DEA-C01 Practice Test 44 — 15 Questions

Question 1

A data engineer maintains an AWS Glue ETL job that reads from an Amazon Kinesis Data Streams stream and writes to Amazon S3 in Parquet format. The job has been running successfully, but after the data volume increased threefold, the job now fails with an error stating that the Glue job's bookmarks are not advancing and the job is reprocessing old data. The engineer has enabled job bookmarks with the default settings. Which action should the engineer take to resolve the issue?

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

Accepted Answer

Verify that the Kinesis Data Streams source is configured with the correct stream name and that the job's transformation does not include an unsupported operation that blocks bookmark propagation, such as a custom transformation that does not preserve the bookmark state.. Job bookmarks in AWS Glue track the last processed data to avoid reprocessing. When bookmarks fail to advance, it is typically due to an unsupported transformation or a misconfigured source. Ensuring the Kinesis source is correctly configured and that all transformations preserve bookmark state will allow the job to process only new data and advance the bookmark.

Answer

Disable job bookmarks and instead use an AWS Lambda function to track the last processed Kinesis sequence number in Amazon DynamoDB.

Answer

Increase the number of AWS Glue DPUs allocated to the job and enable auto-scaling.

Answer

Change the output format from Parquet to JSON, as Parquet does not support job bookmarks with Kinesis sources.

Question 2

A data engineer is managing an Amazon Redshift cluster that experiences performance degradation during peak query hours. The engineer notices that many queries are waiting in the queue, and the WLM query queue wait time is high. The cluster uses automatic WLM. Which action should the engineer take to improve query throughput?

Accepted Answer

Enable concurrency scaling to automatically add transient clusters for bursts of queries.. Concurrency scaling automatically adds transient clusters to handle query bursts, reducing queue wait times. It is specifically designed to improve throughput during peak periods. Manually increasing concurrency is not possible with automatic WLM. Adding nodes or changing distribution style does not directly address queue wait times caused by concurrency limits.

Answer

Change the cluster's distribution style to EVEN to balance data across nodes.

Answer

Increase the number of nodes in the cluster to add more compute resources.

Answer

Modify the WLM configuration to increase the concurrency level for the queue.

Question 3

A company uses Amazon QuickSight for data visualization. The data engineer needs to ensure that users can only see data relevant to their department. The data is stored in Amazon S3 and is accessed via SPICE. The engineer has created datasets in QuickSight and wants to implement row-level security (RLS). The dataset contains a column 'Department' that indicates which department a row belongs to. The engineer has configured RLS rules using a separate permissions dataset. However, users report that they can see all rows, not just their department's rows. What is the most likely reason?

Accepted Answer

The RLS permissions dataset is not correctly configured to map users to department values.. RLS in QuickSight works by matching the user's identity (via username or group) against rules in a permissions dataset that maps users/groups to allowed values of a column (e.g., Department). If users see all rows, the most likely cause is that the permissions dataset is not correctly mapping users to department values — for example, wrong usernames, missing group mappings, or a dataset that does not join properly to the main dataset.

Answer

The 'Department' column is not included in the dataset.

Answer

The users have been granted admin access to the QuickSight dashboard.

Answer

The SPICE dataset does not support row-level security.

Question 4

A data engineer is using Amazon Kinesis Data Streams to ingest real-time data. The engineer needs to ensure that records are delivered to consumers in the same order they were written and that each record is processed exactly once. Which combination of Kinesis Data Streams features should the engineer use?

Accepted Answer

Use a single shard and implement idempotent processing in the consumer application.. To guarantee order, all records must go to a single shard, as Kinesis preserves order only within a shard. For exactly-once processing, the consumer must be idempotent because Kinesis Data Streams provides at-least-once delivery. Combining a single shard with idempotent processing ensures both requirements are met.

Answer

Use partition keys to distribute records across shards and enable AWS Lambda as a consumer with checkpointing.

Answer

Use multiple shards with sequence numbers and enable Kinesis Client Library (KCL) with deduplication.

Answer

Use a single shard and enable enhanced fan-out for consumers.

Question 5

A company is using AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job fails intermittently with a 'MemoryError' when the stream has a sudden spike in data volume. Which configuration change would best prevent this error?

Accepted Answer

Increase the number of DPUs (Data Processing Units) for the Glue job.. Increasing the number of DPUs for the AWS Glue job provides more compute and memory resources to the job, which directly addresses the MemoryError caused by sudden data volume spikes. Glue DPUs are the unit of processing capacity, and scaling them up allows the job to handle larger volumes without running out of memory. This is the most direct configuration change to prevent memory-related failures.

Answer

Store intermediate results in Amazon RDS.

Answer

Use a batch transformation instead of streaming.

Answer

Increase the number of shards in the Kinesis data stream.

