DEA-C01 Data Operations and Support Practice Test 23 — 15 Questions

Question 1

A financial services company runs a critical data pipeline using AWS Step Functions to orchestrate multiple AWS Lambda functions and AWS Glue jobs. The pipeline processes transaction data and must complete within 15 minutes to meet a service-level agreement (SLA). Recently, the pipeline has been failing intermittently with a 'StateMachineExecutionLimitExceeded' error. The Step Functions state machine is configured with a Standard type. The company has a single state machine that runs on demand. The error occurs when multiple requests are submitted simultaneously. What should the team do to prevent this error?

Accepted Answer

Request a service quota increase for concurrent executions of Standard Workflows.. Option D is correct because Standard Workflows have a limit of 1 million state transitions and a maximum execution duration of 1 year, but they also have a limit of 1,000 open executions per state machine. However, the error 'StateMachineExecutionLimitExceeded' indicates the account-level limit for concurrent executions is exceeded. The default limit for Standard Workflows is 1,000 executions per state machine. To handle spikes, the team should request a limit increase. Option A is wrong because Express Workflows are designed for high-volume event-driven workloads and have different limits (e.g., 5-minute max duration), which may not meet the SLA. Option B is wrong because increasing the timeout does not affect execution limits. Option C is wrong because Lambda concurrency limits are separate from Step Functions execution limits.

Answer

Increase the state machine execution timeout to 30 minutes.

Answer

Switch the state machine type to Express Workflow to handle higher throughput.

Answer

Increase the Lambda function reserved concurrency to 100.

Question 2

A data engineer is tasked with reducing costs for an Amazon Redshift cluster. The cluster is used for both ETL workloads and BI reporting. The engineer observes that the cluster is over-provisioned during off-peak hours. Which action would be MOST effective in reducing costs while maintaining performance during peak hours?

Accepted Answer

Enable concurrency scaling to automatically add cluster capacity during peak hours.. Option B is correct because concurrency scaling adds additional capacity on demand and is cost-effective for variable workloads. Option A is incorrect because reserved instances require upfront payment and are for steady-state, not variable. Option C is incorrect because reducing node count may impact performance during peak hours. Option D is incorrect because RA3 nodes are for managed storage, not cost reduction for variable workloads.

Answer

Switch to RA3 node types for managed storage.

Answer

Purchase Reserved Instances for the cluster.

Answer

Reduce the number of nodes in the cluster.

Question 3

A data engineer is troubleshooting an Amazon Redshift cluster that is running slowly. The cluster has 4 dc2.large nodes. The engineer runs a query that scans a large table and notices that the query uses only a single slice instead of all slices. The table is distributed with DISTSTYLE ALL. What is the most likely reason for the query using only one slice?

Accepted Answer

The table uses DISTSTYLE ALL, which stores the entire table on a single slice per node.. Option B is correct because DISTSTYLE ALL means the entire table is copied to every node, but within each node, the data is stored on a single slice. Redshift slices are determined by the number of cores per node; for dc2.large nodes, there are 2 slices per node. However, with DISTSTYLE ALL, each node stores a full copy of the table, but the table is not distributed across slices; it is replicated to each node, and queries may not parallelize across slices efficiently. Actually, with DISTSTYLE ALL, the table is replicated to all nodes, but within each node, the data is stored on a single slice (the leader node assigns the table to one slice per node). So queries that scan the entire table may only use one slice per node, leading to underutilization. Option A is wrong because the sort key affects data ordering, not slice utilization. Option C is wrong because WLM queues affect concurrency, not slice usage. Option D is wrong because the query is not using a single node but multiple nodes, but only one slice per node.

Answer

The query is running on the leader node instead of the compute nodes.

Answer

The workload management (WLM) queue is configured with a single query slot.

Answer

The table does not have a sort key defined.

