DEA-C01 Data Store Management Practice Test 3 — 15 Questions

Question 1

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?

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 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.

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AWS S3 Transfer Acceleration

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AWS Glue

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AWS Snowball Edge

Question 2

A data engineer is troubleshooting a slow-running query on an Amazon Redshift cluster. The query involves joining two large tables. The engineer notices that the query plan shows a large number of distribution and broadcast operations. Which design change would most likely improve query performance?

Accepted Answer

Change the distribution style of both tables to KEY on the join column. Changing the distribution style of both tables to KEY on the join column ensures that rows with the same join key value are co-located on the same node. This eliminates the need for expensive broadcast or redistribution operations during the join, as Redshift can perform the join locally on each slice without moving data across the network.

Answer

Change the distribution style of both tables to ALL

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Change the distribution style of both tables to EVEN

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Add a sort key on the join column

Question 3

A company uses Amazon DynamoDB for a gaming leaderboard. The table has a partition key of 'game_id' and a sort key of 'score'. The read capacity is provisioned at 1000 RCUs. During peak hours, users report high latency when querying the top 10 scores for a specific game. The DynamoDB metrics show ConsumedReadCapacityUnits averaging 800 but occasional throttling. What is the most likely cause and solution?

Accepted Answer

The hot game_id partition is exceeding its throughput; add DynamoDB Accelerator (DAX) to cache reads. The hot game_id partition is exceeding its provisioned throughput because DynamoDB distributes RCUs evenly across partitions, and a single partition can only handle up to (1000 RCUs / number of partitions) per second. When a specific game_id becomes popular, all reads hit the same partition, causing throttling despite low overall consumed capacity. Adding DynamoDB Accelerator (DAX) caches the top 10 scores for that partition, reducing read pressure and eliminating throttling without increasing RCUs.

Answer

Create a global secondary index with the same key schema to distribute reads

Answer

Remove the sort key and use a global secondary index

Answer

Increase the provisioned RCUs to 2000

Question 4

A data engineer runs the above AWS CLI command and receives the output. The object is part of an S3 Lifecycle policy that transitions objects to Glacier Instant Retrieval after 30 days. The object was created on January 1, 2023. Why is the object still in STANDARD_IA storage class?

Accepted Answer

The object has not reached the transition age of 30 days yet. The S3 Lifecycle rule transitions objects to Glacier Instant Retrieval after 30 days, but the object was created on January 1, 2023, and the current date (implied by the command output) is before January 31, 2023. The transition age is calculated from the object's LastModified date, not from any other timestamp, and the object must be at least 30 days old before S3 applies the transition. Since the object is only 20 days old (as of January 21, 2023, based on the output showing STANDARD_IA), it has not yet met the 30-day threshold.

Answer

The Lifecycle policy has a filter that excludes this object's prefix

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Versioning is enabled and the current version is not the oldest

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The metadata timestamp is used for lifecycle transitions instead of LastModified

Question 5

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.)

Accepted Answer

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

Answer

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

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

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

Question 6

A company runs a real-time analytics platform using Amazon Kinesis Data Streams with a shard count of 10. The data is consumed by an AWS Lambda function that writes to an Amazon DynamoDB table. The DynamoDB table has a partition key of 'user_id' and a sort key of 'timestamp'. The table is provisioned with 5000 RCUs and 5000 WCUs. Recently, the application experienced increased write latency and throttling errors (ProvisionedThroughputExceededException) on the DynamoDB table. The CloudWatch metrics show that ConsumedWriteCapacityUnits averages 4500 with occasional spikes to 6000. The Lambda function’s concurrency is set to 1000. The data engineer suspects the issue is due to hot partitions. Upon investigation, the engineer finds that a small number of users generate a disproportionately large amount of data. Which course of action would best resolve the throttling while minimizing cost?

Accepted Answer

Enable DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users. The root cause is hot partitions caused by a small number of high-volume users. Enabling DynamoDB adaptive capacity allows the table to automatically adjust throughput to accommodate uneven access patterns, but the key fix is write sharding — adding a random or calculated suffix to the partition key for those high-volume users. This distributes writes across multiple physical partitions, eliminating the hot spot without requiring a global increase in provisioned capacity, thus resolving throttling while minimizing cost.

