DEA-C01 Multiple Select — 30 Questions

Question 1

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.)

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

Accepted Answer

Use DAX with Write-Through caching.. 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.

Answer

Enable DynamoDB Global Tables.

Answer

Enable DynamoDB Streams.

Answer

Enable auto-scaling for read and write capacity.

Question 2

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)

Accepted Answer

Amazon S3 with lifecycle policies. 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.

Answer

Amazon EBS with io2 Block Express volumes

Answer

Amazon EFS

Answer

Amazon RDS for PostgreSQL

Question 3

Which THREE factors should a data engineer consider when choosing between Amazon RDS and Amazon DynamoDB for a new application? (Choose THREE.)

Accepted Answer

Requirement for ACID transactions across multiple tables.. Amazon RDS (with engines like PostgreSQL or MySQL) supports ACID transactions across multiple tables, ensuring atomicity, consistency, isolation, and durability for complex operations. DynamoDB, while supporting transactions, is limited to a single AWS account and region, and its transactional API has constraints on item sizes and throughput, making RDS the better choice for multi-table ACID compliance.

Answer

Ability to encrypt data at rest.

Answer

Support for multi-region disaster recovery.

Question 4

A company uses Amazon RDS for MySQL with Multi-AZ deployment. The primary instance fails, and automatic failover occurs. After failover, the data engineer notices that the new primary instance has a different DNS endpoint. Which TWO statements are true about this scenario? (Choose TWO.)

Accepted Answer

The DNS CNAME record is updated to point to the new primary.. When Amazon RDS performs automatic failover in a Multi-AZ deployment, it updates the DNS CNAME record for the primary DB instance to point to the new primary (formerly the standby). This ensures that applications using the original endpoint are transparently redirected to the new primary without manual intervention.

Answer

The standby instance is created in the same Availability Zone as the failed primary.

Answer

A manual DNS update is required to connect to the new primary.

Answer

The endpoint changes to the standby instance's endpoint.

Question 5

A company is using AWS Glue ETL jobs to process data from Amazon S3 and write results back to S3. The jobs are failing intermittently with 'ThrottlingException' errors. Which TWO configurations would help reduce these errors?

Accepted Answer

Add retry logic with exponential backoff in the job script.. Adding retry logic with exponential backoff in the job script directly addresses transient 'ThrottlingException' errors by automatically retrying failed API calls after increasing delays. This is a standard best practice for handling service throttling in AWS Glue, as it reduces the request rate to stay within service limits without requiring infrastructure changes.

Answer

Decrease the number of DPUs for the job.

Answer

Enable GZIP compression on the output data.

Answer

Change the job type from Spark to Python shell.

Question 6

A company stores sensitive data in Amazon S3. The data engineer needs to implement a solution that automatically detects and redacts PII in new objects as they are uploaded. Which TWO AWS services should be used together?

Accepted Answer

Amazon Macie. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in Amazon S3. Amazon Comprehend is a natural language processing (NLP) service that can be used to detect and redact PII entities from text. Together, they enable automated detection and redaction of PII in newly uploaded S3 objects by triggering Macie to identify sensitive data and then using Comprehend to redact the PII.

Answer

AWS Glue ETL

Answer

Amazon DynamoDB

Answer

AWS Glue Data Catalog

Question 7

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

Answer

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

Answer

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

Question 8

Which THREE factors should a data engineer consider when choosing between Amazon S3 and Amazon DynamoDB for storing time-series data? (Choose three.)

Accepted Answer

Required query complexity (simple key lookups vs. range scans). Amazon S3 supports range scans via its ListObjectsV2 API with prefix and delimiter parameters, but it is not optimized for complex queries like filtering on non-key attributes or aggregations. DynamoDB, on the other hand, excels at simple key lookups and range scans on its sort key, but lacks native support for complex query patterns such as multi-attribute filtering or joins. Therefore, the required query complexity directly influences the choice: S3 is better for simple prefix-based scans, while DynamoDB is better for key-value lookups and sort-key range queries.

Answer

Cost per GB of storage

Answer

Total data volume

Question 9

A data engineer needs to ensure that data in an Amazon S3 bucket is not publicly accessible. Which TWO measures should the engineer implement? (Choose TWO.)

Accepted Answer

Attach a bucket policy that denies access to 'Principal': '*' unless specific conditions are met.. To prevent public access to an S3 bucket, you can use bucket policies that explicitly deny access to anonymous principals (option A) or enable S3 Block Public Access settings (option C). Option B is incorrect because lifecycle policies manage object retention and deletion, not access control. Option D is incorrect because versioning protects against accidental deletion/overwrites but does not control access. Option E is incorrect because default encryption secures data at rest but does not restrict public access.

Answer

Create a lifecycle policy to delete objects after 30 days.

Answer

Enable S3 Versioning on the bucket.

Answer

Enable default encryption on the bucket.