Question 4

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.. Option C is correct because DynamoDB on-demand mode accommodates traffic spikes but can throttle if the spike exceeds the previous peak by more than double. Implementing exponential backoff with jitter in the application allows retries to succeed without overwhelming the table. Option A is wrong because switching to provisioned capacity would require predicting the peak and may still throttle if the spike is higher. Option B is wrong because DAX (DynamoDB Accelerator) is an in-memory cache that reduces read load but does not eliminate throttling from write spikes; it also adds cost and complexity. Option D is wrong because increasing read capacity units only applies to provisioned mode.

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 5

A company uses Amazon S3 to store log files from multiple applications. The logs are encrypted with AWS KMS (SSE-KMS). A data engineer needs to grant a new IAM user read-only access to the logs. The engineer attaches an S3 bucket policy that allows s3:GetObject and a KMS key policy that allows kms:Decrypt. However, the user still receives an 'Access Denied' error when trying to download an object. What is the MOST likely missing permission?

Accepted Answer

The user's IAM policy does not include kms:Decrypt permission.. Option C is correct because to use SSE-KMS, the user needs kms:Decrypt, but also the IAM policy must allow kms:Decrypt, not just the key policy. The key policy alone is not sufficient if the IAM user's policy denies or does not allow the action. Option A is incorrect because s3:ListBucket is for listing, not downloading. Option B is incorrect because s3:GetObjectVersion is for versioned buckets. Option D is incorrect because kms:GenerateDataKey is for encryption, not decryption.

Answer

The user does not have s3:ListBucket permission on the bucket.

Answer

The user does not have s3:GetObjectVersion permission.

Answer

The user does not have kms:GenerateDataKey permission.

Question 6

A company runs a data lake on Amazon S3 with AWS Lake Formation for access control. The data lake contains sensitive customer information. A data scientist needs to query the data using Amazon Athena. The data scientist has been granted SELECT permission on the database and tables via Lake Formation. However, when the data scientist runs a query in Athena, they receive an error: 'Access denied. Please check your permissions.' The IAM role used by Athena has the following permissions: s3:GetObject, s3:ListBucket, and lakeformation:GetDataAccess. The Lake Formation admin has verified that the data scientist is a member of a Lake Formation data lake location and has been granted 'Describe' and 'Select' permissions on the table. What is the most likely reason for the access denied error?

Accepted Answer

The S3 bucket policy does not grant the Athena IAM role access to the S3 location.. Option A is correct because Lake Formation requires the IAM role to have lakeformation:GetDataAccess permission, but also the role needs permissions to the underlying S3 location. If the S3 bucket policy does not allow the Athena IAM role to access the bucket, the request will be denied. The data scientist is granted permissions in Lake Formation, but the S3 bucket policy must also grant access to the IAM role. Option B is wrong because the role already has lakeformation:GetDataAccess. Option C is wrong because the error is not about Lake Formation tag access. Option D is wrong because Athena uses the IAM role's permissions, not the user's IAM permissions directly.

Answer

The data scientist is not assigned to the correct Lake Formation tag.

Answer

The Athena IAM role is missing lakeformation:GetEffectivePermissions permission.

Answer

The data scientist's IAM user lacks the necessary S3 permissions.

Question 7

A data engineer is designing a disaster recovery plan for an Amazon RDS for MySQL database. The database must have a Recovery Point Objective (RPO) of less than 5 minutes and a Recovery Time Objective (RTO) of less than 30 minutes. Which TWO actions should the engineer take to meet these requirements?

Accepted Answer

Enable automated backups with a 1-day retention period and point-in-time recovery.. Options B and D are correct. Multi-AZ provides automatic failover to a standby in another AZ, meeting RTO. Automated backups with point-in-time recovery provide RPO of 5 minutes. Option A is wrong because read replicas are for read scaling, not DR. Option C is wrong because cross-Region read replicas have higher RTO due to manual promotion. Option E is wrong because single-AZ does not provide automatic failover.

Answer

Enable automated backups with a 5-minute retention period.

Answer

Create a cross-Region read replica.

Answer

Use a single-AZ instance with a standby in another Region.

