DEA-C01 Practice Exam 63 — 20 Questions

Question 1

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Redshift. The migration is successful, but after a few days, data in Redshift becomes inconsistent with the source due to ongoing changes. The company needs to keep Redshift synchronized with minimal latency. Which approach should the data engineer use?

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

Accepted Answer

Configure DMS with ongoing replication using change data capture (CDC).. AWS DMS supports ongoing replication using change data capture (CDC), which captures incremental changes from the Oracle source (via Oracle LogMiner or binary logs) and applies them to Amazon Redshift in near real-time. This approach ensures that Redshift remains synchronized with the source database with minimal latency, meeting the requirement for ongoing consistency after the initial full load.

Answer

Use Amazon Redshift COPY with S3 staging and AWS Lambda triggers.

Answer

Schedule a full DMS load every night.

Answer

Set up Amazon Redshift Spectrum to query the Oracle database directly.

Question 2

A data engineer needs to migrate an on-premises MySQL database to Amazon RDS for MySQL with minimal downtime. Which approach should they use?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication from the source database.. AWS DMS with ongoing replication (change data capture, CDC) is the correct approach because it allows continuous synchronization from the on-premises MySQL source to the RDS target, enabling a cutover with minimal downtime. Unlike one-time export/import tools, DMS captures ongoing changes during the migration, so the target stays up-to-date until you switch over.

Answer

Use mysqldump to export the database and import into RDS.

Answer

Create an RDS read replica and promote it.

Answer

Use AWS Schema Conversion Tool (SCT) to convert the schema and then copy data.

Question 3

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.

Answer

AWS S3 Transfer Acceleration

Answer

AWS Glue

Answer

AWS Snowball Edge

Question 4

A company stores sensitive customer data in an Amazon S3 bucket with versioning enabled. A data engineer accidentally deleted the current version of an object. What is the quickest way to restore the object to its previous state without additional data transfer costs?

Accepted Answer

Delete the delete marker that was created by the deletion.. With S3 versioning enabled, deleting an object does not permanently remove it; instead, a delete marker is placed. To restore the object to its previous state, you simply remove the delete marker, which makes the previous version the current version again. Option A is incorrect because S3 does not have a Recycle Bin; S3 Batch Operations are for bulk actions but not for restoring from a recycle bin. Option C would not restore the object properly; copying the previous version to itself would create a new version, not restore the original. Option D is incorrect because the s3 sync command synchronizes objects between locations and does not restore previous versions.

Answer

Use S3 Batch Operations to restore the object from the Recycle Bin.

Answer

Copy the previous version from the bucket to itself.

Answer

Use the S3 sync command to restore the previous version.

Question 5

A company uses AWS Data Pipeline to copy data from DynamoDB to S3 daily. Recently, the pipeline started failing with 'ThrottlingException' errors. The DynamoDB table has on-demand capacity. Which action should be taken to resolve the issue?

Accepted Answer

Configure the pipeline to use a retry strategy with exponential backoff.. ThrottlingException errors in AWS Data Pipeline when reading from DynamoDB indicate that the pipeline's read requests are exceeding the table's available throughput. Since the table uses on-demand capacity, which can handle spikes but has a per-second throughput limit, implementing exponential backoff in the pipeline's retry strategy allows it to reduce request rate upon throttling, aligning with AWS SDK best practices for handling DynamoDB throttling.

Answer

Increase the write capacity units of the DynamoDB table.

Answer

Replace Data Pipeline with AWS Glue using a DynamoDB connector.

Answer

Disable the pipeline's retry logic and increase the timeout.

Question 6

A company is using AWS Lake Formation to manage permissions on a data lake. They want to grant a data scientist the ability to query tables in the 'analytics' database using Amazon Athena, but prevent them from accessing the underlying S3 data directly. What is the best way to achieve this?

Accepted Answer

Grant SELECT permission on the 'analytics' database tables in Lake Formation.. Lake Formation grants SELECT permission on named database tables, which allows querying via Athena without granting direct S3 access. Option A is incorrect because granting s3:GetObject on the entire bucket would allow the data scientist to bypass Lake Formation and access the data directly. Option C is incorrect because a policy that allows Athena queries only does not grant the necessary permissions to access the database tables. Option D is incorrect because adding the user to a data lake location with read access is too broad and would also grant direct S3 access, which does not meet the requirement of preventing direct S3 access.

Answer

Grant the data scientist an IAM policy with s3:GetObject on the S3 bucket.

Answer

Create an IAM policy that allows Athena queries only.

Answer

Add the data scientist to a Lake Formation data lake location with read access.

Question 7

A data engineer is troubleshooting an Amazon Redshift cluster that is running out of disk space. The engineer runs STV_PARTITIONS and notices that some slices have significantly more data than others. What is the most likely cause and solution?

