DEA-C01 Practice Exam 68 — 20 Questions

Question 1

Your organization uses Amazon Redshift for analytical workloads. You have noticed that queries are slow on a large fact table. The table is distributed by KEY on the customer_id column and sorted by transaction_date. The table is frequently updated with new records. To improve query performance, you decide to implement a distribution style that reduces data movement. Which action should you take?

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

Accepted Answer

Change the distribution style to EVEN to distribute rows evenly across all nodes.. Changing to EVEN distribution distributes rows evenly across all nodes, reducing data movement during queries that do not benefit from the current KEY distribution on customer_id. This is especially useful for large, frequently updated tables where data skew or redistribution can cause performance issues. Option A (ALL) is wrong because it duplicates the entire table on every node, which is inefficient for large tables. Option B (AUTO) is wrong because it may not choose the optimal distribution style for this workload. Option D (adding a sort key) improves sort performance but does not directly reduce data movement.

Answer

Change the distribution style to ALL to put a copy of the table on every node.

Answer

Change the distribution style to AUTO to let Redshift choose the best distribution.

Answer

Change the sort key to include customer_id as well.

Question 2

Refer to the exhibit. A data engineer is troubleshooting an AWS Glue ETL job that fails with an access denied error when writing to S3. The IAM role attached to the Glue job has the policy shown. What is the most likely cause of the error?

Accepted Answer

The Glue job is writing to an S3 bucket that is not included in the Resource ARN. The IAM policy shown in the exhibit explicitly restricts the `s3:PutObject` action to a specific ARN pattern (e.g., `arn:aws:s3:::example-bucket/*`). If the Glue job attempts to write to a different S3 bucket—one not matching that ARN—the request will be denied with an access denied error. AWS Glue ETL jobs use the attached IAM role's permissions, and a mismatch between the target bucket and the resource ARN in the policy is the most common cause of such failures.

Answer

The Glue job is missing the s3:ListBucket permission on the bucket

Answer

The S3 bucket is encrypted with AWS KMS and the policy does not include kms:Decrypt permissions

Answer

The Glue job does not have permission to call glue:StartJobRun

Question 3

A company uses Amazon DynamoDB as a data store for a real-time dashboard application. The application performs point lookups and range queries on a table that has a partition key and sort key. The table uses on-demand capacity mode. Recently, the application's response time has increased, and CloudWatch metrics show high 'ThrottledRequests' for the table. The application uses the AWS SDK with default retry settings. The data access pattern is read-heavy with occasional spikes. What is the most effective way to reduce throttling?

Accepted Answer

Implement exponential backoff with jitter in the application code.. DynamoDB on-demand mode can throttle requests when traffic exceeds the table's previous peak by more than double. Implementing exponential backoff with jitter in the application code allows retries to spread out and succeed without overwhelming the table. Option A is incorrect because switching to provisioned capacity requires accurate capacity planning and may still throttle during unexpected spikes. Option B is incorrect because DAX caches frequently read items, reducing read load, but it does not directly address throttling caused by exceeding the table's throughput limits; it also adds cost and complexity. Option C is incorrect because increasing read capacity units applies only to provisioned capacity mode, not on-demand.

Answer

Switch the table to provisioned capacity and set the read capacity units to a high value.

Answer

Enable DynamoDB Accelerator (DAX) to cache frequently read items.

Answer

Increase the read capacity units to a higher value.

Question 4

A financial services company uses Amazon Redshift for its data warehouse. The compliance team requires that all access to the database be logged, including the SQL queries executed, and that the logs be stored in a separate S3 bucket that is encrypted with a customer-managed KMS key. Additionally, the logs must be retained for 7 years. The data engineer has enabled audit logging on the Redshift cluster and configured it to deliver logs to an S3 bucket. However, the compliance team reports that the logs are not being delivered. The S3 bucket policy allows the Redshift service to write logs. What is the most likely reason for the failure?

Accepted Answer

The S3 bucket policy does not include a statement allowing the Redshift service principal to write objects.. Amazon Redshift audit logging requires a specific S3 bucket policy that grants the Redshift service principal permission to write objects. Without this policy statement, log delivery fails. Option A is incorrect because the S3 bucket can be in a different region. Option B is incorrect because versioning does not block log delivery. Option C is incorrect because the issue is the bucket policy, not the KMS key policy; the Redshift service principal needs s3:PutObject permission, not KMS decrypt.

