DEA-C01 Data Operations and Support Practice Test 16 — 15 Questions

Question 1

A data engineer is running an Amazon Athena query that scans a large amount of data in Amazon S3, resulting in high costs. The data is stored in Parquet format in a partitioned table. Which strategy would be MOST effective in reducing the amount of data scanned?

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

Accepted Answer

Ensure the query includes a WHERE clause that filters on partition columns.. Partition pruning allows Athena to read only the partitions that match the WHERE clause, significantly reducing the amount of data scanned. Option A is correct because filtering on partition columns is the most effective way to minimize scanned data. Option B is incorrect because Parquet is a columnar format that already compresses well and reduces scan compared to CSV with GZIP. Option C is incorrect because S3 Intelligent-Tiering optimizes storage costs, not query scan costs. Option D is incorrect because adding more partition columns does not reduce scan unless the query filters on them, and may increase metadata overhead.

Answer

Convert the Parquet files to CSV format and apply GZIP compression.

Answer

Use S3 Intelligent-Tiering storage class to reduce storage costs.

Answer

Increase the number of partitions by adding more partition columns.

Question 2

A company uses Amazon DynamoDB as the primary data store for a real-time application. The data engineer observes that some read requests are returning stale data, even though the application uses strongly consistent reads. The table has auto-scaling enabled with a maximum read capacity of 10,000 RCUs. The observed read traffic averages 8,000 RCUs but occasionally spikes to 12,000 RCUs. What is the most likely cause of the stale reads?

Accepted Answer

Read capacity auto-scaling cannot keep up with sudden traffic spikes, causing throttling and fallback to eventually consistent reads.. When read traffic spikes above the maximum auto-scaling limit of 10,000 RCUs (e.g., to 12,000 RCUs), the table cannot provision enough read capacity units quickly enough. This leads to throttling of requests. The AWS SDKs are designed to retry throttled requests, and under certain conditions, they may fall back to eventually consistent reads to reduce latency, which can return stale data. This explains why stale data appears even when the application explicitly requests strongly consistent reads. Option B is incorrect: write sharding does not inherently cause read-after-write inconsistencies; DynamoDB's strong consistency guarantees hold if capacity is sufficient. Option C is incorrect: DAX is a caching layer that provides eventual consistency by default; strongly consistent reads bypass DAX and go directly to the table, so DAX is not involved. Option D is incorrect: global tables replicate data asynchronously, but the statement says the application uses a single table; moreover, reading from a replica region would use eventually consistent reads only if configured, but the question states strongly consistent reads are used.

Answer

The application uses write sharding, causing read-after-write inconsistencies.

Answer

The application is using DynamoDB Accelerator (DAX) which caches data and may return stale values.

Answer

The table is part of a DynamoDB global table, and the application reads from a replica in a different region.

Question 3

A data engineer sees the CloudWatch log entry in the exhibit for a Lambda function that processes data from an Amazon SQS queue. What is the MOST likely cause of the timeout?

Accepted Answer

The Lambda function's timeout is too short for the processing required.. The CloudWatch log shows the Lambda function timed out after 30 seconds (duration 30001.23 ms), which is the default timeout. Increasing the timeout allows the function to complete its processing. Option A is incorrect because reserved concurrency affects throughput, not a single function's timeout. Option B is incorrect because memory usage is low (64 MB out of 128 MB), so memory is not the issue. Option D is incorrect because the SQS visibility timeout controls how long a message is hidden after being picked up, but the Lambda timeout is independent.

Answer

The Lambda function's reserved concurrency is set too low.

Answer

The Lambda function is running out of memory.

Answer

The SQS queue's visibility timeout is set too low.

Question 4

A data engineer needs to implement a data pipeline that ingests data from an on-premises database using AWS DMS and loads it into Amazon S3 in Parquet format. The data should be encrypted at rest in S3 using a customer-managed KMS key. Which combination of actions should the engineer take? (Choose the correct course of action.)

Accepted Answer

Configure the DMS task to write to S3 in Parquet format, and specify the KMS key ID in the S3 endpoint settings.. AWS DMS can directly write data to Amazon S3 in Parquet format when configuring the DMS task. To encrypt data at rest with a customer-managed KMS key, you specify the KMS key ID in the S3 endpoint settings. This allows DMS to encrypt objects with the specified KMS key as they are written to S3. Option B is incorrect because it introduces an unnecessary EC2 instance; DMS can write Parquet directly without an intermediate conversion step. Option C is incorrect because DMS can write Parquet directly; converting JSON to Parquet with Glue adds complexity and is not required. Option D is incorrect because a bucket policy requiring KMS encryption does not automatically encrypt data written by DMS; the encryption must be configured in the DMS task or endpoint settings.

