DEA-C01 Mock Exam 82 — 25 Questions

Question 1

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources in Parquet format, and the schema evolves over time. Which approach allows querying the data with Amazon Athena while supporting schema evolution?

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

Accepted Answer

Use AWS Glue Data Catalog with crawlers to automatically update the table schema.. AWS Glue Data Catalog with crawlers automatically infers and updates the table schema as new Parquet files with evolving schemas are ingested into S3. This allows Athena to query the data using the latest schema without manual intervention, making it the ideal solution for schema evolution in a data lake.

Answer

Define Hive-style partitions in Athena and manually update the schema.

Answer

Use S3 Select to query the data directly without a schema.

Answer

Use Amazon Redshift Spectrum with external tables and update the schema manually.

Question 2

A company uses Amazon Redshift for its data warehouse. The data engineer notices that queries are slow on a large table that is frequently filtered on a column 'transaction_date'. Which optimization technique best improves query performance?

Accepted Answer

Set the sort key to 'transaction_date'.. Setting the sort key to 'transaction_date' organizes the table data physically by that column, which allows Redshift to use zone maps to skip blocks that don't match query filters. This dramatically reduces the amount of data scanned for range-restricted queries on 'transaction_date', improving query performance.

Answer

Apply compression encoding to 'transaction_date'.

Answer

Set the distribution key to 'transaction_date'.

Answer

Run VACUUM on the table.

Question 3

A company uses Amazon Redshift for a data warehouse. They notice that queries are slow due to heavy data skew. Which optimization technique should be applied first?

Accepted Answer

Set an appropriate distribution style. Data skew occurs when rows are distributed unevenly across Redshift slices, causing some nodes to process far more data than others. Setting an appropriate distribution style (e.g., KEY, EVEN, or ALL) redistributes the data to balance the workload, directly addressing the root cause of the slowness. This is the first optimization to apply because skew is a fundamental distribution issue that other tuning steps cannot fix.

Answer

Configure workload management (WLM) queues

Answer

Define sort keys on frequently filtered columns

Answer

Apply compression encodings to columns

Question 4

A company is using AWS DMS to replicate data from an on-premises Oracle database to Amazon RDS for MySQL. The replication is working, but the target table has a different schema. Which DMS feature should be used to transform the source schema to match the target?

Accepted Answer

Use DMS transformation rules. AWS DMS transformation rules allow you to modify the schema, table, or column names and data types during the migration process. This feature is specifically designed to handle schema transformations within the DMS task itself, enabling you to map the source Oracle schema to the target MySQL schema without external tools or services.

Answer

Use AWS Schema Conversion Tool (SCT)

Answer

Use AWS Glue ETL jobs

Answer

Use AWS Lambda triggers

Question 5

A company uses Amazon RDS for MySQL as a source for AWS DMS. The replication tasks are failing due to large transactions on the source. The team wants to reduce the impact of large transactions on DMS. Which THREE actions should the team take?

Accepted Answer

Increase the size of the source RDS instance and enable binary logging with ROW format.. To reduce the impact of large transactions on AWS DMS when using Amazon RDS for MySQL as a source, the recommended actions are: increasing the source RDS instance size and enabling binary logging with ROW format (option B) to better handle large transactions and support CDC; enabling BatchApply (option C) to reduce the time to apply changes by batching them; and tuning the DMS task to use a larger memory limit while adjusting the transaction size (option E) to improve memory management. Option A is incorrect because increasing parallel threads on the source does not directly mitigate large transaction impact; DMS manages parallelism internally. Option D is incorrect because using 'full load only' would stop CDC, making it unsuitable for ongoing replication.

Answer

Increase the number of parallel threads on the source.

Answer

Use the 'full load only' migration type.

Question 6

A data engineer needs to store semi-structured JSON transaction logs for analytics. The logs are written once and rarely accessed. The storage must be cost-effective. Which AWS service should be used?