Answer

Increase the provisioned WCUs to 10000 to handle spikes

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Switch the DynamoDB table to on-demand capacity mode

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Reduce the Lambda function concurrency to 100 to limit write requests

Question 7

A data engineering team is designing a data lake on Amazon S3 with a folder structure that separates raw, transformed, and curated data. The team needs to implement lifecycle policies to minimize storage costs while ensuring that data in the 'raw' zone is retained for 90 days before being moved to Amazon S3 Glacier Deep Archive. Additionally, data in the 'curated' zone should be deleted after 365 days. What is the MOST cost-effective way to achieve these requirements?

Accepted Answer

Create a single lifecycle policy with two rules: one to transition raw zone objects to S3 Glacier Deep Archive after 90 days, and another to delete curated zone objects after 365 days.. A single S3 lifecycle policy can contain multiple rules, each applying to different prefixes. This allows you to transition raw zone objects to S3 Glacier Deep Archive after 90 days and delete curated zone objects after 365 days, minimizing storage costs without needing separate policies. S3 lifecycle policies are evaluated per object based on creation date, and using one policy reduces management overhead.

Answer

Configure S3 Intelligent-Tiering on both prefixes with automatic archiving to Glacier Deep Archive after 90 days in raw and deletion after 365 days in curated.

Answer

Create separate lifecycle policies for each prefix: one to transition raw to S3 Glacier Deep Archive after 90 days, another to delete curated after 365 days.

Answer

Create a lifecycle policy that transitions raw zone data to S3 Standard-IA after 90 days and deletes curated zone data after 365 days.

Question 8

A data engineer is setting up an Amazon Redshift cluster for a data warehouse. The cluster will store historical sales data and support complex analytical queries. To optimize query performance and manage storage, the engineer needs to choose appropriate distribution styles and sort keys for a large fact table 'sales' and several dimension tables. Which TWO of the following design decisions are BEST practices?

Accepted Answer

Use interleaved sort keys on columns that are frequently used in filter predicates (e.g., date, region, product).. Interleaved sort keys in Amazon Redshift give equal weight to each column in the sort key, making them ideal for queries with filter predicates on multiple columns (e.g., date, region, product). This design optimizes zone maps and minimizes the amount of data scanned, significantly improving query performance for complex analytical workloads on large fact tables.

Answer

Use ALL distribution for the 'sales' fact table to replicate data to every node and avoid data movement.

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Use a compound sort key with the most frequently filtered column first.

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Choose AUTO distribution style for all tables and let Amazon Redshift automatically assign distribution.

Question 9

A data engineer ran the above CLI command to describe an Amazon DynamoDB table named 'Orders'. The table has a key schema with 'OrderID' as the partition key and 'CustomerID' as the sort key. The table currently has no items. The engineer wants to add a new attribute 'OrderDate' and then query all orders for a specific customer within a date range. Which of the following actions is the MOST efficient approach to support this query pattern?

Accepted Answer

Create a Global Secondary Index (GSI) with 'CustomerID' as partition key and 'OrderDate' as sort key.. A Global Secondary Index (GSI) allows querying on a different partition key ('CustomerID') and sort key ('OrderDate') without altering the base table's key schema. This supports efficient range queries on 'OrderDate' for a specific customer, as GSIs provide a separate index with its own provisioned throughput and can be created on existing tables with items. The base table's primary key remains unchanged, and the GSI enables the desired query pattern with low latency.

Answer

Modify the table's primary key to include 'OrderDate' as an additional sort key.

Answer

Use a Scan operation with a filter expression on 'CustomerID' and 'OrderDate' to retrieve the data.

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Create a Local Secondary Index (LSI) with 'CustomerID' as partition key and 'OrderDate' as sort key.

Question 10

Arrange the steps to set up cross-region replication for an S3 bucket.