Answer

The S3 bucket is in a different region than the Redshift cluster.

Answer

The S3 bucket has versioning enabled, which blocks log delivery.

Answer

The KMS key policy does not grant the Redshift service principal decrypt permissions.

Question 5

A data engineer is troubleshooting a slow-running query on Amazon Redshift. The query scans a large table but returns few rows. Which diagnostic step should be taken first?

Accepted Answer

Use EXPLAIN to review the query plan.. When a query scans a large table but returns few rows, the most likely cause is an inefficient query plan—such as a full table scan instead of using indexes or zone maps. Using EXPLAIN first reveals the execution plan, allowing the engineer to identify whether the query is performing unnecessary sequential scans, missing filter pushdown, or using suboptimal join strategies. This diagnostic step should always precede tuning actions like ANALYZE or VACUUM, which address data distribution or storage bloat rather than query planning.

Answer

Run ANALYZE on the table.

Answer

Check the concurrency scaling status.

Answer

Run VACUUM on the table.

Question 6

A data engineer needs to transfer 50 TB of data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited to 500 Mbps. Which TWO methods are appropriate for this transfer? (Choose TWO.)

Accepted Answer

Order an AWS Snowball Edge device to physically ship the data.. Options B and E are correct. AWS Snowball Edge is a physical device suitable for transferring large amounts of data (like 50 TB) when network bandwidth is limited (500 Mbps). AWS DataSync can also transfer data over the network with built-in optimization, making it appropriate for this scenario. Options A and C are incorrect: AWS Direct Connect provides a dedicated connection but does not increase bandwidth beyond 500 Mbps, and S3 Transfer Acceleration speeds up transfers but still relies on the same limited bandwidth. Option D is incorrect: Amazon Kinesis Data Firehose is designed for real-time streaming, not bulk data transfer.

Answer

Set up an AWS Direct Connect connection for higher bandwidth.

Answer

Use S3 Transfer Acceleration to upload over the internet.

Answer

Use Amazon Kinesis Data Firehose to stream the data.

Question 7

An e-commerce company ingests clickstream data from their website into Amazon S3. The data is in JSON format, and each file is about 10 MB. They need to transform the data into a columnar format for analytics and load it into Amazon Redshift nightly. The transformation should be cost-effective and require minimal operational overhead. Which approach meets these requirements?

Accepted Answer

Use AWS Glue ETL job to convert to Parquet and load into Redshift.. AWS Glue ETL is the correct choice because it is a serverless, managed service that can efficiently convert JSON to Parquet (a columnar format optimized for Redshift) and load the data into Redshift with minimal operational overhead. The nightly batch processing of 10 MB files is well-suited for Glue's pay-per-use pricing, making it cost-effective without requiring infrastructure management.

Answer

Use Amazon Redshift COPY command to load JSON directly.

Answer

Use Amazon EMR with Spark to transform and load data.

Answer

Use AWS Lambda to transform each file and write to Redshift.

Question 8

A company has a Glue ETL job that reads from an Amazon RDS for MySQL table and writes to Amazon S3. The job runs hourly and processes new records based on a 'last_modified' timestamp column. Recently, the job started missing some records because the timestamp in MySQL is stored with microsecond precision but Glue's job bookmark only tracks second precision. Which solution addresses this issue?

Accepted Answer

Use a job parameter to store the last processed timestamp with millisecond precision and query records greater than that value.. AWS Glue job bookmarks track timestamps with only second precision, so records with microsecond differences within the same second are missed. By using a custom job parameter to store the last processed timestamp with millisecond precision and querying records greater than that value, you bypass Glue's bookmark limitation and capture all new or modified records.

Answer

Increase the job frequency to every 30 minutes.

Answer

Run a full refresh of the table each time instead of incremental.

Answer

Modify the MySQL table to use a DATE data type instead of TIMESTAMP.

Question 9

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 10

A company is using Amazon DynamoDB for an e-commerce application. The application experiences sudden spikes in traffic, causing throttling errors. The data engineer needs to handle the spikes cost-effectively. Which solution should be used?