Accepted Answer

Amazon S3. Amazon S3 is the correct choice because it provides highly durable, cost-effective object storage ideal for semi-structured JSON transaction logs that are written once and rarely accessed. S3's lifecycle policies can automatically transition such infrequently accessed data to S3 Glacier or S3 Glacier Deep Archive for even lower storage costs, making it the most economical option for this use case.

Answer

Amazon DynamoDB

Answer

Amazon RDS

Answer

Amazon Redshift

Question 7

A data engineer needs to store JSON documents that are accessed by a serverless application using AWS Lambda. The documents are frequently updated and need low latency (single-digit milliseconds) for read and write operations. Which AWS service should the engineer use?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is a fully managed NoSQL key-value and document database that provides single-digit millisecond latency for read and write operations at any scale. It natively supports JSON documents, integrates directly with AWS Lambda via the AWS SDK, and handles frequent updates efficiently through its auto-scaling and on-demand capacity modes, making it ideal for serverless applications requiring low-latency data access.

Answer

Amazon ElastiCache for Redis

Answer

Amazon S3 (with S3 Select)

Answer

Amazon RDS for MySQL

Question 8

A data engineer needs to store and analyze time-series data from IoT devices. The data volume is 10 GB per day, and the queries are mostly on the most recent 7 days of data. The engineer wants to minimize storage costs while retaining historical data for 1 year. Which combination of AWS services is most cost-effective?

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series data, offering automatic tiering between in-memory (for recent 7 days) and magnetic stores (for historical data up to 1 year). This matches the query pattern (mostly recent 7 days) and retention requirement (1 year) while minimizing storage costs through its serverless, pay-per-query model. Timestream also supports time-series-specific functions like interpolation and smoothing, making it more efficient than general-purpose databases for this workload.

Answer

Amazon DynamoDB with TTL and S3 for archival

Answer

Amazon Redshift

Answer

Amazon RDS with MySQL

Question 9

A company uses DynamoDB with provisioned capacity and experiences throttling on a table during peak hours. The data engineer notices that the table has a partition key with high cardinality and the workload is read-heavy. Which action would best resolve the throttling?

Accepted Answer

Enable DynamoDB Auto Scaling for the table.. DynamoDB Auto Scaling adjusts the provisioned read capacity units (RCUs) based on actual traffic patterns, preventing throttling during peak hours without manual intervention. Since the table has high-cardinality partition keys and is read-heavy, throttling is likely due to insufficient RCUs, which Auto Scaling dynamically increases to match demand.

Answer

Switch the table to on-demand capacity mode.

Answer

Increase the provisioned write capacity units.

Answer

Add a global secondary index (GSI) to distribute reads.

Question 10

A company wants to enable automatic encryption for all new objects written to an S3 bucket. The bucket has existing objects that are unencrypted. Which solution meets these requirements with the least operational overhead?

Accepted Answer

Enable default encryption on the bucket using SSE-S3. Enabling default encryption on the bucket using SSE-S3 automatically encrypts all new objects written to the bucket, with minimal operational overhead. Option A is incorrect because lifecycle policies do not encrypt objects; they manage transitions and deletions. Option C is redundant since bucket default encryption already denies unencrypted writes via policy, but the policy itself is additional overhead and not automatic. Option D is incorrect because S3 Batch Operations require manual initiation and only encrypt existing objects, not new ones automatically.

Answer

Configure a lifecycle policy to transition objects to a new bucket with encryption

Answer

Use S3 server-side encryption with S3 managed keys (SSE-S3) and apply a bucket policy that denies writes without encryption

Answer

Use S3 Batch Operations to copy existing objects with SSE-S3

Question 11

A company is ingesting log files from EC2 instances into CloudWatch Logs and then wants to deliver them to S3 for long-term storage and analysis. The data engineer needs to ensure the logs are delivered to S3 within 5 minutes of being generated. Which approach meets this requirement?

Accepted Answer

Use CloudWatch Logs subscription filter with Kinesis Data Firehose to deliver to S3. CloudWatch Logs subscription filters can stream log data in near real-time to Kinesis Data Firehose, which then delivers the data to S3 with a buffer interval configurable down to 60 seconds, easily meeting the 5-minute requirement. This approach provides the lowest latency for automated, continuous delivery without custom code.

