DEA-C01 Data Operations and Support Practice Test 3 — 15 Questions

Question 1

A data engineer is monitoring an Amazon EMR cluster running a Spark job. The job is processing a large dataset and the engineer notices that the cluster is using a high percentage of disk space on the core nodes. The job fails with 'No space left on device' error. What is the most effective way to resolve this issue without modifying the job logic?

Accepted Answer

Increase the number of core nodes in the cluster.. Option D is correct because increasing the number of core nodes distributes the intermediate shuffle data and temporary files across more nodes, reducing the per-node disk usage. This directly addresses the 'No space left on device' error without altering the Spark job logic, as core nodes in EMR store both HDFS data and local shuffle spills.

Answer

Attach additional EBS volumes to the core nodes.

Answer

Increase the EBS volume size attached to the core nodes.

Answer

Change the core node instance type to one with more memory.

Question 2

A company uses Amazon CloudWatch Logs to collect application logs from EC2 instances. The logs are exported to Amazon S3 for long-term storage. Recently, the export task failed with the error 'Access Denied'. What is the most likely cause of this failure?

Accepted Answer

The IAM role does not have s3:PutObject permission on the destination bucket.. The export task from CloudWatch Logs to S3 uses an IAM role to write data to the destination bucket. If the role lacks the s3:PutObject permission, the S3 service will reject the request with an 'Access Denied' error. This is the most common cause because the export operation requires write access to the bucket.

Answer

The S3 bucket policy denies access from the CloudWatch Logs service.

Answer

The IAM role does not have s3:ListBucket permission.

Answer

The EC2 instances are in a VPC without a VPC endpoint for CloudWatch Logs.

Question 3

A data engineer is troubleshooting an AWS Glue crawler that is not correctly inferring the schema of CSV files stored in Amazon S3. The files have headers, but the crawler is treating the header row as data. The crawler is configured with a custom classifier that has a CSV classifier with 'Column header' set to 'Use first row as header'. What is the most likely reason the crawler is not recognizing the header?

Accepted Answer

The CSV classifier's 'Quote symbol' setting does not match the files.. The CSV classifier may not be applied if the file does not match the classifier's 'Quote symbol' or 'Allow single column' settings. Option D is correct because if the classifier expects a quote symbol but the file has none, it may not match. Option A is wrong because the classifier is set to use first row as header. Option B is wrong because the crawler does not require the header to be uppercase. Option C is wrong because the number of columns is not a header recognition issue.

Answer

The CSV files have a varying number of columns across rows.

Answer

The CSV files have a different delimiter than the default comma.

Answer

The header row contains uppercase letters.

Question 4

A company is using Amazon DynamoDB as a data store for a real-time application. The application reads a single item by primary key and occasionally updates it. The data engineer notices high read latency during peak hours. Which TWO actions would most effectively reduce read latency?

Accepted Answer

Increase the read capacity units for the table.. Option B: Increasing read capacity units reduces throttling, which reduces latency. Option D: Enabling DynamoDB Accelerator (DAX) adds an in-memory cache, reducing read latency. Option A is wrong because disabling auto-scaling could make things worse. Option C is wrong because global tables are for multi-region, not latency. Option E is wrong because sparse indexes don't help single-item reads.

Answer

Enable DynamoDB global tables.

Answer

Add a local secondary index on the table.

Answer

Disable auto-scaling and set a fixed read capacity.

Question 5

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and must be queryable using Amazon Athena. The engineer needs to optimize query performance and reduce costs. Which THREE actions would achieve this?

Accepted Answer

Partition the data by a commonly used filter column.. Option A: Partitioning reduces data scanned. Option C: Using columnar formats like Parquet reduces data scanned. Option E: Compression reduces storage and data scanned. Option B is wrong because the number of files should be optimized, not increased. Option D is wrong because S3 Select is for filtering within a single file, not for Athena.

Answer

Store data in many small files to increase parallelism.

Answer

Use S3 Select instead of Athena for queries.

Question 6

A company is using AWS Glue ETL jobs to process data from Amazon S3 and write results back to S3. The jobs are failing intermittently with 'ThrottlingException' errors. Which TWO configurations would help reduce these errors?