Question 8

A company uses Amazon S3 to store log files from multiple applications. The logs are written in JSON format. A data engineer wants to use Amazon Athena to query these logs. The logs are stored in a bucket with the following structure: 's3://logs/app1/date=2021-01-01/'. The engineer creates an Athena table with partitions. However, when querying, Athena returns zero results for partitions that exist. The engineer has run MSCK REPAIR TABLE to add partitions. What is the most likely cause of the issue?

Accepted Answer

The partition key name in the table definition does not match the S3 folder naming convention.. Option C is correct because Athena relies on the partition metadata stored in the Glue Data Catalog. If the partition folder structure does not match the table's partition definition (e.g., the folder is named 'date=2021-01-01' but the table's partition key is named 'dt'), MSCK REPAIR will not register the partitions. The partition key name must match the folder prefix. Option A is wrong because Athena supports JSON format. Option B is wrong because MSCK REPAIR does add partitions if the structure matches. Option D is wrong because the data is already in the S3 bucket; no need to copy.

Answer

The MSCK REPAIR TABLE command failed silently.

Answer

The log files are in JSON format and Athena does not support JSON.

Answer

The log files need to be copied to a different bucket in the same region.

Question 9

A company is using AWS Glue to run ETL jobs that process data from Amazon S3 and load it into Amazon Redshift. The data engineer notices that the Glue job is failing with the error 'S3ServiceException: Access Denied' when writing to the staging S3 bucket. Which THREE actions should the engineer take to resolve this issue?

Accepted Answer

Verify that the IAM role used by the Glue job has the s3:PutObject permission for the staging bucket.. Options A, C, and E are correct. The IAM role must have s3:PutObject, and also the S3 bucket policy must allow the role's access (option A). Option C is needed for writing temporary files. Option E is needed for the Glue service principal to write to the bucket. Option B is wrong because the error is about write access, not read. Option D is wrong because the error is about the staging bucket, not the Glue job script.

Answer

Ensure that the Glue job script is correctly referencing the S3 bucket path.

Answer

Verify that the IAM role has s3:GetObject permission for the source bucket.

Question 10

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is then processed by a scheduled AWS Glue ETL job that loads it into an Amazon Redshift table. Recently, the Glue job has been failing with the error: 'S3ServiceException: Access Denied'. The Firehose delivery stream is configured with a prefix and error logging to the same S3 bucket. The Glue job uses the same IAM role that has s3:GetObject and s3:ListBucket permissions on the bucket. What is the most likely cause?

Accepted Answer

The S3 bucket has default encryption enabled with AWS KMS (SSE-KMS), and the Glue job's IAM role lacks kms:Decrypt permission.. Option D is correct because Firehose uses SSE-S3 by default unless configured otherwise. If the S3 bucket has default encryption enabled with SSE-KMS, Firehose will use that encryption, but the Glue job's IAM role may lack kms:Decrypt permission for the KMS key. The error 'Access Denied' when reading from S3 often indicates encryption permission issues. Option A is wrong because the Glue job can read from S3 with the current permissions if no encryption is involved. Option B is wrong because the error is about access, not schema. Option C is wrong because the Glue job can use the same role as Firehose, but the role may not have KMS permissions.

Answer

The Glue job expects a different data format than what Firehose writes.

Answer

The Glue job's IAM role does not have s3:GetObjectVersion permission.

Answer

The Glue job is using the wrong IAM role that does not have permissions to the S3 bucket.

Question 11

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.. Options B, C, and D are correct. The Lambda function must be created (B), Firehose must be configured to invoke the Lambda function (C), and the IAM role must allow Firehose to invoke Lambda (D). Option A is wrong because Lambda@Edge is for CloudFront. Option E is wrong because S3 events are not used for Firehose transformation.

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 12

A company runs a data pipeline that uses Amazon EMR to process large datasets. The pipeline reads data from S3, processes it using Spark, and writes results back to S3. Recently, the pipeline has been failing with 'OutOfMemoryError' in the Spark executors. The EMR cluster is configured with 5 core nodes of type m5.xlarge (4 vCPU, 16 GB memory each). The Spark application uses dynamic allocation and default Spark configurations. The input data size is approximately 500 GB in Parquet format. What is the most cost-effective way to resolve the out-of-memory errors?

