DEA-C01 Data Operations and Support Practice Test 6 — 15 Questions

Question 1

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?

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

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 2

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 3

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.. A wait for WLM (Workload Management) resources indicates that queries are being throttled due to insufficient concurrency slots. Increasing the WLM queue concurrency allows more queries to run simultaneously. Option A is incorrect because sort keys affect query scan efficiency, not WLM queue waits. Option B is incorrect because distribution style affects data redistribution, not concurrency throttling. Option D is incorrect because low disk space would cause different errors, not specifically WLM queue waits.

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 4

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.. Enabling auto-tuning on the Redshift cluster and using concurrency scaling dynamically adds cluster capacity to absorb spikes in write requests, directly addressing the 'Rate exceeded' error. This error typically occurs when the Glue ETL job's write throughput exceeds the cluster's current capacity, and concurrency scaling provides additional query queues to handle the load without manual intervention.

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 5

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.. S3 Object Lock with a retention period of 30 days enforces a write-once-read-many (WORM) policy that prevents objects from being overwritten or deleted during the retention period. This meets the requirement exactly, as it applies to both new and existing objects when enabled on a versioning-enabled bucket.

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 6

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.. 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 7

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.. 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 8

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.. 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 9

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. AWS DataSync is the correct choice because it is designed for large-scale data transfers over the network, handling 50 TB efficiently over a 1 Gbps link by parallelizing transfers and automating scheduling. Amazon S3 Transfer Acceleration only improves upload speed over long distances but is not a dedicated transfer service. AWS Snowball Edge would be slower due to shipping and is unnecessary since the network link is sufficient. AWS Direct Connect provides a dedicated network connection but is not a data transfer service.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Snowball Edge

Answer

AWS Direct Connect

Question 10

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 11

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.

Question 12

A company runs a nightly Amazon EMR job that processes data from S3 and writes results back to S3. The job fails with 'OutOfMemoryError' in the reduce phase. The cluster currently uses 5 m5.xlarge instances. Which cost-effective change should the data engineer make?

Accepted Answer

Increase the number of reducers (mapreduce.reduce.tasks) and keep the same instance type.. Increasing the number of reducers (mapreduce.reduce.tasks) distributes the memory load across the existing 5 m5.xlarge instances, reducing the per-reducer memory pressure and preventing OutOfMemoryError without adding cost. Option A (adding more core nodes) increases cost without directly addressing reducer memory. Option C (reducing input data) may affect completeness and is not a proper fix. Option D (switching to r5.xlarge) is more expensive per instance, making it less cost-effective.

Answer

Increase the number of core nodes to 10.

Answer

Reduce the input data size by filtering early in the job.

Answer

Switch to r5.xlarge instances for more memory per instance.

Question 13

A data engineer is using Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is delivered in 5-minute intervals. However, the engineer notices that the data in S3 is often delayed by up to 30 minutes. Which configuration change would most likely reduce the delay?

Accepted Answer

Decrease the 'Buffer interval' from 300 seconds to 60 seconds.. The buffer interval determines the maximum time Firehose will wait before delivering data, regardless of buffer size. Decreasing it from 300 to 60 seconds forces more frequent deliveries, reducing the delay. Option B (compression) reduces data size, which could slow buffer filling and potentially increase delay if the buffer size trigger is not met. Option C (increasing buffer size) would cause Firehose to wait longer for the buffer to fill, increasing delay. Option D (dynamic partitioning) affects data organization, not delivery frequency, so it does not reduce delay.

Answer

Enable compression (GZIP) on the Firehose delivery stream.

Answer

Increase the 'Buffer size' from 5 MB to 50 MB.

Answer

Enable 'Dynamic partitioning' on the Firehose stream.

Question 14

A data engineer notices that an Amazon RDS for PostgreSQL instance's CPU utilization is consistently above 90% during business hours. The database is used for reporting queries. Which action should be taken FIRST to improve performance?

Accepted Answer

Enable Performance Insights and review slow queries.. The first step in diagnosing high CPU utilization on an RDS for PostgreSQL instance used for reporting queries is to identify the root cause. Enabling Performance Insights provides a detailed view of database load, wait events, and SQL query performance, allowing the data engineer to pinpoint slow or inefficient queries that are consuming CPU resources. Without this diagnostic data, any other action would be premature and could lead to unnecessary cost or complexity.

Answer

Enable Multi-AZ deployment for automatic failover.

Answer

Create a read replica to offload reporting queries.

Answer

Increase the instance size to a larger instance class.

Question 15

A data engineer is monitoring an Amazon RDS for PostgreSQL instance. The engineer wants to set up alerts for high CPU utilization and low free storage space. Which AWS services can be used together to achieve this? (Choose TWO.)

Accepted Answer

Amazon Simple Notification Service (SNS). Amazon CloudWatch is the correct service because it can monitor RDS metrics such as CPUUtilization and FreeStorageSpace, and trigger alarms based on thresholds. Amazon SNS is correct because it can receive CloudWatch alarm notifications and deliver them via email, SMS, or other endpoints, enabling the data engineer to be alerted when high CPU or low storage conditions occur.

Answer

AWS CloudTrail

Answer

AWS Config

Answer

Amazon Route 53