Accepted Answer

Use DynamoDB auto scaling with a target utilization of 70%.. DynamoDB auto scaling with a target utilization of 70% allows the table to dynamically adjust provisioned read/write capacity based on actual traffic patterns, handling sudden spikes without manual intervention while avoiding over-provisioning. This balances performance and cost by scaling up during spikes and scaling down during low traffic, preventing throttling errors cost-effectively.

Answer

Implement DynamoDB Accelerator (DAX) to cache reads.

Answer

Switch to DynamoDB on-demand capacity mode.

Answer

Provision high read and write capacity units to handle peak traffic.

Question 11

A data engineer notices that an AWS Glue ETL job processing data from Amazon S3 to Amazon Redshift has been failing intermittently with the error 'S3ServiceException: SlowDown'. Which action is MOST likely to resolve this issue?

Accepted Answer

Implement exponential backoff and retry logic in the Glue job.. The 'S3ServiceException: SlowDown' error indicates that the AWS Glue job is making requests to Amazon S3 at a rate that exceeds the bucket's request rate limits. Implementing exponential backoff and retry logic (option C) is the most effective solution because it reduces the effective request rate by introducing delays between retries, allowing S3 to recover from throttling. Option A is incorrect because increasing partitions would likely increase the number of concurrent requests, exacerbating throttling. Option B is incorrect because switching to a larger worker type does not affect the rate of S3 requests. Option D is incorrect because S3 Transfer Acceleration improves network transfer speed but does not reduce request throttling.

Answer

Increase the number of partitions in the Glue job to parallelize reads.

Answer

Switch from a Standard to a G.2X large Glue worker type.

Answer

Enable S3 Transfer Acceleration on the source bucket.

Question 12

A company uses Kinesis Data Streams to ingest clickstream data. They notice that the data processing latency increases as the number of shards grows. What is the most likely cause and solution?

Accepted Answer

Reduce the number of shards or increase the number of consumers.. As the number of shards increases, the total throughput of the stream increases, but each shard has a fixed limit of 5 read transactions per second. If the number of consumers remains constant, each consumer must poll more shards sequentially, increasing the per-record processing latency. Reducing the number of shards or adding more consumers distributes the polling load, reducing the time each consumer spends waiting for shard-level throttling.

Answer

Increase the Kinesis Producer Library (KPL) batch size.

Answer

Use enhanced fan-out to allow multiple consumers per shard.

Answer

Increase the number of shards to handle more data.

Question 13

A data engineer is designing a data store for a real-time analytics application that requires sub-millisecond read and write latency for time-series data. The data volume is expected to grow to hundreds of terabytes. Which TWO AWS services should the engineer consider? (Choose TWO.)

Accepted Answer

Amazon DynamoDB with Time-to-Live (TTL). Amazon DynamoDB with TTL is correct because it provides single-digit millisecond read and write latency at any scale, making it suitable for real-time time-series data. The TTL feature automatically expires old records, which helps manage the hundreds of terabytes of data without manual intervention, keeping storage costs predictable.

Answer

Amazon Redshift

Answer

Amazon ElastiCache for Redis

Answer

Amazon RDS for PostgreSQL

Question 14

A streaming application sends data to Amazon Kinesis Data Streams. The data must be enriched with reference data from an Amazon DynamoDB table in real-time. Which AWS service can be used to perform this enrichment with minimal latency?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Analytics for Apache Flink is correct because it allows you to run Apache Flink applications that can read from a Kinesis data stream, perform stateful stream processing, and enrich records in real-time by joining with reference data stored in DynamoDB. Flink's asynchronous I/O and managed state enable sub-second enrichment latency without the cold-start delays or concurrency limits of Lambda-based approaches.

Answer

Amazon Kinesis Data Firehose with Lambda transformation

Answer

AWS Lambda function triggered by Kinesis Data Streams

Answer

AWS Glue streaming ETL

Question 15

A company uses AWS Glue ETL jobs to process data from an S3 data lake. The job reads data in CSV format, transforms it, and writes to Parquet. The job runs daily and takes 2 hours to complete. The data volume is increasing by 20% each month. The engineer wants to reduce the job runtime. Which action is most effective?

Accepted Answer

Partition the input data in S3 by date and use partition pruning in the job. Most effective because partitioning the input data by date and using partition pruning allows the Glue ETL job to read only the relevant partitions instead of scanning the entire S3 data lake. This drastically reduces the amount of data processed, which directly addresses the growing data volume and shortens job runtime. Partition pruning is a core optimization for Spark-based Glue jobs, as it leverages Hive-style partitioning to skip unnecessary files.