Accepted Answer

Change the core node instance type to r5.xlarge (32 GB memory) and keep 5 nodes.. Option B is correct because the current cluster has limited memory per node (16 GB). By switching to memory-optimized instances like r5.xlarge (32 GB), each node has double the memory, reducing the chance of OOM. This is more cost-effective than adding more nodes because the total memory per node increases without increasing the number of instances. Option A is wrong because increasing the number of nodes adds more memory but also more cost; it might be more expensive than using fewer, larger nodes. Option C is wrong because it's generally not recommended to increase spark.executor.memory beyond the physical memory; it could cause YARN to kill containers. Option D is wrong because Parquet is already efficient; changing to a different format may not solve memory issues.

Answer

Increase the spark.executor.memory setting to 8 GB in the Spark configuration.

Answer

Increase the number of core nodes to 10 to distribute the data across more executors.

Answer

Change the input data format from Parquet to ORC to reduce memory footprint.

Question 13

A data engineer attempts to suspend versioning on an S3 bucket but receives the error shown. The engineer needs to suspend versioning to reduce storage costs. What should the engineer do FIRST?

Accepted Answer

Disable MFA Delete by using the AWS CLI with the --mfa parameter and then suspend versioning.. Option B is correct because MFA Delete must be disabled before versioning can be suspended. Option A is incorrect because MFA Delete is already enabled, not disabled. Option C is incorrect because versioning cannot be suspended directly. Option D is incorrect because the error is about MFA Delete, not permissions.

Answer

Use the AWS Management Console to suspend versioning, as it bypasses MFA Delete.

Answer

Delete the bucket and recreate it without versioning.

Answer

Add a bucket policy to allow versioning suspension.

Question 14

A data engineer is troubleshooting an access issue. A user has the IAM policy shown in the exhibit. The user attempts to upload an object to `s3://data-lake-bucket/confidential/report.pdf`. What will happen?

Accepted Answer

The upload will fail with an 'Access Denied' error.. Option B is correct because the explicit Deny overrides the Allow, so the upload will be denied. Option A is incorrect because the user has s3:PutObject allowed for the bucket, but the Deny for the confidential path takes precedence. Option C is incorrect because the policy is valid. Option D is incorrect because the Deny is explicit.

Answer

The upload will succeed because the Deny statement is not valid without a condition.

Answer

The upload will succeed because the Allow statement is more specific than the Deny.

Answer

The upload will succeed because the user has s3:PutObject permission on the bucket.

Question 15

A company runs a data pipeline on Amazon EMR that processes terabytes of data daily. The pipeline reads from Amazon S3, performs transformations using Spark, and writes results back to S3. Recently, the data engineer noticed that the EMR cluster's spot instances are frequently reclaimed, causing job failures and delays. The cluster uses a mix of On-Demand and Spot instances. The engineer wants to minimize job interruptions while keeping costs low. The current configuration uses a single EMR cluster with a core node group of 10 On-Demand instances and a task node group of 20 Spot instances. The job failures occur during the shuffle phase when tasks on Spot instances are lost. The engineer has no control over when spot instances are reclaimed. Which action will MOST effectively reduce job failures while maintaining cost efficiency?

Accepted Answer

Enable EMR managed scaling to automatically add On-Demand instances when Spot instances are reclaimed.. Option B is correct because enabling managed scaling allows EMR to automatically add On-Demand instances to replace reclaimed Spot instances, ensuring capacity without manual intervention. Option A is incorrect because using all Spot instances increases risk. Option C is incorrect because using all On-Demand increases cost significantly. Option D is incorrect because increasing On-Demand count in core node group may help but does not dynamically adjust; managed scaling is more effective.

Answer

Increase the number of On-Demand instances in the core node group to 20.

Answer

Configure the task node group to use only Spot instances and increase the bid price to the On-Demand price.

Answer

Change the task node group to use only On-Demand instances.