Question 6

A data engineer is using AWS Database Migration Service (AWS DMS) to migrate an on-premises Oracle database to Amazon Aurora PostgreSQL. The migration is running but the engineer observes that some tables are not being replicated. The DMS task logs show no errors, and the task status is 'Running'. Which action should the engineer take to identify the missing tables?

Accepted Answer

Check the DMS task's table mappings to ensure all required tables are included in the selection rules.. Table mappings in AWS DMS determine which tables are migrated. If tables are missing and no errors are present, the most likely cause is that the tables are not included in the selection rules. Reviewing and correcting the table mappings is the direct way to ensure all required tables are replicated.

Answer

Enable CloudWatch Logs for the DMS task and look for warnings about table selection.

Answer

Increase the DMS replication instance size to handle more tables concurrently.

Answer

Restart the DMS task with the 'Reload target' option to force a full load of all tables.

Question 7

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The application reads from a Kinesis data stream and writes results to an S3 bucket. The application is consistently running out of memory and failing. The operator has already increased the Parallelism and TaskManager memory. What is the next BEST step to troubleshoot?

Accepted Answer

Enable Apache Flink metrics in Amazon CloudWatch to monitor heap and checkpoint details. When a Flink application on Kinesis Data Analytics runs out of memory despite increased parallelism and TaskManager memory, the next best step is to enable Apache Flink metrics in CloudWatch to observe heap usage, garbage collection, and checkpoint behavior. These metrics reveal whether the issue is memory leaks, backpressure, or state growth. Without observability, further tuning is guesswork.

Answer

Change the processing mode from exactly-once to at-least-once

Answer

Reduce the number of shards in the source stream

Answer

Increase the buffer timeout for the S3 sink

Question 8

A data engineer manages an AWS Glue ETL job that reads from an Amazon S3 bucket and writes to an Amazon Redshift table. The job runs daily and recently started failing with the error 'Unable to find a suitable security group for the connection'. The Glue connection is configured with a VPC, subnet, and security group. The engineer verifies that the IAM role has the necessary permissions and the S3 bucket is accessible. What is the most likely cause of this error?

Accepted Answer

The security group specified in the Glue connection does not exist or is not in the same VPC as the subnet.. The error 'Unable to find a suitable security group for the connection' occurs when the security group specified in the Glue connection is invalid for the subnet. This typically happens if the security group does not exist, is in a different VPC, or is not associated with the subnet's VPC. Ensuring the security group and subnet are in the same VPC and that the security group exists resolves the issue.

Answer

The security group attached to the Glue connection does not allow outbound traffic to the Redshift cluster on the required port.

Answer

The IAM role used by the Glue job lacks permissions to describe security groups in the VPC.

Answer

The Glue connection is associated with a subnet that does not have a route to the Redshift cluster.

Question 9

A data engineer maintains an AWS Glue Data Catalog table for an S3-based dataset. After new files were added, queries in Amazon Athena fail with the error 'HIVE_BAD_DATA: Error parsing field value for field 3: For input string: "N/A"'. The column is defined as bigint in the Data Catalog but contains the string 'N/A' in some records. Which action should the data engineer take to allow Athena to query the data without changing the underlying files?

Accepted Answer

Alter the table to set the column type to string in the AWS Glue Data Catalog.. The error is caused by a mismatch between the declared bigint type and actual string values in the files. The most direct fix that avoids rewriting data is to change the column type to string in the AWS Glue Data Catalog. Athena will then read the field as text, and the query will no longer attempt numeric conversion. Partition repair, projection, and SerDe tweaks do not address the type conversion failure.

Answer

Add a partition projection configuration for the table.

Answer

Run MSCK REPAIR TABLE on the table to refresh partitions.

Answer

Create a new table with the column as bigint and use a SerDe that skips malformed rows.

Question 10

A data engineer is using AWS Database Migration Service (AWS DMS) to replicate ongoing changes from an Amazon RDS for MySQL database to an Amazon S3 bucket in Parquet format. The replication task is configured with full load plus change data capture (CDC). After several hours, the engineer notices that the S3 bucket contains only the full load data and no incremental changes. Which action should the engineer take to ensure CDC changes are captured?

Accepted Answer

Enable binary logging on the source RDS for MySQL instance and set the binlog_format parameter to ROW.. For AWS DMS to perform ongoing replication from MySQL, the source must have binary logging enabled and binlog_format set to ROW. Without these settings, DMS cannot read the transaction log to capture changes. The engineer must enable binary logging and set the format to ROW, then restart the task. Other actions like scaling the instance or enabling Multi-AZ do not enable CDC.

Answer

Increase the DMS replication instance size to handle the change volume.

Answer

Enable Multi-AZ on the source RDS for MySQL instance.

Answer

Modify the DMS task to use a different target endpoint for CDC data.