Answer

Increase the number of DPUs for the Glue job

Answer

Enable compression on the input CSV files

Answer

Switch from Python Shell to Spark ETL

Question 16

A company uses Amazon RDS for MySQL to store application data. The database contains personally identifiable information (PII). The security team requires that all data be encrypted at rest using AWS KMS. The database is currently unencrypted. The data engineer needs to enable encryption without significant downtime. Which approach should the data engineer take?

Accepted Answer

Take a snapshot of the database, copy it with encryption enabled, and restore from the encrypted snapshot.. To enable encryption on an existing unencrypted RDS instance, you must take a snapshot of the database, copy it with encryption enabled (using AWS KMS), and restore from the encrypted snapshot. This process involves downtime during the restore but is the only supported method. Option A is incorrect because AWS DMS can migrate data to a new encrypted RDS instance, but that adds complexity and is not the simplest approach. Option C is incorrect because read replicas cannot be promoted to primary if encryption is enabled on the replica but not on the source; also, the source must be encrypted. Option D is incorrect because you cannot modify an existing RDS instance to enable encryption directly; encryption can only be enabled when creating a new instance from an encrypted snapshot.

Answer

Use AWS DMS to migrate data to a new encrypted RDS instance continuously.

Answer

Create a read replica with encryption enabled and promote it to primary.

Answer

Modify the RDS instance and enable encryption in the configuration.

Question 17

A company runs an Amazon RDS for PostgreSQL database and wants to capture change data (inserts, updates, deletes) to stream into Amazon Kinesis Data Streams for real-time processing. Which AWS service should be used to capture the changes directly from the database?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication. AWS DMS with ongoing replication (change data capture) is the correct service because it can continuously capture insert, update, and delete operations from the PostgreSQL transaction logs (WAL) and stream them to a Kinesis Data Streams endpoint. This allows real-time processing without modifying the source database or requiring application-level triggers.

Answer

Amazon RDS automated snapshots

Answer

AWS Glue ETL job scheduled to run every minute

Answer

Amazon Kinesis Agent

Question 18

A company uses Amazon DynamoDB for a gaming application. They need to store player session data that expires after 24 hours. Which DynamoDB feature should they use to automatically delete expired items?

Accepted Answer

Time to Live (TTL). DynamoDB Time to Live (TTL) is the correct feature because it allows you to define a per-item timestamp attribute (e.g., `expireAt`) that DynamoDB automatically deletes once that timestamp is reached. This is ideal for expiring session data after 24 hours without requiring custom code or scheduled jobs to scan and delete items, reducing cost and operational overhead.

Answer

DynamoDB auto scaling

Answer

DynamoDB Streams

Answer

Point-in-time recovery

Question 19

A company uses AWS Glue to transform data stored in Amazon S3. During a run, the job fails with a 'OutOfMemoryError' in the Spark executor. The job processes 2 TB of parquet files using 10 DPUs. The data is evenly distributed across partitions. Which action would MOST likely resolve the issue without impacting the job logic?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. The OutOfMemoryError in the Spark executor indicates that the available memory per executor is insufficient for the data being processed. Increasing the number of DPUs allocated to the Glue job increases the total memory and compute resources available, allowing Spark to handle the 2 TB dataset without changing the job logic.

Answer

Enable S3 request rate increase to speed up data reading.

Answer

Repartition the data to a larger number of partitions.

Answer

Change the input format from Parquet to Snappy-compressed CSV.

Question 20

A data engineer notices that an Amazon S3 bucket policy is overly permissive. What is the best practice to restrict access while maintaining required permissions?

Accepted Answer

Write a new bucket policy that denies all actions.. The bucket policy is overly permissive, so writing a new bucket policy that denies all actions immediately restricts all access. While this may temporarily block required permissions, it is the most direct way to address the bucket policy issue; you can then refine the policy to allow only necessary actions. Option D does not change the bucket policy and thus does not resolve the problem. Option A makes permissions even more permissive. Option C is not a standard AWS feature.

Answer

Grant full S3 access using a new IAM policy.

Answer

Use an S3 blocklist to restrict access.

Answer

Attach the AWS managed policy AmazonS3ReadOnlyAccess to the IAM user.