DEA-C01 Data Operations and Support Practice Test 4 — 15 Questions

Question 1

A data engineer is designing a data lake on Amazon S3 with sensitive data. The engineer needs to ensure that data at rest is encrypted and that access is logged for compliance. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Enable CloudTrail data events for S3 object-level operations.. Option C is correct because enabling CloudTrail data events for S3 object-level operations captures detailed logs of all read, write, and delete actions on objects, which is essential for compliance auditing. Option D is correct because enabling default encryption on the S3 bucket using SSE-KMS ensures that all objects stored in the bucket are encrypted at rest with AWS Key Management Service (KMS) keys, providing centralized control and auditability of encryption keys.

Answer

Enable S3 Select to filter data at read time.

Answer

Enable S3 Transfer Acceleration.

Answer

Enable S3 Block Public Access on the account.

Question 2

A data engineer is migrating a legacy data warehouse to Amazon Redshift. The engineer needs to load data from multiple sources efficiently. Which THREE services can be used to load data into Redshift? (Choose THREE.)

Accepted Answer

Use the COPY command to load from Amazon DynamoDB.. Options A, B, and D are correct: COPY from S3, COPY from DynamoDB, and Kinesis Data Firehose directly load into Redshift. Option C is wrong because S3 Transfer Acceleration is for uploading to S3, not loading into Redshift. Option E is wrong because AWS DMS can migrate data but is not a direct load service for Redshift; it can load into Redshift as a target.

Answer

Use AWS DMS to replicate data continuously.

Answer

Use S3 Transfer Acceleration.

Question 3

A company runs a data processing pipeline on Amazon EMR. The pipeline reads data from S3, processes it with Spark, and writes results back to S3. The engineer notices that the cluster is underutilized and wants to reduce costs. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Use Spot instances for task nodes.. Option A is correct because using Spot instances for task nodes in Amazon EMR can significantly reduce costs, as Spot instances are spare EC2 capacity offered at up to 90% discount compared to On-Demand instances. Since task nodes are stateless and can be added or removed without affecting cluster stability, they are ideal candidates for Spot instances, allowing the engineer to lower expenses while maintaining processing capacity.

Answer

Change the master node to a larger instance type.

Answer

Enable EMRFS consistent view.

Answer

Increase the number of core nodes to improve parallelism.

Question 4

A data engineer is troubleshooting a failed AWS Glue ETL job. The job reads from an S3 bucket and writes to an RDS MySQL database. The job fails with an 'Access Denied' error when trying to write to RDS. What is the most likely cause?

Accepted Answer

The IAM role associated with the Glue job does not have the necessary permissions to write to the RDS instance.. The error 'Access Denied' when writing to RDS indicates that the AWS Glue job's IAM role lacks the necessary permissions (e.g., rds-db:connect, or specific database-level GRANTs) to perform write operations on the RDS MySQL instance. AWS Glue uses the attached IAM role to authenticate and authorize actions against AWS services, and without proper IAM policies allowing access to the RDS resource, the write attempt is denied.

Answer

The Glue job is running in a VPC without a route to the internet.

Answer

The S3 bucket policy does not allow the Glue job to read the data.

Answer

The RDS instance is encrypted with a KMS key that the Glue job cannot access.

Question 5

A company uses Kinesis Data Streams to ingest real-time clickstream data. The data is processed by a Lambda function that writes to an S3 bucket. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' errors. Which action should be taken to resolve this issue?

Accepted Answer

Increase the number of shards in the Kinesis stream.. The 'ProvisionedThroughputExceededException' error indicates that the Lambda function is reading data from the Kinesis stream faster than the stream's shard-level throughput limits allow. Each shard in a Kinesis Data Stream supports up to 2 MB/s read throughput or 5 read transactions per second. Increasing the number of shards distributes the read load across more shards, raising the total available read throughput and resolving the throttling.

Answer

Increase the Lambda function's memory allocation.

Answer

Enable enhanced fan-out for the stream.