Accepted Answer

Data distribution skew due to uneven distribution style; change distribution style to EVEN or correct KEY. B is correct because STV_PARTITIONS shows per-slice disk usage, and significant variation indicates data distribution skew. Uneven distribution causes some slices to fill faster, leading to premature disk-full errors. Changing the distribution style to EVEN (for tables without join keys) or correcting the KEY distribution style (using a high-cardinality, evenly distributed column) rebalances data across slices.

Answer

Poorly chosen sort keys; redefine sort keys

Answer

Some nodes are underutilized; add more nodes

Answer

Concurrency scaling is disabled; enable concurrency scaling

Question 8

A data engineer is using AWS Glue ETL to transform a large dataset in S3. The job processes 2 TB of data daily and currently runs for 6 hours. The engineer wants to reduce runtime without changing the transformation logic. What is the best approach?

Accepted Answer

Increase the number of Glue DPUs or enable auto-scaling.. Increasing the number of DPUs or enabling auto-scaling directly allocates more distributed processing capacity to the AWS Glue job, which reduces runtime for large datasets by parallelizing the workload across more resources. Since the transformation logic is fixed and the job is already running on Spark, adding compute capacity is the most straightforward way to speed up processing without code changes.

Answer

Reduce the number of DPUs to minimize overhead.

Answer

Use the Spark UI to analyze bottlenecks and rewrite code.

Answer

Switch from Spark to Python shell.

Question 9

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data must be transformed in real-time using a custom Lambda function. Which TWO steps are required to enable this? (Choose TWO)

Accepted Answer

Configure Kinesis Data Firehose to use a Lambda function for data transformation. Option A is correct because enabling real-time transformation in Kinesis Data Firehose requires configuring the delivery stream to invoke a Lambda function as its processing step, which Firehose then calls synchronously for each batch of records. Option B is correct because the Lambda function must return the transformed records in the exact format Firehose expects — a JSON object containing the processed records with fields such as recordId, result (Ok, Dropped, or ProcessingFailed), and data (base64-encoded) — otherwise Firehose cannot continue delivery. Option C is wrong because Kinesis Data Analytics is a separate service for SQL/Flink stream analytics, not the mechanism Firehose uses for Lambda-based transformation. Option D is wrong because Firehose does not execute inline transformation logic; it only supports invoking a Lambda function for that purpose. Option E is wrong because using Kinesis Data Streams as a source is unrelated to enabling Lambda transformation and is not a required step.

Answer

Create a Kinesis Data Analytics application to transform the data

Answer

Write the transformation logic directly in the Firehose delivery stream configuration

Answer

Use Kinesis Data Streams as the source for Firehose

Question 10

A company is migrating a legacy on-premises ETL pipeline to AWS. The pipeline processes daily batch files from an FTP server. The data must be transformed using complex business logic before being loaded into Amazon Redshift. Which THREE AWS services should be used for this migration?

Accepted Answer

Amazon Redshift. AWS Transfer Family (E) is correct because it provides a fully managed FTP/FTPS/SFTP endpoint that can land the legacy daily batch files directly into Amazon S3, replacing the on-premises FTP server without changing the source's protocol. AWS Glue (C) is correct because it is a serverless ETL service whose Spark-based jobs and Glue Data Catalog can implement the complex business logic transformations on the batch data before loading. Amazon Redshift (B) is correct because it is the target data warehouse where the transformed data is loaded for analytics, and Glue can write to it via the Redshift JDBC/ODBC connection or the Redshift data source. Amazon Athena (A) is not appropriate because it is an interactive query service over S3, not an ETL engine for applying complex transformation logic. Amazon Kinesis Data Streams (D) is not appropriate because it is designed for real-time streaming ingestion, whereas this pipeline processes daily batch files.

Answer

Amazon Athena

Answer

Amazon Kinesis Data Streams

Question 11

A data streaming application uses Kinesis Data Streams with 10 shards. The data producer is throttled frequently. Which action should be taken to resolve this issue?

Accepted Answer

Increase the number of shards. Throttling in Kinesis Data Streams occurs when the write throughput exceeds the shard limits. Each shard supports up to 1 MB/s or 1,000 records/s for writes. With 10 shards, the total write capacity is 10 MB/s or 10,000 records/s. Increasing the number of shards (Option D) directly increases the write capacity, resolving the throttling issue by distributing the load across more shards.

Answer

Decrease the data retention period

Answer

Use enhanced fan-out for consumers

Answer

Enable server-side encryption

Question 12

A company uses Amazon Redshift for its data warehouse and needs to enforce column-level security on sensitive columns. Which TWO approaches can achieve this?