Accepted Answer

Add retry logic with exponential backoff in the job script.. Option B: Increasing the number of DPUs may distribute the load and reduce throttling. Option D: Implementing retry logic in the job handles transient throttling. Option A is wrong because it increases the chance of throttling. Option C is wrong because compression doesn't reduce API calls. Option E is wrong because the job type is not related.

Answer

Decrease the number of DPUs for the job.

Answer

Enable GZIP compression on the output data.

Answer

Change the job type from Spark to Python shell.

Question 7

A data engineer is troubleshooting a nightly ETL job that reads data from an RDS MySQL instance and writes to an S3 bucket in Parquet format. The job runs on an EMR cluster and uses PySpark. Recently, the job started failing with 'OutOfMemoryError' in the executor logs. The data volume has grown 30% in the last month. Which is the MOST efficient solution to resolve this issue without changing the code?

Accepted Answer

Increase the executor memory and memoryOverhead in the Spark configuration.. Option A is correct because increasing executor memory and adjusting the spark.executor.memoryOverhead setting addresses memory limitations for large data processing. Option B is wrong because switching to Glue may not directly resolve memory issues and requires code changes. Option C is wrong because increasing cluster size adds cost and may not fix memory per executor. Option D is wrong because using a larger instance type is less flexible than tuning Spark configurations.

Answer

Change the RDS instance to a larger size to reduce load.

Answer

Switch the ETL job to use AWS Glue with a larger WorkerType.

Answer

Increase the number of core nodes in the EMR cluster.

Question 8

A data engineer receives an alert that a Kinesis Data Stream has a 'WriteProvisionedThroughputExceeded' error. The stream has 5 shards with 1 MB/s write capacity per shard. The producer application is sending data at 8 MB/s sustained. What should the engineer do to resolve the issue?

Accepted Answer

Increase the number of shards from 5 to 10.. The 'WriteProvisionedThroughputExceeded' error indicates that the total write throughput to the Kinesis Data Stream exceeds the provisioned capacity. With 5 shards, each offering 1 MB/s write capacity, the total write capacity is 5 MB/s. The producer is sending 8 MB/s, which is above this limit. Increasing the number of shards to 10 raises the total write capacity to 10 MB/s, accommodating the sustained 8 MB/s throughput and resolving the throttling.

Answer

Reduce the record size to below 1 MB per record.

Answer

Enable enhanced fan-out on the stream.

Answer

Use Kinesis Firehose as an intermediary to buffer data.

Question 9

A company ingests IoT sensor data into an S3 bucket. Daily, a Lambda function reads new objects, processes them, and writes results to a DynamoDB table. Recently, the Lambda function started timing out after 15 minutes. The data volume has increased, and the function processes records one by one. Which solution would improve performance without significant cost increase?

Accepted Answer

Use S3 Batch Operations to invoke the Lambda function in parallel for each object.. Option B is correct because using S3 Batch Operations invokes a Lambda function for each object in parallel, handling large volumes efficiently. Option A is wrong because increasing Lambda timeout does not address the root cause of sequential processing. Option C is wrong because Glue jobs have a startup overhead and may cost more. Option D is wrong because increasing DynamoDB write capacity does not speed up the Lambda processing.

Answer

Replace Lambda with an AWS Glue ETL job.

Answer

Increase the Lambda function timeout to 30 minutes.

Answer

Increase the DynamoDB write capacity units.

Question 10

A data engineer is monitoring a Redshift cluster that is experiencing slow query performance. The cluster has 4 dc2.large nodes. The engineer notices that disk space usage is at 85% across all nodes. Which action would MOST likely improve query performance?

Accepted Answer

Increase the number of nodes to 8.. Option D is correct because adding more nodes distributes data and improves I/O parallelism. Option A is wrong because vacuum reclaims space but does not help if disk usage is high due to data volume. Option B is wrong because DISTKEY and SORTKEY changes are design-time decisions. Option C is wrong because compression is already applied at load time.

Answer

Change the table design to use DISTKEY and SORTKEY.

Answer

Enable compression on all columns.

Answer

Run the VACUUM command to reclaim space.