Answer

Reduce the batch size in the Lambda event source mapping.

Question 6

A data engineer is monitoring an Amazon Redshift cluster and notices that queries are taking longer than expected. The engineer checks the system tables and sees that many queries are waiting for 'WLM' resources. What is the most likely cause and recommended fix?

Accepted Answer

The WLM queue concurrency is set too low; increase the concurrency level.. Option D is correct because WLM queue wait indicates concurrency throttling. Option A is wrong because disk space is unrelated. Option B is wrong because sort keys improve scan efficiency, not concurrency. Option C is wrong because distribution style affects data movement, not queue wait.

Answer

The table sort keys are poorly designed; recreate tables with better sort keys.

Answer

The distribution style is set to ALL; change to KEY distribution.

Answer

The cluster is running low on disk space; resize the cluster.

Question 7

A data pipeline using AWS Glue ETL jobs is failing intermittently with the error 'Rate exceeded' when writing to an Amazon Redshift cluster. Which action is MOST effective to resolve this issue?

Accepted Answer

Enable auto-tuning on the Redshift cluster and use concurrency scaling.. Option A is correct because enabling Redshift auto-tuning with concurrency scaling can automatically handle write spikes. Option B is wrong because increasing Glue job timeout does not address rate limiting. Option C is wrong because disabling Redshift WLM concurrency scaling would exacerbate the issue. Option D is wrong because using a different data format does not affect write rate limits.

Answer

Increase the timeout of the Glue ETL job to allow more time for retries.

Answer

Disable workload management (WLM) concurrency scaling in Redshift.

Answer

Change the output file format from Parquet to CSV to reduce write size.

Question 8

A company is using Amazon S3 as a data lake. Data is ingested hourly from multiple sources. The data engineer needs to ensure that once an object is written to S3, it cannot be overwritten or deleted for 30 days. Which S3 feature should be used?

Accepted Answer

Enable S3 Object Lock with a retention period of 30 days.. Option B is correct because S3 Object Lock with retention mode prevents overwrites and deletes. Option A is wrong because it only prevents accidental deletion. Option C is wrong because it only protects against delete, not overwrite. Option D is wrong because it manages lifecycle, not immutability.

Answer

Use S3 Lifecycle policies to transition objects to Glacier after 30 days.

Answer

Enable S3 Versioning and MFA Delete.

Answer

Configure a bucket policy that denies s3:DeleteObject for all principals.

Question 9

A data engineer is running a Spark job on Amazon EMR. The job reads from S3, processes data, and writes to S3. The job is taking longer than expected. The engineer notices that the job is spending a lot of time in the 'GC' (garbage collection) phase. Which configuration change is most likely to improve performance?

Accepted Answer

Increase the spark.executor.memory setting.. Option A is correct because increasing executor memory reduces GC frequency. Option B is wrong because it reduces parallelism. Option C is wrong because it reduces memory per task. Option D is wrong because it reduces memory and may increase GC.

Answer

Increase the spark.sql.shuffle.partitions.

Answer

Decrease the number of executor cores.

Answer

Decrease the spark.executor.memoryOverhead.

Question 10

A team manages an Amazon DynamoDB table with on-demand capacity. Recently, they noticed increased throttling errors during peak hours. The table has a Lambda trigger that processes changes and writes to an S3 bucket. Which design change would BEST reduce throttling?

Accepted Answer

Implement DynamoDB Accelerator (DAX) to cache frequent reads.. Option D is correct because DynamoDB Accelerator (DAX) provides an in-memory cache that reduces the number of read requests hitting the table, which can alleviate throttling during peak hours. The question describes throttling errors, which are typically caused by exceeding the table's read or write capacity; DAX offloads read traffic, reducing the load on the table and thus decreasing throttling events.

Answer

Switch the table to provisioned capacity and enable auto-scaling.

Answer

Increase the write capacity units to handle the peak load.

Answer

Enable S3 bucket versioning to reduce the number of writes.

