DEA-C01 Practice Test 3 — 30 Questions

Question 1

A data engineer is troubleshooting a Glue ETL job that reads from an S3 bucket and writes to a Redshift table. The job fails with a 'MemoryError' when processing a large dataset. Which TWO actions should the engineer take to resolve this issue? (Choose TWO.)

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

Accepted Answer

Increase the number of DPUs and set 'spark.sql.shuffle.partitions' to a higher value.. Increasing the number of DPUs (Data Processing Units) provides more memory and compute resources to the Glue job, directly addressing the MemoryError. Setting 'spark.sql.shuffle.partitions' to a higher value reduces the amount of data shuffled per partition, preventing out-of-memory errors during wide transformations like joins or aggregations.

Answer

Increase the number of DPUs and set 'coalesce(1)' in the script.

Answer

Decrease the number of DPUs and increase 'spark.shuffle.partitions'.

Answer

Set the 'RedshiftTempDir' parameter to a larger S3 bucket.

Question 2

A data engineer is designing a streaming ingestion pipeline using Amazon Kinesis Data Streams. The stream has 10 shards, and the data volume is expected to grow by 50% over the next month. The engineer needs to ensure that the pipeline can scale without manual intervention. Which approach should be used?

Accepted Answer

Switch the Kinesis stream to on-demand capacity mode. Kinesis Data Streams on-demand capacity mode automatically scales the number of shards based on the incoming traffic pattern, eliminating the need for manual intervention. Option A is incorrect because CloudWatch Alarms can trigger a Lambda function to add shards via the UpdateShardCount API, but this approach requires custom code and does not provide automatic scaling without manual setup. Option B is incorrect because Auto Scaling groups are used for EC2 instances, not for Kinesis stream shards. Option D is incorrect because Lambda functions can be used to scale shards programmatically, but this still requires custom implementation and is not a native automatic scaling feature.

Answer

Set up a CloudWatch Alarm to trigger a Lambda function to add shards

Answer

Use an Auto Scaling group to add more shards

Answer

Configure the stream to use a Lambda function that scales shards

Question 3

A logistics company ingests real-time GPS location data from thousands of delivery vehicles into Amazon Kinesis Data Streams. Each vehicle sends a JSON payload every 10 seconds containing vehicle_id, latitude, longitude, timestamp, and speed. The data must be stored in Amazon S3 for historical analysis, but the company wants to first aggregate the data per vehicle per minute (average speed, min/max coordinates) to reduce storage costs. The solution must be serverless and handle potential duplicate records without double-counting. What should the engineer do?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to aggregate data in a 1-minute tumbling window with deduplication logic, then output to Kinesis Data Firehose for delivery to S3.. Amazon Kinesis Data Analytics for Apache Flink can process streaming data with tumbling windows (1-minute) to aggregate per vehicle per minute, and Flink's stateful processing allows deduplication to avoid double-counting. The output is then sent to Kinesis Data Firehose for persistent storage in Amazon S3, all serverless. Option A (EMR with Spark Streaming) is not serverless. Option C (Kinesis Data Firehose with Lambda transformation) cannot perform stateful windowed aggregation and deduplication efficiently—it processes events individually without maintaining state across records. Option D (AWS Glue streaming ETL job) is serverless but adds higher latency and complexity compared to Kinesis Data Analytics, and is not as optimized for low-latency streaming aggregations with deduplication.

Answer

Use Amazon EMR with Spark Streaming to perform the aggregation and write to S3.

Answer

Use Kinesis Data Firehose with a Lambda transformation to aggregate records in a 1-minute window.

Answer

Use an AWS Glue streaming ETL job with Spark Structured Streaming to aggregate and deduplicate.

Question 4

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 5

A company ingests streaming data from social media APIs into Kinesis Data Streams. Each record is approximately 5 KB. The data must be enriched with geolocation information from a DynamoDB table before being stored in S3. The enrichment process takes about 200 ms per record. Which architecture minimizes latency and cost?

Accepted Answer

Use Kinesis Data Firehose with a Lambda function that queries DynamoDB. Kinesis Data Firehose can invoke a Lambda function for per-record enrichment, providing automatic scaling and low operational overhead. Option A is wrong because an EC2 instance adds significant operational overhead and requires manual scaling, increasing complexity and cost. Option B is wrong because AWS Glue ETL jobs are optimized for batch processing, not continuous streaming with low latency per record. Option C is wrong because Kinesis Data Analytics (SQL) is designed for real-time analytics on streaming data, not for per-record enrichment involving external lookups like DynamoDB.

