DEA-C01 Mock Exam 94 — 25 Questions

Question 1

A company uses Amazon Redshift for its data warehouse. The data engineering team loads data daily from Amazon S3 using COPY commands. Recently, the load performance has degraded because the S3 bucket contains many small files. The team needs to optimize the COPY operation to improve performance. Which approach should they take?

Accepted Answer

Use a manifest file that lists only the necessary files, and consolidate small files into larger ones before loading.. Option C is correct because the performance degradation is caused by the overhead of processing many small files during the COPY command. Consolidating small files into larger ones (e.g., 100 MB–1 GB each) reduces the number of S3 GET requests and the metadata overhead on Redshift, directly improving load throughput. Using a manifest file further optimizes by explicitly listing only the required files, avoiding unnecessary S3 list operations.

Answer

Use Redshift Spectrum to query the data directly from S3 without loading.

Answer

Increase the number of nodes in the Redshift cluster.

Answer

Enable automatic compression on the Redshift table.

Question 2

A company stores its application logs in Amazon S3. The logs are generated daily and need to be retained for 3 years for compliance. The logs are accessed frequently for the first 30 days, occasionally for the next 6 months, and rarely after that. The data engineering team wants to minimize storage costs while ensuring that logs are available for retrieval within 12 hours for the first 6 months and within 48 hours after that. The team also wants to automatically delete logs after 3 years. Which lifecycle policy should the team implement?

Accepted Answer

Transition to S3 Standard-IA after 30 days, to S3 Glacier Deep Archive after 6 months, delete after 3 years.. Option D is correct because it aligns with the access patterns and retrieval requirements: S3 Standard-IA after 30 days for occasional access with immediate retrieval, then S3 Glacier Deep Archive after 6 months for rare access with a 12-hour retrieval time (via expedited or standard retrieval), and deletion after 3 years. This minimizes storage costs while meeting the 48-hour retrieval window for older logs.

Answer

Transition to S3 One Zone-IA after 30 days, delete after 6 months.

Answer

Transition to S3 Standard-IA after 30 days, delete after 3 years.

Answer

Transition to S3 Standard-IA after 30 days, to S3 Glacier after 6 months, delete after 3 years.

Question 3

A startup uses Amazon S3 to store user-uploaded images. The images are accessed frequently for the first week after upload, but after that they are rarely accessed. The company wants to optimize storage costs without compromising availability. The data engineer must implement a lifecycle policy to transition objects to a more cost-effective storage class after 30 days. The objects must be retrievable within minutes. Which storage class should the engineer transition the objects to?

Accepted Answer

S3 Standard-Infrequent Access. S3 Standard-Infrequent Access (S3 Standard-IA) is the correct choice because it offers a per-GB storage cost lower than S3 Standard while maintaining high durability (99.999999999%) and availability (99.9%), with retrieval times in milliseconds. The lifecycle policy transitions objects after 30 days, aligning with the access pattern where images are rarely accessed after the first week, and the requirement for retrieval within minutes is satisfied by S3 Standard-IA's instant access.

Answer

S3 One Zone-Infrequent Access

Answer

S3 Standard

Answer

S3 Glacier Instant Retrieval

Question 4

A data engineering team is using Amazon DynamoDB to store time-series data for a monitoring application. The table has a primary key of device_id (partition key) and timestamp (sort key). The application queries data for a specific device over a time range. The team notices that read latency is high for devices that generate large amounts of data. They need to improve query performance. Which solution should they implement?

Accepted Answer

Create a global secondary index with device_id as partition key and a truncated timestamp as sort key to distribute writes.. Option C is correct because creating a global secondary index (GSI) with device_id as the partition key and a truncated timestamp as the sort key helps distribute write traffic more evenly across partitions. This reduces hot partitions caused by devices that generate large amounts of data, thereby improving read latency by preventing throttling and reducing contention on a single partition.

Answer

Enable DynamoDB Accelerator (DAX) for the table.

Answer

Change the application to use eventually consistent reads.

Answer

Increase the read capacity units for the table.