Accepted Answer

Enable versioning on source and destination, then create the destination bucket, then add a replication rule, then assign an IAM role.. First, enable versioning on source and destination. Then create the destination bucket, add a replication rule, and assign an IAM role for replication.

Answer

Create the destination bucket first, then enable versioning on source and destination, then add a replication rule, then assign an IAM role.

Answer

Enable versioning on source and destination, then add a replication rule, then create the destination bucket, then assign an IAM role.

Answer

Enable versioning on source and destination, then create the destination bucket, then assign an IAM role, then add a replication rule.

Question 11

Order the steps to set up an Amazon EMR cluster for processing data in S3 using Spark.

Accepted Answer

Prepare the S3 bucket, then launch the EMR cluster with Spark, then configure instances, then submit the job, then terminate the cluster.. First, prepare the S3 bucket. Then launch the EMR cluster with Spark, configure instances, submit the job, and terminate.

Answer

Launch the EMR cluster with Spark, then prepare the S3 bucket, then configure instances, then submit the job, then terminate the cluster.

Answer

Prepare the S3 bucket, then configure instances, then launch the EMR cluster with Spark, then submit the job, then terminate the cluster.

Answer

Submit the job, then launch the EMR cluster with Spark, then configure instances, then prepare the S3 bucket, then terminate the cluster.

Question 12

Match each AWS Glue component to its role.

Accepted Answer

Data Catalog: Central metadata repository for storing table definitions and schema.. AWS Glue components work together for ETL. The Data Catalog stores metadata, Crawlers populate the catalog, Classifiers infer schema, and ETL Jobs execute transformations. Connections provide connectivity but are not listed here.

Answer

Data Catalog: Executes transformation scripts.

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Crawler: Stores configuration for connecting to data stores.

Question 13

Match each AWS monitoring tool to its primary use.

Accepted Answer

CloudWatch: Monitor metrics, logs, and alarms. 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.

Answer

CloudWatch: Auditing API activity

Answer

CloudTrail: Monitor metrics, logs, and alarms

Question 14

A company uses Amazon S3 to store sensitive customer data. The security team requires that all objects uploaded to a specific bucket be encrypted at rest using AWS KMS with a customer managed key. Which bucket policy statement should be applied to enforce this requirement?

Accepted Answer

Deny s3:PutObject unless s3:x-amz-server-side-encryption-aws-kms-key-id equals the specific KMS key ARN. The security team requires encryption at rest using AWS KMS with a customer managed key. The bucket policy must deny any s3:PutObject request that does not include the s3:x-amz-server-side-encryption-aws-kms-key-id condition key set to the specific KMS key ARN. This ensures that only objects encrypted with the designated customer managed key are allowed, enforcing the encryption requirement at the bucket policy level.

Answer

Deny s3:PutObject unless s3:x-amz-server-side-encryption is present

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Allow s3:PutObject only if s3:x-amz-server-side-encryption is present

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Deny s3:PutObject unless s3:x-amz-server-side-encryption equals AES256

Question 15

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and needs to be partitioned by year, month, day, and event type for efficient querying with Amazon Athena. Which S3 key prefix structure is most appropriate?

Accepted Answer

s3://bucket/event_type=events/year=2024/month=01/day=01/data.parquet. Uses Hive-style partitioning (event_type=events/year=2024/month=01/day=01), which Athena and other query engines natively support. This structure allows Athena to perform partition pruning, reading only the relevant directories based on WHERE clause filters, significantly reducing data scanned and improving query performance. Option D also uses Hive-style partitioning but with a different order of partition keys (day, month, year). While still valid, this non-standard order may cause issues with automatic partition discovery when using MSCK REPAIR TABLE, which expects the partition order to match the table definition. Therefore, option C is the most appropriate because it follows the common convention of listing partitions from coarse to fine granularity (year > month > day) and can be easily loaded into Athena without additional configuration.

Answer

s3://bucket/events/2024-01-01/event_type=data.parquet

Answer

s3://bucket/2024/01/01/event_type/events/data.parquet

Answer

s3://bucket/day=01/month=01/year=2024/event_type=events/data.parquet