Question 10

Which TWO options are valid ways to reduce storage costs for an Amazon S3 data lake that stores historical data rarely accessed after 30 days? (Choose TWO.)

Accepted Answer

Create a lifecycle policy to transition objects to S3 Standard-IA after 30 days.. S3 Standard-IA (Infrequent Access) is designed for data accessed less frequently but requires rapid access when needed. Transitioning objects to Standard-IA after 30 days reduces storage costs compared to S3 Standard while maintaining low-latency retrieval, making it ideal for a data lake where historical data is rarely accessed after the first month.

Answer

Enable S3 Transfer Acceleration for all uploads.

Answer

Create a lifecycle policy to delete objects after 30 days.

Answer

Enable S3 Versioning to preserve all object versions.

Question 11

Which THREE of the following are benefits of using Amazon DynamoDB Accelerator (DAX)? (Choose three.)

Accepted Answer

Offloads read traffic from the DynamoDB table.. DAX acts as a read-through cache that offloads read traffic from the DynamoDB table, reducing the number of read requests that hit the underlying table and thus lowering the consumed read capacity units (RCUs). This allows the table to handle more concurrent reads without scaling up provisioned capacity.

Answer

Improves write throughput by batching writes.

Answer

Supports write-through caching to improve write performance.

Question 12

A data engineer needs to transform CSV files in S3 to Parquet format using a serverless solution. The files are large (up to 5 GB each) and arrive irregularly. Which TWO services can accomplish this with minimal operational overhead? (Choose TWO.)

Accepted Answer

AWS Glue ETL job. AWS Glue ETL job is correct because it is a fully managed, serverless service that can automatically convert CSV to Parquet without provisioning infrastructure. It handles large files (up to 5 GB) by scaling Spark executors dynamically, and can be triggered by S3 events for irregular arrivals, minimizing operational overhead.

Answer

Amazon EC2 with a script

Answer

Amazon EMR cluster

Answer

Amazon Redshift Spectrum

Question 13

A data engineer is troubleshooting a Glue ETL job that reads from an S3 bucket and writes to a Redshift table. The job fails with a 'MemoryError' when processing a large dataset. Which TWO actions should the engineer take to resolve this issue? (Choose TWO.)

Accepted Answer

Increase the number of DPUs and set 'spark.sql.shuffle.partitions' to a higher value.. Increasing the number of DPUs (Data Processing Units) provides more memory and compute resources to the Glue job, directly addressing the MemoryError. Setting 'spark.sql.shuffle.partitions' to a higher value reduces the amount of data shuffled per partition, preventing out-of-memory errors during wide transformations like joins or aggregations.

Answer

Increase the number of DPUs and set 'coalesce(1)' in the script.

Answer

Decrease the number of DPUs and increase 'spark.shuffle.partitions'.

Answer

Set the 'RedshiftTempDir' parameter to a larger S3 bucket.

Question 14

A data engineer is implementing a CDC (Change Data Capture) pipeline from a relational database to Amazon S3 using AWS Database Migration Service (DMS). Which TWO configurations are required for continuous replication?

Accepted Answer

Enable binary logging on the source database.. For continuous CDC replication with AWS DMS, two key configurations are required: enabling binary logging on the source database (option B) and selecting both 'Full load' and 'Ongoing replication' in the DMS task (option D). Binary logging (e.g., binlog for MySQL) captures ongoing changes, while enabling both full load and ongoing replication ensures that the initial data load is completed before continuous change capture begins. Option A (transformation rules) is optional, not required. Option C (VPC endpoint) is not necessary because DMS can connect to the source database over the internet, VPN, or VPC peering without a VPC endpoint. Option E (pre-creating the target table in S3) is not needed because DMS can create the target table automatically or you can let DMS handle the schema.

Answer

Define transformation rules in the DMS task.

Answer

Configure a VPC endpoint for DMS.

Answer

Pre-create the target table in S3.

Question 15

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from an S3 bucket. The job logs show the following error: 'java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found'. Which TWO actions will resolve this issue?

Accepted Answer

Include the hadoop-aws jar as an extra jar in the Glue job configuration.. The error 'ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem' indicates the S3A filesystem library is missing from the classpath. Option B resolves this by adding the hadoop-aws jar as an extra jar, which provides the S3AFileSystem class. Option D resolves it by using a Glue version (3.0 or later) that includes the S3A library by default. Option A (VPC endpoint) addresses networking, not classpath. Option C (IAM role) addresses permissions, not missing classes. Option E (EMRFS) is for Amazon EMR, not AWS Glue.

Answer

Enable VPC S3 endpoint for the Glue job.

Answer

Update the IAM role to allow access to S3.

Answer

Change the S3 access mode from S3A to EMRFS.

Question 16

Which THREE are valid considerations when troubleshooting data loss in an AWS Glue ETL job? (Choose three.)