Answer

Use an EC2 instance running a custom application to consume from Kinesis, enrich, and write to S3

Answer

Use AWS Glue ETL jobs running continuously on the stream

Answer

Use Kinesis Data Analytics to perform enrichment with SQL

Question 6

A data engineer is migrating an on-premises Apache HBase workload to Amazon DynamoDB. The application requires strongly consistent reads and the ability to query by a composite key (partition key + sort key). Which DynamoDB table design should be used?

Accepted Answer

Create a table with a partition key and sort key, and use ConsistentRead parameter.. DynamoDB natively supports strongly consistent reads when you use the `ConsistentRead` parameter set to `true` on GetItem, Query, or Scan operations. By defining a table with a partition key and sort key, you can directly query by the composite key (partition key + sort key) with strong consistency, meeting both requirements without additional infrastructure.

Answer

Use a secondary index with strongly consistent reads.

Answer

Use a local secondary index (LSI) with the composite key.

Answer

Create a global secondary index (GSI) with the composite key.

Question 7

Refer to the exhibit. A data engineer is troubleshooting an IAM policy attached to a user. The user reports that they cannot upload objects to the S3 bucket 'data-lake-bucket' unless they explicitly specify the 'x-amz-server-side-encryption' header with value 'AES256'. The engineer wants to modify the policy to allow uploads without requiring encryption headers, but still enforce encryption on the bucket itself. Which change should the engineer make?

Accepted Answer

Remove the entire Deny statement.. Removing the Deny statement eliminates the explicit denial that blocks uploads without the 'x-amz-server-side-encryption' header set to 'AES256'. The Allow statement already grants s3:PutObject, and by removing the Deny, users can upload without specifying encryption headers. The bucket's default encryption setting (e.g., AES256) will then automatically encrypt objects at rest, enforcing encryption without requiring the header in the request.

Answer

Remove the Condition block from the Allow statement.

Answer

Change the Condition in the Allow statement to use aws:kms instead of AES256.

Answer

Set the bucket's default encryption to AES256 and keep the policy unchanged.

Question 8

A company runs a daily batch processing job on Amazon EMR that reads data from Amazon S3 and writes results back to S3. The job takes longer than expected. The engineer wants to monitor the job's resource utilization. Which AWS service should be used to collect and visualize metrics such as CPU and memory usage of the EMR cluster's nodes?

Accepted Answer

Amazon CloudWatch with the CloudWatch Agent installed on the EMR nodes.. Amazon CloudWatch with the CloudWatch Agent installed on EMR nodes can collect CPU, memory, and other system-level metrics, which can be visualized in CloudWatch dashboards. Option A is incorrect because AWS Config records configuration changes, not resource utilization. Option B is incorrect because Amazon Athena is a query service for data in S3, not a monitoring service. Option D is incorrect because AWS CloudTrail logs API calls, not performance metrics.

Answer

AWS Config to record configuration changes in the EMR cluster.

Answer

Amazon Athena to query EMR job logs stored in S3.

Answer

AWS CloudTrail to log API calls made by the EMR job.

Question 9

A marketing analytics team needs to ingest customer transaction data from an on-premises PostgreSQL database into Amazon S3 for analysis. The data volume is about 10 GB daily, and the team wants to perform full refresh daily (truncate and load) into S3 as Parquet files. The company has a Direct Connect connection to AWS. The team needs a simple, managed solution that minimizes operational overhead. What should the team use?

Accepted Answer

Use an AWS Glue ETL job with a JDBC connection to the PostgreSQL database, extract data, and write to S3 in Parquet format.. AWS Glue ETL job is the best choice for this scenario. It is a fully managed service that can connect to on-premises PostgreSQL via a JDBC connection (using an AWS Glue connection with the appropriate network configuration over Direct Connect). It can perform a full extract (truncate and load) each day and write the data directly to S3 in Parquet format, minimizing operational overhead. Option A (AWS DMS) is designed for ongoing change data capture (CDC) and continuous replication, not for daily full refreshes without incremental changes. Option B (Amazon EMR with Spark) requires managing clusters and is more complex than necessary for a simple daily load. Option D (AWS Data Pipeline) requires custom scripting and more configuration compared to Glue's built-in ETL capabilities. Therefore, option C is correct.

Answer

Set up AWS Database Migration Service (DMS) to continuously replicate data to S3 in Parquet format.

Answer

Use Amazon EMR with a Spark job that reads from PostgreSQL and writes to S3.

Answer

Use AWS Data Pipeline with a SQLActivity to extract data and copy to S3.

Question 10

A data engineer is implementing a CDC (Change Data Capture) pipeline from a relational database to Amazon S3 using AWS Database Migration Service (DMS). Which TWO configurations are required for continuous replication?