Accepted Answer

Create views that expose only non-sensitive columns and grant access to the views.. Option B is correct because creating views that expose only non-sensitive columns and granting users access to those views (rather than the base tables) is a standard Redshift pattern for column-level security: users query the view and cannot see the restricted columns. Option D is correct because Amazon Redshift natively supports column-level access control via GRANT and REVOKE on individual columns (for example, GRANT SELECT(col1, col2) ON table TO user), which directly enforces column-level security. Option A is wrong because an S3 bucket policy governs access to objects in S3, not to columns within Redshift tables, and does not control SQL-level column visibility. Option C is wrong because Redshift Spectrum external schemas and tables do not provide a column-restriction mechanism for enforcing column-level security on Redshift data. Option E is wrong because Redshift row-level security (RLS) policies filter which rows a user can see, not which columns they can access.

Answer

Apply an S3 bucket policy to the underlying data files.

Answer

Use Redshift Spectrum to query external tables and restrict columns via the external schema.

Answer

Use Redshift row-level security policies to restrict column access.

Question 13

Refer to the exhibit. An IAM policy includes the above statement to allow decryption of a KMS key under specific conditions. What does this policy allow?

Accepted Answer

Decrypt data that was encrypted with the encryption context {"aws:pi":"db-123"}. The policy allows the kms:Decrypt action only when the encryption context matches the specified key-value pair. The condition likely uses the StringEquals condition operator on the kms:EncryptionContext:aws:pi key with value db-123. This means decryption is permitted only for ciphertext that was encrypted with that exact encryption context.

Answer

Decrypt any data encrypted with any KMS key

Answer

Encrypt data with the KMS key using the specified encryption context

Answer

Decrypt data encrypted with the KMS key without any encryption context

Question 14

A company has an AWS Glue ETL job that reads from an RDS MySQL instance and writes to S3. The security team requires that the connection to RDS be encrypted and that credentials be rotated automatically. Which configuration should be used?

Accepted Answer

Store the password in AWS Secrets Manager with automatic rotation enabled and configure Glue to use SSL for the connection.. AWS Secrets Manager provides automatic rotation of RDS credentials, and AWS Glue can be configured to use SSL for an encrypted connection to RDS MySQL. Option A (Systems Manager Parameter Store) stores encrypted parameters but does not natively support automatic rotation of RDS credentials. Option B (IAM database authentication) provides authentication but does not encrypt the connection itself; SSL is still required for encryption. Option C (encrypted S3 bucket) is not a service designed for dynamic credential management and lacks automatic rotation.

Answer

Store the database password in an encrypted parameter in Systems Manager Parameter Store and enable SSL for the connection.

Answer

Use IAM database authentication for RDS and store credentials in Glue connection properties.

Answer

Store the password in a text file in an encrypted S3 bucket and use SSL.

Question 15

A company is using AWS Glue to catalog data in Amazon S3. The data is stored in CSV format, but the schema is not consistent across all files. Which TWO actions can the company take to handle schema evolution and ensure the Glue Data Catalog is up to date? (Choose TWO.)

Accepted Answer

Configure the Glue crawler to update the table schema on each run.. Option A is correct because configuring the Glue crawler to update the table schema on each run allows it to detect new columns or changed data types in the CSV files and automatically revise the Data Catalog table definition, which is essential when schemas vary across files. Option D is correct because scheduling the crawler to run periodically ensures that any schema changes introduced by new or modified files are detected and reflected in the Glue Data Catalog in a timely, automated manner. Together, these two actions provide automated, recurring schema evolution handling. Option B is not ideal because manual updates are error-prone and do not scale, and the question asks for actions the company can take to handle schema evolution automatically. Option C is incorrect because disabling schema update prevents the crawler from adapting to schema changes, and manual partition addition does not address evolving column structures. Option E is incorrect because enforcing a single fixed schema contradicts the scenario where schemas are already inconsistent and does not help update the catalog for existing variation.

Answer

Manually update the Glue Data Catalog tables whenever the schema changes.

Answer

Disable schema update in the crawler and add partitions manually.

Answer

Require all data producers to use a single fixed schema.

Question 16

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data is in JSON format, and each record is approximately 5 KB. The company has set the buffer interval to 60 seconds and the buffer size to 5 MB. However, the data engineer observes that the delivery to S3 is delayed by up to 5 minutes during peak traffic. The engineer wants to reduce the delivery latency to under 1 minute. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Reduce the buffer size to 1 MB.. Option B is correct because Kinesis Data Firehose flushes data to S3 when either the buffer size OR the buffer interval is reached; lowering the buffer size from 5 MB to 1 MB makes the size threshold trigger much sooner, so smaller batches are delivered more frequently and latency drops. Option E is correct because reducing the buffer interval from 60 seconds to 10 seconds forces a flush at least every 10 seconds even if the buffer size is not reached, directly capping delivery latency well under 1 minute. Together, B and E ensure the delivery stream flushes on the shorter of the two thresholds, which is exactly what is needed to cut the observed 5-minute delays. Option A does not help because GZIP compression only reduces payload size and can actually add CPU overhead, not reduce flush latency. Option C is wrong because increasing the buffer size to 50 MB would delay flushes further, worsening latency. Option D is wrong because converting to Parquet changes the storage format and query efficiency, not the Firehose buffering/flush timing that governs delivery latency.