Accepted Answer

Job bookmarks may be skipping new data if not configured properly.. Options A, D, and E are correct. Job bookmarks may skip new data if not configured properly (A). Dynamic frame transformations can drop rows if errors occur during processing (D). Incorrect mapping of source columns to target columns can cause data loss (E). Option B is incorrect because disabling server-side encryption on S3 does not cause data loss; it affects data at rest encryption but not data integrity during ETL. Option C is incorrect because setting the job timeout too low may cause the job to fail prematurely, but it does not directly cause data loss; data can be reprocessed after adjusting the timeout.

Answer

Server-side encryption is disabled on the S3 bucket.

Answer

The job timeout is set too low.

Question 17

Which TWO actions are effective ways to monitor the health of an Amazon DynamoDB table? (Choose two.)

Accepted Answer

Enable DynamoDB Streams and process with Lambda to detect failures.. Options C and D are correct. DynamoDB Streams with Lambda can detect failures by processing change events, and CloudWatch alarms on ConsumedReadCapacityUnits help monitor throughput and potential throttling. Option A is wrong because S3 inventory tracks S3 objects, not DynamoDB. Option B is wrong because EC2 instance status checks are for EC2 instances, not DynamoDB tables. Option E is wrong because there is no 'TableHealth' metric in CloudWatch; DynamoDB health is monitored via metrics like ConsumedReadCapacityUnits, ThrottledRequests, and SystemErrors.

Answer

Use AWS S3 inventory to track table size.

Answer

Use EC2 instance status checks.

Answer

Monitor the 'TableHealth' metric in CloudWatch.

Question 18

A data engineer is setting up Amazon CloudWatch alarms for an Amazon Redshift cluster. The engineer wants to be alerted when the disk space usage exceeds 80% for more than 5 minutes and when the CPU utilization exceeds 90% for more than 10 minutes. Which TWO CloudWatch metrics and conditions should the engineer use? (Choose two.)

Accepted Answer

Metric: CPUUtilization; Condition: > 90 for 10 minutes. The correct metrics and conditions are: for disk space usage exceeding 80% for more than 5 minutes, use PercentageDiskSpace with a threshold of 80 and a period of 5 minutes; for CPU utilization exceeding 90% for more than 10 minutes, use CPUUtilization with a threshold of 90 and a period of 10 minutes. These correspond to options A and E. Option B (DatabaseConnections) monitors connections, not disk or CPU. Option C (NetworkReceiveThroughput) measures network traffic. Option D (WLMQueueLength) tracks query queue length, not disk or CPU.

Answer

Metric: DatabaseConnections; Condition: > 500 for 5 minutes

Answer

Metric: NetworkReceiveThroughput; Condition: > 1 GB for 10 minutes

Answer

Metric: WLMQueueLength; Condition: > 100 for 5 minutes

Question 19

A company is designing a data lake on AWS using Amazon S3. The data includes sensitive customer information that must be encrypted at rest. The company requires that encryption keys be managed by AWS, but the keys must be rotated automatically every year. Which TWO options meet these requirements? (Choose TWO.)

Accepted Answer

Use Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3).. SSE-S3 is correct because it provides server-side encryption where AWS manages the encryption keys entirely, including automatic annual rotation. This meets the requirement of encryption at rest with AWS-managed keys and automatic yearly rotation without any additional configuration.

Answer

Use client-side encryption with an AWS KMS key.

Answer

Use Server-Side Encryption with Customer-Provided Keys (SSE-C).

Answer

Use Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS) with manual key rotation.

Question 20

A company runs an Amazon Redshift cluster for data warehousing. The data engineering team notices that the 'Amazon Redshift Data API' is timing out when executing long-running queries. The queries typically take more than 10 minutes to complete. The team wants to ensure that the queries can complete without timeout and that the results are retrievable. Which TWO steps should the team take? (Choose TWO.)

Accepted Answer

Use the 'GetStatementResult' operation to retrieve results after the query completes.. Options C and E are correct. The Amazon Redshift Data API has a default timeout of 10 minutes for a single API call. To handle queries that take longer, you can run the query asynchronously by using the StatementName parameter (option E). This allows the query to continue running even if the initial API call times out, and you can poll for completion using the DescribeStatement operation. Once the query completes, you can retrieve the results using the GetStatementResult operation (option C). Option A is incorrect because QueryExecutionTimeout is not a valid parameter for the Data API. Option B is incorrect because increasing the timeout parameter in the Redshift cluster configuration does not affect the Data API timeout. Option D is incorrect because there is no max_execution_time parameter in Redshift; the relevant parameter is statement_timeout, which controls how long a query can run before being canceled, but it does not address the Data API timeout.

Answer

Set the 'QueryExecutionTimeout' parameter in the Data API call to 30 minutes.

Answer

Increase the 'timeout' parameter in the Redshift cluster configuration.

Answer

Set the 'max_execution_time' parameter in the Redshift parameter group to 30 minutes.