Question 5

A data engineering team is managing an Amazon Redshift cluster that is used for BI reporting. The cluster has a mix of large tables (some over 1 TB) and many smaller tables. The team notices that queries on a large fact table are slow. The fact table is distributed using KEY distribution on the customer_id column, which has high cardinality. The team wants to improve query performance. They have the option to change the distribution style and sort key. Which redesign should they implement?

Accepted Answer

Change the distribution style to EVEN and set the sort key to a date column used in WHERE clauses.. Option D is correct because using EVEN distribution ensures data is evenly distributed across all nodes, avoiding data skew that can occur with KEY distribution on a high-cardinality column like customer_id. Setting the sort key to a date column used in WHERE clauses enables range-restricted scans, significantly reducing the amount of data scanned for common BI queries that filter by date. This combination improves query performance by maximizing parallelism and minimizing I/O.

Answer

Keep the distribution style as AUTO and set the sort key to customer_id.

Answer

Change the distribution style to ALL and set the sort key to customer_id.

Answer

Change the distribution style to KEY on a different column with high cardinality.

Question 6

A media company stores video metadata in Amazon RDS for PostgreSQL. The database is 500 GB and experiences high write traffic. The data engineer notices that the transaction log (WAL) is growing rapidly, causing storage issues. The company needs to retain backups for 30 days for compliance. The database is currently using automated backups with a retention period of 7 days. Which solution should the engineer implement to address the WAL growth while meeting compliance requirements?

Accepted Answer

Increase the backup retention period to 30 days.. Option C is correct because increasing the backup retention period to 30 days directly addresses the compliance requirement while also managing WAL growth. In Amazon RDS for PostgreSQL, automated backups rely on WAL files to support point-in-time recovery (PITR). When the retention period is too short (e.g., 7 days), RDS may not purge old WAL segments aggressively enough, causing them to accumulate. By extending the retention to 30 days, RDS properly manages WAL cleanup based on the new retention window, preventing unbounded WAL growth and meeting the 30-day compliance requirement.

Answer

Create manual snapshots daily and delete automated backups.

Answer

Change the instance type to a larger one with more storage.

Answer

Configure the database to stream WAL files to Amazon S3.

Question 7

A company runs a MySQL-compatible Amazon Aurora database for its e-commerce platform. The database experiences high write latency during peak hours. The application performs frequent INSERT and UPDATE operations on a table with 50 million rows. The DB instance is db.r5.large with 500 GB of Provisioned IOPS storage. A recent performance analysis shows that the average queue depth is consistently above 32 and write latency exceeds 50 ms. The company needs to reduce write latency without changing the application code. What should a data engineer do?

Accepted Answer

Resize the DB instance to a larger instance type such as db.r5.xlarge.. Option C is correct because the high queue depth (consistently above 32) and write latency exceeding 50 ms indicate that the current db.r5.large instance is CPU or I/O constrained for the write workload. Resizing to a larger instance type such as db.r5.xlarge increases the available vCPUs, memory, and network bandwidth, which directly reduces queue depth and write latency by allowing more concurrent write operations to be processed. This solution does not require application code changes and addresses the root cause of insufficient compute capacity for the frequent INSERT and UPDATE operations on the 50-million-row table.

Answer

Enable Aurora Auto Scaling to automatically add reader instances.

Answer

Convert the cluster to Aurora Serverless v2 to automatically scale compute capacity.

Answer

Create a read replica and configure the application to offload read queries.

Question 8

A company uses Amazon S3 to store sensitive documents. The security team has mandated that all objects must be encrypted at rest using server-side encryption with AWS KMS (SSE-KMS). Additionally, the company wants to ensure that any attempt to upload an unencrypted object is denied. A data engineer has configured a bucket policy that denies PutObject if the encryption header does not include x-amz-server-side-encryption: aws:kms. However, the engineer notices that some objects are still being stored without encryption. Upon investigation, the engineer suspects that the policy is not being evaluated correctly. What should the engineer do to ensure that all objects are encrypted with SSE-KMS?