Question 11

A company uses AWS Database Migration Service (DMS) to migrate an on-premises Oracle database to Amazon RDS for PostgreSQL. The migration completes successfully, but the data engineer notices that some tables have fewer rows in the target than the source. Which DMS setting should be checked to ensure full data migration?

Accepted Answer

The task logs show that some rows failed to apply due to data type conversion errors.. Option B is correct because if some rows failed to apply due to data type conversion errors, those rows would be logged as errors and not written to the target, resulting in fewer rows. AWS DMS task logs capture these failures, and checking them is the direct way to identify rows that were skipped or rejected during migration. This is the most common cause of row count mismatches after a successful DMS task.

Answer

The LOB mode is set to 'Limited LOB mode' instead of 'Full LOB mode'.

Answer

The 'Enable validation' option is turned off.

Answer

The 'Parallel Apply' feature is disabled, slowing down the migration.

Question 12

A data engineer needs to transfer 50 TB of data from an on-premises HDFS cluster to Amazon S3. The on-premises network has a 1 Gbps link to AWS. Which AWS service should be used to perform the transfer efficiently?

Accepted Answer

AWS DataSync. Option B is correct because AWS DataSync is designed for large-scale data transfers over the network. Option A is wrong because S3 Transfer Acceleration speeds up uploads but is not a transfer service. Option C is wrong because AWS Snowball is for offline data transfer, but the network link is sufficient. Option D is wrong because AWS Direct Connect is a network connection, not a data transfer service.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Snowball Edge

Answer

AWS Direct Connect

Question 13

A data engineer needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited to 100 Mbps, and the data transfer must be completed within 5 days. What is the most cost-effective solution?

Accepted Answer

Use AWS Snowball Edge to physically ship the data.. With 10 TB of data and a 100 Mbps link, the theoretical transfer time over the internet is approximately 10 days (10 TB * 8 / 100 Mbps = 800,000 seconds ≈ 9.26 days), which exceeds the 5-day requirement. AWS Snowball Edge is the most cost-effective solution because it bypasses the network bottleneck entirely by physically shipping the data, and it is designed for large-scale data transfers where network constraints make online transfer impractical.

Answer

Use S3 Transfer Acceleration to speed up the transfer over the internet.

Answer

Use AWS DataSync over the internet to transfer the data.

Answer

Set up an AWS Direct Connect connection to increase bandwidth.

Question 14

An Amazon Kinesis Data Streams application is lagging behind. The data records are small (1 KB) and the shard count is 10. The consumer uses the KCL with default configuration. Which action will MOST effectively reduce the consumer lag?

Accepted Answer

Increase the number of KCL workers per shard (e.g., 2 workers per shard).. Option A is correct because the KCL (Kinesis Client Library) uses a single worker per shard by default, and each worker processes records sequentially within that shard. Increasing the number of workers per shard (e.g., 2 workers) allows parallel processing of the same shard’s records, directly reducing consumer lag when records are small (1 KB) and the bottleneck is CPU or processing time per record, not throughput limits.

Answer

Use Enhanced Fan-Out to provide dedicated throughput.

Answer

Increase the number of shards to 20.

Answer

Reduce the record size by compressing the data.

Question 15

A company is using Amazon Athena to query data stored in S3. Queries are failing with 'HIVE_INVALID_PARTITION' errors. What is the most likely cause?

Accepted Answer

A partition folder in S3 has been deleted or moved, but the table metadata still references it.. The 'HIVE_INVALID_PARTITION' error in Amazon Athena occurs when the table's partition metadata in the AWS Glue Data Catalog (or Hive metastore) references a partition folder that no longer exists in the S3 bucket. Athena relies on the metadata to locate data files; if a partition folder is deleted or moved without updating the metadata, queries fail because Athena cannot find the expected data location.

Answer

The S3 bucket is configured with a bucket policy that denies access to the Athena service.

Answer

The data is compressed with gzip, but the table definition expects uncompressed data.

Answer

The data files are in CSV format but the table definition expects Parquet.