Answer

Configure a CloudWatch Logs metric filter and invoke a Lambda function to write to S3

Answer

Use CloudWatch Logs Insights to query logs and save results to S3

Answer

Use the CloudWatch Logs export to S3 feature

Question 12

A company uses AWS DMS to replicate data from an on-premises Oracle database to Amazon RDS for MySQL. The full load completes successfully, but ongoing replication (CDC) is failing with a 'Failed to add supplemental logging' error. What should the data engineer do to resolve this issue?

Accepted Answer

Enable supplemental logging on the source Oracle database manually.. The 'Failed to add supplemental logging' error occurs because AWS DMS requires supplemental logging on the source Oracle database for CDC to capture changes. DMS typically attempts to add supplemental logging automatically, but if it fails (e.g., due to insufficient privileges), the engineer must enable it manually. Option A is correct: enabling supplemental logging manually resolves the issue. Option B is incorrect because recreating the endpoint does not address the missing supplemental logging. Option C is incorrect because the target MySQL engine version is unrelated to the source Oracle logging requirement. Option D is incorrect because increasing the task log interval only affects log output frequency, not the underlying logging configuration.

Answer

Recreate the DMS endpoint for the source database with a new connection.

Answer

Modify the target MySQL database to use a different engine version.

Answer

Increase the task log interval in the DMS task settings.

Question 13

Which TWO of the following are valid approaches to implement fine-grained access control for Amazon DynamoDB items based on user attributes? (Choose 2.)

Accepted Answer

Use Amazon Cognito identity pools with IAM roles that include conditions based on user attributes.. Amazon Cognito identity pools can be configured to assume IAM roles with fine-grained policies that use condition keys such as `dynamodb:LeadingKeys` or custom attribute-based conditions. This allows access to DynamoDB items to be restricted based on user-specific attributes (e.g., user ID) without hardcoding permissions per user.

Answer

Enable row-level security in DynamoDB using AWS Lake Formation.

Answer

Configure a VPC endpoint with a bucket policy to restrict access to specific items.

Answer

Store user-specific items in separate S3 buckets and use IAM policies to restrict bucket access.

Question 14

A data engineer needs to store semi-structured JSON data for a real-time analytics application. The data will be queried using SQL-like statements and must support high-speed ingestion with minimal latency. Which AWS service is best suited for this use case?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is best suited because it natively processes streaming JSON data using SQL-like statements (via Kinesis Data Analytics for SQL applications) with sub-second latency, enabling real-time analytics on semi-structured data without requiring a separate storage layer for ingestion. It directly integrates with Kinesis Data Streams or Firehose for high-speed ingestion and supports in-application queries on JSON payloads using the `json_extract` function or schema discovery.

Answer

Amazon S3

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 15

A company uses Amazon S3 to store historical stock market data as CSV files. They run daily Amazon Athena queries to generate reports. Recently, the finance team reported that queries are timing out and costs have increased significantly. The data engineering team notices that the S3 bucket contains thousands of small files (average 100 KB) due to a misconfigured ingestion pipeline. They need to improve query performance and reduce costs without changing the existing reporting schedule. The team has access to AWS Glue and can create new tables. Which solution should they implement?

Accepted Answer

Use AWS Glue ETL to read the CSV files, convert them to Parquet, and write them back to S3 in fewer, larger files.. Converting the thousands of small CSV files into fewer, larger Parquet files using AWS Glue ETL directly addresses the root cause of poor Athena performance and high costs. Parquet is a columnar format that reduces the amount of data scanned per query, and larger files minimize the overhead of S3 LIST and GET operations, improving throughput. This solution does not change the reporting schedule and leverages existing Glue capabilities to create new optimized tables.

Answer

Partition the data by date and create a new Athena table with partitions.

Answer

Use S3 Select to filter rows within each file before Athena processes them.