Accepted Answer

Enable default encryption on the S3 bucket with SSE-KMS.. Option D is correct because enabling default encryption on the S3 bucket with SSE-KMS ensures that any object uploaded without an explicit encryption header is automatically encrypted with SSE-KMS. This closes the gap where the bucket policy condition fails to catch uploads that omit the `x-amz-server-side-encryption` header entirely, as the policy only denies requests with an incorrect header but does not block requests that lack the header altogether. Default encryption applies server-side encryption at the bucket level, making it impossible to store an unencrypted object.

Answer

Use an IAM policy to require encryption instead of a bucket policy.

Answer

Enable S3 Block Public Access settings.

Answer

Add a condition to the bucket policy that checks for aws:SourceVpce.

Question 9

A company runs an e-commerce platform that generates clickstream data from millions of users. The data is ingested into Amazon Kinesis Data Streams with a shard count of 10. The data is then consumed by a Kinesis Data Analytics application that runs SQL queries to aggregate metrics in real time. Recently, the application has been falling behind, and the stream's iterator age metric is increasing. The data volume has doubled over the past month. The application currently uses a single Kinesis Data Analytics application with parallelism of 1. Which action should the data engineer take to improve the processing rate and reduce the iterator age without losing data or causing duplicates?

Accepted Answer

Increase the parallelism of the Kinesis Data Analytics application to match the number of shards.. Option D is correct because Kinesis Data Analytics (KDA) processes data from each shard in a stream using one or more parallel operators. With a parallelism of 1, the application uses only a single processing thread, which cannot keep up with the doubled data volume across 10 shards. By increasing parallelism to match the shard count (10), KDA can read from all shards concurrently, distributing the processing load and reducing the iterator age without data loss or duplicates, as KDA manages checkpointing and exactly-once semantics internally.

Answer

Change the Kinesis Data Analytics application to use a Kinesis Data Firehose delivery stream as the source.

Answer

Reduce the retention period of the Kinesis Data Streams to 24 hours.

Answer

Increase the number of shards in the Kinesis Data Streams to 20.

Question 10

A financial services company ingests real-time stock trade data from multiple exchanges into Amazon Kinesis Data Streams. Each trade record is a JSON object containing fields: trade_id, symbol, price, quantity, and timestamp. The data is consumed by an AWS Lambda function that performs data validation and enrichment, then writes the processed records to an Amazon DynamoDB table for low-latency querying. Recently, the Lambda function has been timing out and failing to process all records. The Lambda function is configured with a 5-second timeout and 128 MB memory. The average record size is 2 KB, and the stream receives about 1000 records per second. The Lambda function's concurrency limit is 1000. Which set of actions should the data engineer take to resolve the issue without losing data?

Accepted Answer

Increase the Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records and enable parallelization factor of 10.. Option A is correct because increasing the Lambda timeout and memory addresses the processing bottleneck, while setting the batch size to 100 and enabling a parallelization factor of 10 allows each shard to process up to 10 concurrent batches, dramatically increasing throughput to handle 1000 records/sec (each shard can process 10 batches of 100 records concurrently, yielding 1000 records/sec per shard if the stream has at least 1 shard). This combination ensures no data loss by keeping up with the ingestion rate without exceeding the Lambda concurrency limit of 1000.

Answer

Increase the number of shards in the Kinesis data stream to 20 and keep the Lambda configuration unchanged.

Answer

Replace the Lambda function with a Kinesis Data Firehose delivery stream that writes directly to DynamoDB using a Lambda transformation.

Answer

Increase the Lambda function timeout to 60 seconds and memory to 1024 MB. Set the batch size to 100 records.

Question 11

A company uses AWS Glue to run ETL jobs that process data from Amazon S3 and write results to Amazon Redshift. The Glue job uses the JDBC connection to Redshift. Recently, the job has been failing intermittently with the error: 'java.sql.SQLException: [Amazon](500310) Invalid operation: INSERT has more expressions than target columns;' The Glue job writes to a staging table in Redshift before performing a merge into the final table. The staging table schema matches the source data. The error occurs only on some days and affects different columns each time. The data engineer suspects that the source data occasionally contains extra columns due to a schema drift in the upstream data producer. Which approach should the data engineer take to handle this issue robustly?