Answer

Enable GZIP compression for the delivery stream.

Answer

Increase the buffer size to 50 MB.

Answer

Convert the data format to Apache Parquet before delivery.

Question 17

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.. Option B is correct because AWS DMS continuous replication (CDC) requires the source relational database to expose its change stream — for MySQL/MariaDB that means binary logging (log_bin) enabled with binlog_format=ROW, and for other engines the equivalent (e.g., Oracle supplemental logging, SQL Server MS-CDC). Option D is correct because the DMS task's migration type must be set to 'Full load and ongoing replication' (or 'Ongoing replication' if data is already loaded) so DMS performs the initial load and then continuously applies CDC changes to Amazon S3. Option A is not required — transformation rules are optional and only used to rename, filter, or modify schema/data during migration. Option C is not required — a VPC endpoint is only needed for private connectivity to services like S3 when using a VPC-attached replication instance, not as a general CDC requirement. Option E is not required — DMS writes to S3 as CSV or Parquet files and does not need a pre-created target table.

Answer

Define transformation rules in the DMS task.

Answer

Configure a VPC endpoint for DMS.

Answer

Pre-create the target table in S3.

Question 18

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.. Option C is correct because DynamoDB Streams captures item-level changes in near real time, and a Lambda consumer can inspect those records to detect anomalies or failed writes, providing an event-driven health signal for the table. Option D is correct because CloudWatch publishes DynamoDB metrics such as ConsumedReadCapacityUnits (and ConsumedWriteCapacityUnits), and alarms on these metrics reveal throttling or unexpected capacity consumption that indicates table health issues. Option A is wrong because S3 Inventory reports on S3 bucket objects, not DynamoDB table size or health. Option B is wrong because EC2 instance status checks only reflect the health of EC2 instances, not a managed DynamoDB table. Option E is wrong because DynamoDB does not expose a 'TableHealth' metric in CloudWatch; health must be inferred from real metrics like throttled requests, consumed capacity, and latency.

Answer

Use AWS S3 inventory to track table size.

Answer

Use EC2 instance status checks.

Answer

Monitor the 'TableHealth' metric in CloudWatch.

Question 19

A data engineer is setting up a data pipeline using Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data must be transformed using an AWS Lambda function before delivery. Which THREE steps are required to configure this?

Accepted Answer

Create an AWS Lambda function that transforms the data.. Option B is correct because Firehose data transformation requires an AWS Lambda function (a standard regional Lambda function, not Lambda@Edge) that receives batches of records and returns transformed records in the expected Firehose format. Option C is correct because the Firehose delivery stream assumes an IAM role, and that role must include lambda:InvokeFunction permission on the transformation function so Firehose can call it. Option E is correct because you must enable and select the Lambda function in the Firehose delivery stream's processing configuration (the Lambda transformation processor) so records are transformed before being written to Amazon S3. Option A is wrong because Lambda@Edge runs at CloudFront edge locations for viewer/origin request and response events, not for Firehose record transformation. Option D is wrong because S3 event notifications trigger actions after objects land in S3; Firehose invokes the transformation Lambda itself, so no S3 event notification is needed.

Answer

Create a Lambda@Edge function in the same Region.

Answer

Configure an S3 event notification to trigger the Lambda function when new data arrives.

Question 20

A company uses AWS DMS to replicate data from an Amazon RDS for MySQL database to Amazon S3. Which TWO configurations are required to enable continuous change data capture (CDC) from MySQL?

Accepted Answer

Grant REPLICATION CLIENT and REPLICATION SLAVE privileges to the DMS user. Option B is correct because AWS DMS requires the MySQL endpoint user to have REPLICATION CLIENT (to read binlog metadata/status) and REPLICATION SLAVE (to read the binary log events) privileges so it can capture ongoing changes for CDC. Option C is correct because continuous change data capture depends on the MySQL source having binary logging enabled (log_bin=ON) with a suitable binlog_format (e.g., ROW) so DMS can read committed changes from the binlog. Option A is not required, since DMS can replicate to an S3 bucket in a different Region than the source database. Option D is not required, as S3 bucket versioning is unrelated to DMS CDC and is not needed for change capture. Option E is not required, because Multi-AZ is a high-availability deployment choice for the source and does not enable or affect DMS change data capture.

Answer

Ensure the S3 bucket is in the same AWS Region as the source database

Answer

Enable versioning on the target S3 bucket

Answer

Configure the MySQL source to be Multi-AZ