Answer

Set up an EC2 instance to run a script that reads from the source and writes Parquet to S3 with KMS encryption.

Answer

Use DMS to write JSON to S3, then use an AWS Glue job to convert to Parquet and enable KMS encryption on the Glue job.

Answer

Configure the S3 bucket policy to require KMS encryption for all objects, and use DMS with default settings.

Question 5

A data engineer creates an Amazon DynamoDB table using the CloudFormation snippet in the exhibit. The application writes 200 items per second to the table. The engineer notices that many write requests are being throttled. What is the MOST likely reason?

Accepted Answer

The provisioned write capacity is too low for the application's write rate.. The table is provisioned with only 5 write capacity units, which allows 5 writes per second (each write up to 1 KB). With 200 writes per second, the table is severely under-provisioned. Option A is incorrect because the key schema is fine for a primary key. Option B is incorrect because the attribute type is correct. Option C is incorrect because the table name is valid.

Answer

The table does not have a sort key, causing hot partitions.

Answer

The attribute type for OrderID should be numeric for better performance.

Answer

The table name 'Orders' conflicts with an existing table.

Question 6

A data engineer is troubleshooting an Amazon Redshift cluster that has experienced a node failure. The engineer needs to ensure that the cluster is highly available and can withstand a single node failure with minimal downtime. Which TWO actions should the engineer take?

Accepted Answer

Enable automated snapshots with cross-region copy.. Options A and E are considered correct for ensuring high availability in Amazon Redshift. Option A: Automated snapshots with cross-region copy enable recovery from a node failure by restoring data from a snapshot in another region, which reduces downtime but does not guarantee zero downtime. Option E: A multi-node cluster with RA3 node types separates compute and storage, allowing faster node replacement and minimizing downtime, though it does not eliminate it entirely. The exam expects these as the best actions among the given choices to improve availability and withstand a single node failure.

Answer

Enable concurrency scaling to handle increased read traffic.

Answer

Deploy the cluster as a single-node cluster for simplicity.

Answer

Place the cluster in a public subnet with an internet gateway.

Question 7

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.

Question 8

A data pipeline using AWS Glue jobs is failing with 'Insufficient capacity' errors for Spark executors. Which action should the data engineer take to resolve this?

Accepted Answer

Increase the number of workers (DPUs) in the Glue job configuration.. The 'Insufficient capacity' error for Spark executors indicates that the Glue job is running out of resources (DPUs). Increasing the number of workers (DPUs) provides more compute capacity, allowing the job to allocate sufficient executors. Option A (reducing workers) would worsen the issue. Option B (increasing timeout) does not add resources. Option C (disabling Spark UI) does not affect capacity. Therefore, increasing the number of workers (DPUs) is the correct resolution.

Answer

Reduce the number of workers in the Glue job configuration.

Answer

Increase the job timeout value.

Answer

Disable Spark UI logging.

Question 9

A company is using Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) for streaming data processing. The application is experiencing high latency and the data engineer wants to improve performance. Which THREE actions should the engineer consider? (Choose three.)

Accepted Answer

Use a larger Kinesis data stream with more shards.. Options A, C, and D are correct. Increasing the number of shards in the Kinesis stream (A) increases the ingestion capacity, which can reduce bottlenecks and lower latency. Increasing Flink parallelism (C and D) allows more parallel processing of data, improving throughput and reducing latency. Option B is incorrect because decreasing the buffer time in Flink can lead to increased network overhead and may not improve overall performance; it can actually increase latency due to more frequent data transfers. Option E is incorrect because decreasing the checkpoint interval increases the frequency of state snapshots, adding overhead and potentially increasing latency rather than reducing it.

Answer

Decrease the buffer time in the Flink application to reduce latency.

Answer

Decrease the checkpoint interval to reduce state size.

Question 10

A company uses AWS Lake Formation to manage data lake permissions. A data engineer needs to grant a group of analysts SELECT permission on a set of tables in the 'analytics' database, but only for columns that are not classified as 'PII'. Which approach should the engineer use?

Accepted Answer

Use Lake Formation tag-based access control (LF-TBAC) to grant SELECT on columns without the 'PII' tag.. Lake Formation tag-based access control (LF-TBAC) allows granting SELECT permission on columns that do not have the 'PII' tag, enabling fine-grained column-level access. Option A is incorrect because granting SELECT on the entire database would include all columns, including those classified as PII. Option B is incorrect because IAM policies cannot enforce column-level restrictions based on tags within Lake Formation tables. Option C is incorrect because S3 bucket policies operate at the object level and cannot restrict access to specific columns within a table.