Accepted Answer

Use a Glue DynamicFrame and apply the resolveChoice method to make the schema consistent.. Option B is correct because Glue DynamicFrames can automatically handle schema drift using the `resolveChoice` method, which allows you to specify how to handle columns that appear inconsistently across records (e.g., making them null, casting to a common type, or dropping them). This directly addresses the intermittent error caused by extra columns in the source data without requiring manual schema updates or fragile conditional logic.

Answer

Skip any records that have extra columns by adding a conditional check in the Glue script.

Answer

Manually update the Redshift staging table schema whenever the source data changes.

Answer

Use a Glue DynamicFrame and apply the dropFields method to remove extra columns before writing.

Question 12

A media company is building a data pipeline to ingest user activity logs from multiple sources into Amazon S3. The logs are JSON files generated every minute. The company wants to use Amazon Athena to query the logs with minimal latency and cost. The current approach is to use AWS Kinesis Data Firehose to deliver the logs to S3 with a prefix like 'logs/2024/01/01/00/file.json'. However, when running Athena queries, the team notices high query costs because Athena scans all files in the 'logs/' prefix even when querying for a specific date. What should the team do to reduce the amount of data scanned by Athena?

Accepted Answer

Create a Hive-style partition structure in S3 with keys like 'year=2024/month=01/day=01/hour=00/' and update the Glue Data Catalog accordingly.. Option C is correct because partitioning the data in S3 by date (e.g., year=2024/month=01/day=01/hour=00/) allows Athena to use partition pruning and scan only relevant partitions. Option A is wrong because converting to Parquet helps but alone does not solve the full scan issue. Option B is wrong because increasing number of partitions without proper structure still causes full scan. Option D is wrong because creating views does not change underlying storage or scan behavior.

Answer

Create an Athena view that filters by date.

Answer

Increase the number of partitions by using a more granular prefix like 'logs/2024/01/01/00/00/'.

Answer

Convert the JSON files to Apache Parquet format using AWS Glue ETL jobs.

Question 13

A company is using AWS DMS to migrate a 5 TB SQL Server database to Amazon Aurora PostgreSQL. The migration is using full load plus CDC. After the full load completes, the ongoing replication task is failing with errors related to large transactions on the source. The team needs to ensure that CDC continues without falling behind. What should the team do?

Accepted Answer

Modify the DMS task settings to increase MaxFileSize and decrease the CommitRate.. Option B is correct because increasing the MaxFileSize and reducing CommitRate in the DMS task settings allows DMS to handle large transactions by committing more frequently and using larger log files. Option A is wrong because increasing the instance size helps but does not directly address large transaction handling. Option C is wrong because disabling foreign keys is a schema change, not a CDC fix. Option D is wrong because DMS cannot replicate to Kinesis as a target for CDC to Aurora.

Answer

Use Amazon Kinesis Data Streams as an intermediate target for CDC.

Answer

Increase the DMS replication instance size to provide more memory and CPU.

Answer

Disable foreign key constraints on the target Aurora database.

Question 14

A company is using Amazon S3 to store log files. The security team wants to ensure that any object uploaded to the S3 bucket is automatically scanned for malware before being processed by downstream applications. The data engineer needs to implement a solution that integrates with AWS services and minimizes latency. The bucket receives thousands of objects per day. Which solution should the data engineer use?

Accepted Answer

Enable Amazon GuardDuty with malware protection for S3.. Option B is correct because Amazon GuardDuty has a malware protection feature that can scan S3 objects for malware upon upload. It integrates with S3 events and minimizes latency. Option A is incorrect because Amazon Macie is for sensitive data discovery, not malware. Option C is incorrect because AWS WAF is for web application firewall, not S3 scanning. Option D is incorrect because running a custom scanning solution on EC2 adds operational overhead and latency.