Question 11

A data engineer is tasked with setting up a data pipeline that moves data from an on-premises Oracle database to Amazon S3 every hour. The network bandwidth is limited, and the engineer needs to ensure data consistency. Which AWS service should the engineer use?

Accepted Answer

AWS Database Migration Service (DMS) with change data capture (CDC).. AWS DMS with CDC is the correct choice because it can continuously replicate ongoing changes from an on-premises Oracle database to Amazon S3 while ensuring data consistency. CDC captures only the incremental changes (inserts, updates, deletes) after an initial full load, minimizing the data transferred over limited bandwidth and maintaining transactional integrity.

Answer

AWS DataSync.

Answer

Amazon Kinesis Data Firehose.

Answer

S3 Transfer Acceleration.

Question 12

A company uses a DynamoDB table with on-demand capacity for a gaming application. During a new game launch, the table experienced throttling errors. The engineer checks CloudWatch metrics and sees that the 'ConsumedWriteCapacityUnits' exceeded the 'ProvisionedWriteCapacityUnits' (on-demand uses the table's previous peak). The application is writing at 50,000 WCU but the table's peak was 30,000 WCU. What should the engineer do to resolve throttling?

Accepted Answer

Contact AWS Support to pre-warm the table for higher throughput.. Option B is correct because on-demand capacity automatically scales based on traffic, but it has a warm-up limit. Pre-warming the table increases the initial throughput limit. Option A is wrong because on-demand does not have a provisioned setting. Option C is wrong because increasing partition count does not directly increase throughput limit. Option D is wrong because DAX improves read performance, not write throughput.

Answer

Add a DynamoDB Accelerator (DAX) cluster in front of the table.

Answer

Increase the number of partitions by splitting the partition key.

Answer

Switch the table to provisioned capacity and set WCU to 50,000.

Question 13

A data engineer is monitoring an AWS Glue ETL job that processes data from an S3 bucket and writes to a Redshift table. The job completes successfully but takes longer than expected. The engineer notices that the job uses 10 DPUs and the data size is 500 GB. The job runs in standard mode. Which change would MOST reduce job duration?

Accepted Answer

Increase the number of DPUs to 20.. Option D is correct because increasing DPUs allows more parallelism if the job is distributed. Option A is wrong because reducing the number of partitions may increase data skew. Option B is wrong because using a smaller instance type reduces resources. Option C is wrong because converting to CSV increases file size and processing time.

Answer

Use a smaller worker type like G.1X.

Answer

Change the output format from Parquet to CSV.

Answer

Reduce the number of partitions in the data.

Question 14

A data engineer needs to ensure that a Redshift cluster can recover from a failure with minimal data loss. The cluster is used for reporting and can tolerate a few minutes of downtime. Which feature should the engineer enable?

Accepted Answer

Enable automated snapshots with a retention period of 1 day.. Automated snapshots in Amazon Redshift are taken at regular intervals (default every 8 hours or 5 GB of data changes) and retained for a specified period. Enabling automated snapshots with a retention period of 1 day ensures that, in the event of a failure, the cluster can be restored to the most recent snapshot, minimizing data loss to at most the snapshot interval. This aligns with the requirement for minimal data loss and tolerance for a few minutes of downtime, as restoring from a snapshot takes time but preserves recent data.

Answer

Configure cross-region snapshot copy.

Answer

Take manual snapshots every hour.

Answer

Enable Multi-AZ deployment.

Question 15

A data engineer is troubleshooting a Kinesis Data Analytics application that processes streaming data. The application is falling behind and has a high 'MillisBehindLatest' metric. The application uses a parallelism of 2. The source stream has 4 shards. What is the MOST likely cause and solution?

Accepted Answer

The application parallelism is too low; increase it to 4.. The 'MillisBehindLatest' metric indicates the application is not keeping up with the incoming data. With a source stream of 4 shards and a parallelism of only 2, the application cannot process data from all shards concurrently, leading to backpressure. Increasing parallelism to match the shard count (4) allows each shard to be processed by a separate task, reducing lag.

Answer

The application is using a JSON format; switch to Avro.

Answer

The source stream has too many shards; decrease to 2.

Answer

The output destination is slow; change to a faster sink.