Answer

Grant SELECT on the entire database and rely on analysts to avoid PII columns.

Answer

Create an IAM policy that denies access to PII columns.

Answer

Use an S3 bucket policy to restrict access to objects containing PII data.

Question 11

A data engineer is setting up an AWS Glue job to process data from an Amazon S3 bucket. The job fails with an 'Access Denied' error. Which TWO IAM permissions are MOST likely missing from the Glue job's IAM role?

Accepted Answer

s3:PutObject. Options A and E are correct. A Glue job requires s3:GetObject to read input data from S3 and s3:PutObject to write output data to S3. Option B (kms:Decrypt) is only needed if the S3 objects are encrypted with KMS. Option C (dynamodb:GetItem) is not relevant unless the job accesses DynamoDB. Option D (glue:StartJobRun) is not needed for the job's execution itself; it is used to start a job run.

Answer

kms:Decrypt

Answer

dynamodb:GetItem

Answer

glue:StartJobRun

Question 12

A data engineer is troubleshooting a slow Amazon Redshift query. The EXPLAIN plan shows a 'Seq Scan' on a large table. What is the most likely cause?

Accepted Answer

The table does not have a proper sort key defined.. A 'Seq Scan' in the EXPLAIN plan indicates a full table scan, which typically occurs when the table does not have a proper sort key defined. Without a sort key, Redshift cannot use zone maps to skip blocks, resulting in a sequential scan. Option A (too many nodes) would not cause a Seq Scan; it might improve performance if properly distributed. Option B (concurrent queries) can cause slowdown but not specifically a Seq Scan. Option D (WLM queue misconfiguration) affects query queuing and concurrency, not the scan type.

Answer

The cluster has too many nodes.

Answer

There are too many concurrent queries.

Answer

The workload management (WLM) queue is misconfigured.

Question 13

Refer to the exhibit. A data engineer has an IAM policy attached to an IAM role used by an AWS Glue job. The Glue job reads from S3 bucket 'example-bucket' and writes to an S3 bucket 'output-bucket'. The job fails with an 'Access Denied' error when writing to 'output-bucket'. What is the MOST likely cause?

Accepted Answer

The policy does not allow s3:PutObject on 'output-bucket'.. The policy only allows s3:PutObject on 'example-bucket/*', not on 'output-bucket/*'. The job needs permission on the output bucket. Option A is incorrect because s3:PutObject is allowed on example-bucket, but not on output-bucket. Option C is incorrect because there is no condition that restricts PutObject to example-bucket. Option D is incorrect because the policy allows s3:GetObject on example-bucket, which is for reading.

Answer

The policy does not allow s3:PutObject on any bucket.

Answer

The policy does not allow s3:GetObject on 'output-bucket'.

Answer

The policy has a condition that restricts s3:PutObject to 'example-bucket'.

Question 14

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Aurora MySQL. The migration is successful, but the ongoing replication task is experiencing high latency. Which configuration change is most likely to reduce latency?

Accepted Answer

Increase the size of the DMS replication instance.. Increasing the size of the DMS replication instance provides more CPU and memory, which can process and apply changes faster, thereby reducing latency in ongoing replication. Option B is incorrect because decreasing the batch size and batch apply timeout would likely increase latency by reducing the efficiency of batch writes. Option C is incorrect because changing the target endpoint to Amazon S3 is not relevant; the target is Aurora MySQL, and migrating to S3 would not resolve latency issues for the current setup. Option D is incorrect because enabling CDC from binary logs is not applicable; the source is Oracle, which uses redo logs, not binary logs. Additionally, CDC is already used in ongoing replication from Oracle.

Answer

Decrease the task's batch size and batch apply timeout.

Answer

Change the target endpoint to Amazon S3.

Answer

Enable Change Data Capture (CDC) from binary logs.

Question 15

Refer to the exhibit. A data engineer is reviewing the configuration of an Amazon Redshift cluster. The engineer wants to ensure that the cluster can be restored to a point in time up to 35 days in the past. Based on the exhibit, what change is needed?

Accepted Answer

Increase the automated snapshot retention period to 35 days.. The automated snapshot retention period is currently set to 1 day, which only allows point-in-time recovery within the last day. To restore to a point in time up to 35 days in the past, this retention period must be increased to 35 days. Option B (changing the subnet group) does not affect backup retention. Option C (enabling encryption) is unrelated to snapshot retention; the cluster may already be encrypted or not, but encryption does not extend the retention period. Option D (increasing the number of nodes) also does not affect snapshot retention.

Answer

Change the cluster subnet group to a custom one.

Answer

Enable encryption on the cluster.

Answer

Increase the number of nodes to 6.