Answer

Deploy an EC2 instance with antivirus software that triggers on S3 events.

Answer

Use AWS WAF to inspect objects as they are uploaded.

Answer

Enable Amazon Macie on the S3 bucket to detect malware.

Question 15

A healthcare company uses AWS Glue to process patient data stored in Amazon S3. The data is encrypted at rest using SSE-KMS with a customer managed key. The Glue ETL job runs on a schedule and reads from an S3 bucket, transforms the data, and writes to another S3 bucket also encrypted with the same KMS key. Recently, the security team rotated the KMS key. After the rotation, the Glue job started failing with 'AccessDenied' errors when trying to read from the source bucket. The Glue job's IAM role has permissions to use the KMS key (kms:Decrypt, kms:GenerateDataKey). The S3 bucket policies allow the role to read/write. What is the MOST likely cause of the failure?

Accepted Answer

The KMS key rotation created a new backing key, but the Glue job's IAM role does not have permission to decrypt with the old backing key.. When you rotate a customer managed KMS key, AWS KMS retains the old backing key to allow decryption of data encrypted before the rotation. However, the Glue job's IAM role must have permission to use the old backing key via the kms:Decrypt action. If the key policy or IAM policy does not explicitly allow decryption with the old backing key (or if the key policy was inadvertently updated to remove access to the old key material), the Glue job will fail with AccessDenied when reading SSE-KMS encrypted objects that were encrypted with the previous key version.

Answer

The Glue job's IAM role is missing the kms:Encrypt permission on the KMS key.

Answer

The Glue job is using the wrong encryption context when calling KMS.

Answer

The S3 bucket policy has a condition that requires the request to use the latest version of the KMS key.

Question 16

A financial services company uses AWS Glue ETL jobs to process credit card transaction data stored in Amazon S3. The data includes PII such as names and credit card numbers. The security team requires that all PII be masked before the data is written to the curated zone of the data lake. The data engineer has implemented a Glue job that reads from the raw zone, applies a custom transform to mask credit card numbers using a regular expression, and writes to the curated zone. However, during a recent audit, the security team discovered that some masked data still contained partial credit card numbers (e.g., showing the last four digits) when viewed by analysts who should only see masked data. The company's policy is that credit card numbers must be completely masked, showing only asterisks or a fixed string like "XXXX-XXXX-XXXX-XXXX". The Glue job uses a DynamicFrame and applies a Map transform with a Python function that replaces digits with 'X'. The data is stored in Parquet format. What should the data engineer do to ensure complete masking of credit card numbers?

Accepted Answer

Replace the custom Python Map transform with a built-in Glue Transform for data masking, such as the Mask transform available in Glue Studio.. Option C is correct because AWS Glue provides a built-in Mask transform that can be applied directly in Glue Studio or via the AWS Glue API. This transform is designed to reliably obfuscate sensitive data like credit card numbers by replacing them with a fixed string (e.g., 'XXXX-XXXX-XXXX-XXXX') or asterisks, ensuring complete masking regardless of input format. The custom Python Map transform in the current implementation is error-prone because it relies on a regular expression that may not catch all patterns or partial digits, whereas the Mask transform uses predefined logic to guarantee full masking.

Answer

Use an AWS Glue crawler to classify the data and apply a masking rule based on the classification.

Answer

Enable server-side encryption with AWS KMS on the curated S3 bucket.

Answer

Change the output format from Parquet to CSV and use a different write mode.

Question 17

A company runs a nightly batch processing pipeline using AWS Glue ETL jobs. The pipeline reads data from an Amazon S3 bucket, transforms it, and writes results to an Amazon Redshift cluster. Recently, the data volume has increased significantly, and some Glue jobs are failing with the error 'java.lang.OutOfMemoryError: Java heap space'. The data engineer needs to modify the job configuration to prevent these failures without changing the code. The job currently uses 10 DPUs and processes data in a single Spark DataFrame. Which of the following is the MOST effective solution?