Question 11

A company uses Amazon Kinesis Data Streams with a Lambda consumer. The Lambda function is failing with 'ProvisionedThroughputExceededException' when writing to a DynamoDB table. Which action should the data engineer take to resolve this without losing data?

Accepted Answer

Configure a dead-letter queue (DLQ) on the Lambda function and increase the DynamoDB write capacity.. The Lambda function is throttled by DynamoDB because the write capacity is insufficient for the incoming Kinesis stream rate. Increasing the DynamoDB write capacity resolves the throttling, and configuring a DLQ on the Lambda function ensures that any records that fail after retries are captured for later processing, preventing data loss. This combination directly addresses the root cause while providing a safety net.

Answer

Reduce the number of Kinesis shards to lower the ingestion rate.

Answer

Increase the DynamoDB table's read capacity.

Answer

Disable retries on the Lambda function to avoid throttling.

Question 12

A data engineer manages an AWS Glue job that processes JSON files from Amazon S3 and writes Parquet to another S3 location. The job intermittently fails with 'Unable to find catalog table' errors, even though the table exists in the Glue Data Catalog. The job's IAM role has full S3 access but only limited Glue permissions. Which action will resolve the failure with the LEAST privilege?

Accepted Answer

Add glue:GetTable and glue:GetDatabase permissions for the specific database and table to the job's IAM role.. The job fails because its IAM role cannot call Glue Data Catalog APIs to retrieve table metadata. The minimal fix is to grant glue:GetTable and glue:GetDatabase for the specific database and table. This resolves the error while adhering to least privilege, unlike broad managed policies or irrelevant S3 permissions.

Answer

Enable AWS Glue Data Catalog encryption and update the job's security configuration.

Answer

Attach the AWSGlueServiceRole managed policy to the job's IAM role.

Answer

Modify the job to use a different IAM role that has s3:GetObject permissions on the catalog bucket.

Question 13

A data engineer is managing an Amazon Redshift cluster that experiences performance degradation during peak query hours. The engineer notices that some queries are waiting in the queue for a long time, and the WLM (Workload Management) configuration is set to auto. The engineer wants to implement manual WLM to improve query throughput and ensure that short-running queries are not blocked by long-running ones. Which TWO actions should the engineer take to achieve this? (Choose two.)

Accepted Answer

Set the WLM timeout for the long-running query queue to automatically terminate queries that exceed a specified time.. Manual WLM with separate queues for short and long queries, along with WLM timeouts for long queries, directly addresses the issue of short queries being blocked. Separate queues provide resource isolation, while timeouts prevent long queries from consuming resources indefinitely. These two actions together improve throughput and ensure responsiveness during peak hours.

Answer

Use query monitoring rules (QMR) to log queries that exceed resource thresholds and send alerts.

Answer

Increase the number of nodes in the Redshift cluster to provide more memory and CPU resources.

Answer

Enable concurrency scaling to automatically add cluster capacity for bursts of queries.

Question 14

A data engineer maintains an AWS Glue job that reads JSON files from Amazon S3, applies a transform, and writes Parquet to a second bucket. The job's bookmark was enabled at creation, but each nightly run reprocesses all previously handled files, and downstream tables now contain duplicate rows. The job script has not been modified and the S3 prefix is unchanged. Which action will MOST directly resolve the duplicate processing?

Accepted Answer

Confirm the transformation_ctx parameter is passed to each source and sink call, then reset the job bookmark and rerun once to rebuild state.. AWS Glue job bookmarks persist per-source and per-sink state keyed by the transformation_ctx argument. If that context is missing or inconsistent, the job cannot determine which objects were already processed and re-reads the entire prefix, producing duplicates. Passing a stable transformation_ctx and resetting the bookmark to rebuild the state store fixes the incremental behavior without changing the transform logic.

Answer

Reprocess the prefix with a job that has job bookmarks disabled and rely on the S3 object LastModified timestamp to filter files.

Answer

Increase the number of AWS Glue DPUs allocated to the job so the run completes before the next scheduled trigger.

Answer

Change the job's output write mode to append and add a deduplication step that drops rows whose keys already exist.

Question 15

A data engineer is using Amazon Athena to query data stored in Amazon S3. The engineer notices that queries are slow and scan large amounts of data. The data is stored in CSV format without compression. Which action should the engineer take to improve query performance and reduce cost?

Accepted Answer

Convert the data to Parquet format and compress it with Snappy.. Athena queries are optimized by using columnar formats like Parquet, which allow column pruning, and compression, which reduces data scanned. Converting from CSV to Parquet with Snappy compression will dramatically reduce the amount of data read, improving performance and lowering cost. Partitioning can further help but is secondary to format and compression.

Answer

Use Amazon Redshift Spectrum to query the S3 data instead of Athena.

Answer

Increase the number of Athena query executions by using a larger workgroup.

Answer

Partition the data by date and store it in CSV format.