Answer

Increase the Athena query timeout to 30 minutes.

Question 16

A company is migrating its on-premises Oracle database to Amazon RDS for Oracle. The database is 2 TB in size and has a 24-hour maintenance window. The migration must have minimal downtime. 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 supports heterogeneous migrations (Oracle to RDS for Oracle) with minimal downtime using ongoing replication via Oracle LogMiner or binary reader. DMS can perform a full load of the 2 TB database and then continuously replicate changes from the source to the target during the 24-hour maintenance window, allowing a final cutover with only seconds of downtime.

Answer

Amazon RDS native backup and restore

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Snowball Edge

Question 17

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources in Parquet format, and the schema evolves over time. Which approach allows querying the data with Amazon Athena while supporting schema evolution?

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

Accepted Answer

Use server-side encryption with AWS KMS (SSE-KMS) and enable automatic key rotation.. SSE-KMS with automatic key rotation (Option D) meets the requirements because it encrypts data at rest in S3, allows key rotation every 90 days via AWS KMS automatic rotation, and logs all key usage in AWS CloudTrail for auditing. This provides the necessary encryption, rotation, and access logging without managing keys externally.

Answer

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

Answer

Use client-side encryption with a master key stored in AWS Secrets Manager.

Answer

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

Question 18

A company runs a real-time analytics platform on AWS. Data is ingested from thousands of IoT devices into Amazon Kinesis Data Streams. A Lambda function consumes the stream, processes the data, and writes the results to an Amazon DynamoDB table. The DynamoDB table has a provisioned write capacity of 1000 WCU, and the read capacity is set to 200 RCU. Recently, the company noticed that the Lambda function is failing with ProvisionedThroughputExceededException on DynamoDB writes. The Lambda function is configured with a batch size of 100 and a concurrency limit of 10. The Kinesis shard count is 4. The number of devices has increased, but the data volume per device has remained the same. The company needs to resolve the write throttling without increasing the DynamoDB write capacity. Which action should the data engineer take?

Accepted Answer

Reduce the batch size of the Lambda function to 10.. Reducing the batch size from 100 to 10 decreases the number of records processed per Lambda invocation, which reduces the burst of write requests to DynamoDB per invocation. This helps stay within the 1000 WCU limit without increasing capacity, as the same total throughput is spread across more invocations with smaller batches.

Answer

Increase the number of Kinesis shards to 8.

Answer

Increase the Lambda concurrency limit to 20.

Answer

Increase the batch size to 200.

Question 19

Refer to the exhibit. An S3 event notification is configured to trigger an AWS Lambda function when objects are created in 'my-bucket'. The Lambda function processes the JSON file and writes results to Amazon DynamoDB. The function fails with a timeout error. Which action should the engineer take to resolve the issue?

Accepted Answer

Increase the Lambda function timeout. The Lambda function is failing with a timeout error, which indicates that the function is taking longer to execute than the default timeout of 3 seconds. Increasing the Lambda function timeout allows the function to run longer and complete its processing of the JSON file and DynamoDB write operation without being prematurely terminated.

Answer

Modify the S3 event notification to use a different event type

Answer

Grant the Lambda function permission to access DynamoDB

Answer

Change the trigger to Amazon SQS instead of S3

Question 20

A company uses Amazon CloudWatch Logs to collect application logs from EC2 instances. The logs are exported to Amazon S3 for long-term storage. Recently, the export task failed with the error 'Access Denied'. What is the most likely cause of this failure?

Accepted Answer

The IAM role does not have s3:PutObject permission on the destination bucket.. The export task from CloudWatch Logs to S3 uses an IAM role to write data to the destination bucket. If the role lacks the s3:PutObject permission, the S3 service will reject the request with an 'Access Denied' error. This is the most common cause because the export operation requires write access to the bucket.

Answer

The S3 bucket policy denies access from the CloudWatch Logs service.

Answer

The IAM role does not have s3:ListBucket permission.

Answer

The EC2 instances are in a VPC without a VPC endpoint for CloudWatch Logs.