Accepted Answer

Increase the number of DPUs to 20 and enable job bookmarking for incremental processing.. Increasing DPUs from 10 to 20 provides more memory and compute resources, directly addressing the 'java.lang.OutOfMemoryError: Java heap space' caused by insufficient memory for the single DataFrame. Enabling job bookmarking allows incremental processing, which reduces the volume of data processed per run, further mitigating memory pressure without code changes.

Answer

Reduce the number of DPUs to 5 and increase the Spark executor memory by setting 'spark.executor.memory' in job parameters.

Answer

Change the script to use DynamicFrame instead of DataFrame and disable the 'spark.sql.shuffle.partitions' configuration.

Answer

Add a 'coalesce(1)' operation before writing to Redshift to reduce the number of output files.

Question 18

A company runs a real-time analytics platform on Amazon ECS that ingests streaming data from Amazon Kinesis Data Streams, processes it, and stores results in Amazon DynamoDB. The data volume spikes unpredictably, causing DynamoDB to throttle write requests. The application uses on-demand capacity mode. The data engineer notices that the throttling occurs on a specific partition due to a hot key. The hot key is a customer ID that receives a disproportionate number of writes. The application cannot change the partition key design immediately. The engineer needs to reduce throttling while maintaining low latency. Which solution is most effective?

Accepted Answer

Implement a write buffer using Amazon SQS, and have consumers write to DynamoDB at a controlled rate.. Option B is correct because buffering writes through Amazon SQS decouples the ingestion rate from DynamoDB's capacity, allowing consumers to write at a controlled pace. This directly mitigates throttling on the hot key without requiring a partition key redesign, and SQS provides low-latency, durable buffering suitable for real-time analytics.

Answer

Switch to provisioned capacity with auto scaling and increase the write capacity units.

Answer

Enable DynamoDB Accelerator (DAX) to cache the hot key writes.

Answer

Use DynamoDB Streams to trigger a Lambda function that retries throttled writes.

Question 19

A data engineering team is building a real-time analytics pipeline using Amazon Kinesis Data Streams, AWS Lambda, and Amazon DynamoDB. The Lambda function consumes records from the stream and writes aggregated data to a DynamoDB table. The application requires that each record be processed exactly once to avoid duplicates. The Lambda function is idempotent, but occasionally duplicate records are written due to retries from Kinesis. The team needs to ensure exactly-once semantics for DynamoDB writes. Which solution should they implement?

Accepted Answer

Use DynamoDB TransactWriteItems with a condition check on a unique transaction ID.. Option B is correct because DynamoDB TransactWriteItems with a condition check on a unique transaction ID ensures that the write only succeeds if the transaction ID does not already exist in the table. This provides exactly-once semantics by preventing duplicate writes even when Kinesis retries deliver the same record multiple times. The condition check acts as a distributed lock at the item level, guaranteeing idempotency without relying on downstream deduplication.

Answer

Enable DynamoDB Streams and use a second Lambda to deduplicate.

Answer

Use the Kinesis Client Library (KCL) to checkpoint after processing and ignore duplicates.

Answer

Ensure the Lambda function is idempotent by using upsert operations.

Question 20

A media company stores video files in an Amazon S3 bucket with S3 Standard storage class. The files are accessed frequently for the first 30 days, then rarely after that. However, the company must be able to restore any deleted file within 7 days. The company wants to minimize storage costs while meeting the access and retention requirements. What should a data engineer do?

Accepted Answer

Use S3 Intelligent-Tiering and enable S3 Versioning on the bucket.. Option D is correct because S3 Intelligent-Tiering automatically moves objects between access tiers (frequent, infrequent, and archive instant access) based on changing access patterns, which minimizes storage costs for data with unknown or changing access patterns. Enabling S3 Versioning allows the company to restore any deleted file within 7 days by reverting to a previous version, meeting the retention requirement without additional cost for a separate backup.

Answer

Use S3 Standard-IA storage class from the start.

Answer

Use a lifecycle policy to transition objects to S3 One Zone-IA after 30 days.

Answer

Use S3 Glacier Deep Archive after 30 days and enable S3 Object Lock for retention.