronized until the cutover.

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AWS Glue

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AWS DataSync

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Amazon S3 Transfer Acceleration

Question 12

A company uses Amazon Redshift for analytics. The data engineering team wants to improve query performance for frequently used aggregate queries. Which TWO actions would help achieve this?

Accepted Answer

Use distribution keys to collocate data on the same node slices. Option B is correct because choosing an appropriate distribution key collocates matching rows on the same node slices, so joins and aggregations can be processed locally without expensive data redistribution (broadcast or shuffle) across the cluster, directly speeding up frequently used aggregate queries. Option D is correct because defining appropriate sort keys physically orders data on disk by the key columns, enabling zone maps to skip irrelevant blocks and allowing efficient range-restricted scans and merge joins, which reduces the data read for aggregate queries. Option A is not correct because adding WLM query queues only changes concurrency and memory allocation among query groups; it does not by itself make an individual aggregate query faster. Option C is not correct because VACUUM reclaims space from deleted rows and re-sorts data, which is a maintenance operation rather than a design change that improves aggregate query performance. Option E is not correct because adding nodes increases cluster capacity and parallelism but does not address the underlying data layout, so poorly distributed or unsorted tables can still cause slow aggregate queries.

Answer

Increase the number of WLM query queues

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Run the VACUUM command to reclaim space from deleted rows

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Increase the number of nodes in the cluster

Question 13

A company stores sensitive data in Amazon S3. The security team requires encryption at rest and that the encryption keys are managed by the company using AWS KMS. The data is frequently accessed by multiple AWS services. Which THREE steps should be taken to meet these requirements?

Accepted Answer

Configure the KMS key policy to allow the necessary AWS services to use the key for decryption. The security team requires that encryption keys be managed by the company using AWS KMS, and that multiple AWS services can access the data. To allow those services to decrypt objects encrypted with a customer-managed KMS key, the KMS key policy must explicitly grant the necessary AWS services (e.g., AWS Lambda, Amazon Athena) permission to use the key for decryption (kms:Decrypt). Without this policy, even if the bucket is configured for SSE-KMS, the services will fail to read the encrypted objects.

Answer

Use client-side encryption with the KMS key before uploading to S3

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Enable default encryption on the S3 bucket using SSE-S3

Question 14

A company is designing a data lake on Amazon S3. The data includes CSV files, Parquet files, and images. The data engineering team needs to catalog the metadata and enable SQL queries. Which TWO AWS services should be used together?

Accepted Answer

Amazon Athena. Amazon Athena is correct because it is a serverless interactive query service that can directly query data stored in Amazon S3 using standard SQL, without needing to load or transform data. AWS Glue is correct because it provides a fully managed data catalog (AWS Glue Data Catalog) that stores metadata about the data lake's schema, partitions, and locations, which Athena can use to discover and query the data efficiently.

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Amazon EMR

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Amazon Redshift Spectrum

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Amazon QuickSight

Question 15

A company uses Amazon DynamoDB for a gaming application. The application experiences throttling during peak hours. The table's read and write capacity is provisioned. Which TWO actions can reduce throttling?

Accepted Answer

Enable DynamoDB auto scaling for the table. DynamoDB auto scaling (Option B) automatically adjusts the provisioned read and write capacity based on actual traffic patterns, preventing throttling during peak hours without manual intervention. This is the correct action because it dynamically increases capacity when demand spikes and reduces it during low traffic, directly addressing the throttling issue.

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Enable TTL (time to live) on the table to automatically delete old items

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Increase the provisioned read capacity units (RCUs)

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Add a DynamoDB Global Table for the table