Accepted Answer

Enable binary logging on the source database.. For continuous CDC replication with AWS DMS, two key configurations are required: enabling binary logging on the source database (option B) and selecting both 'Full load' and 'Ongoing replication' in the DMS task (option D). Binary logging (e.g., binlog for MySQL) captures ongoing changes, while enabling both full load and ongoing replication ensures that the initial data load is completed before continuous change capture begins. Option A (transformation rules) is optional, not required. Option C (VPC endpoint) is not necessary because DMS can connect to the source database over the internet, VPN, or VPC peering without a VPC endpoint. Option E (pre-creating the target table in S3) is not needed because DMS can create the target table automatically or you can let DMS handle the schema.

Answer

Define transformation rules in the DMS task.

Answer

Configure a VPC endpoint for DMS.

Answer

Pre-create the target table in S3.

Question 11

A company stores sensitive data in Amazon S3. To meet compliance requirements, they need to ensure that any data older than 1 year is automatically moved to a lower-cost storage class. Which S3 feature should they use?

Accepted Answer

S3 Lifecycle policies. S3 Lifecycle policies allow you to define rules to automatically transition objects between storage classes based on age. Option B is correct. Option A is incorrect because S3 Replication is used to replicate objects across buckets, not to transition storage classes. Option C is incorrect because S3 Glacier is a storage class, not a feature that automates transitions. Option D is incorrect because S3 Intelligent-Tiering automatically moves data between access tiers based on usage, not on a fixed age threshold like 1 year.

Answer

S3 Replication

Answer

S3 Glacier

Answer

S3 Intelligent-Tiering

Question 12

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. A Lambda function processes each record. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' when writing results to a DynamoDB table. The data engineer has already increased the DynamoDB write capacity. What else can the engineer do to resolve the issue?

Accepted Answer

Increase the number of shards in the Kinesis stream.. Increasing the number of shards in the Kinesis stream increases the number of concurrent Lambda invocations, distributing the write load across more Lambda functions and reducing the number of writes per second to DynamoDB from each invocation. This helps mitigate ProvisionedThroughputExceededException. Option A is incorrect: increasing Lambda memory does not directly affect DynamoDB write throttling. Option B is incorrect: increasing DynamoDB read capacity does not help with write throttling. Option C is incorrect: decreasing the batch size to 1 reduces the number of records per invocation, but since each shard still invokes Lambda, it may increase the number of invocations and potentially increase the write frequency, worsening the throttling.

Answer

Increase the Lambda function memory.

Answer

Increase the DynamoDB read capacity units.

Answer

Decrease the Lambda batch size to 1.

Question 13

A data engineer uses Amazon EMR to run a Spark job that reads from S3 and writes to HDFS on the cluster. The job fails with an 'OutOfMemoryError: Java heap space' error in the executors. Which parameter adjustment should be made to resolve this?

Accepted Answer

Increase spark.executor.memory. The 'OutOfMemoryError: Java heap space' in executors indicates that the executor memory is insufficient for the data being processed. Increasing spark.executor.memory allocates more heap space to each executor, directly addressing the issue. Option A (spark.default.parallelism) controls the number of tasks, not memory. Option B (spark.sql.shuffle.partitions) affects shuffle partitions but does not increase memory. Option D (spark.driver.memory) is for the driver, not executors.

Answer

Increase spark.default.parallelism

Answer

Increase spark.sql.shuffle.partitions

Answer

Increase spark.driver.memory

Question 14

A company is using Amazon Redshift for analytics. The cluster has 20 nodes and the data is evenly distributed. Query performance has degraded over time. The data engineer suspects that table maintenance is needed. Which set of operations should be performed to improve query performance?

Accepted Answer

Run VACUUM and ANALYZE commands on all tables. Over time, Amazon Redshift tables accumulate deleted rows and unsorted data due to UPDATE and DELETE operations, which degrades query performance. Running VACUUM reclaims space and re-sorts data according to the table's sort key, while ANALYZE updates table statistics used by the query optimizer. Together, these operations restore data layout and enable efficient query planning, directly addressing the performance degradation.

Answer

Run VACUUM FULL on all tables

Answer

Run REINDEX on all tables

Answer

Run ALTER TABLE APPEND to reorganize data

Question 15

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from an S3 bucket. The job logs show the following error: 'java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found'. Which TWO actions will resolve this issue?

Accepted Answer

Include the hadoop-aws jar as an extra jar in the Glue job configuration.. The error 'ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem' indicates the S3A filesystem library is missing from the classpath. Option B resolves this by adding the hadoop-aws jar as an extra jar, which provides the S3AFileSystem class. Option D resolves it by using a Glue version (3.0 or later) that includes the S3A library by default. Option A (VPC endpoint) addresses networking, not classpath. Option C (IAM role) addresses permissions, not missing classes. Option E (EMRFS) is for Amazon EMR, not AWS Glue.

Answer

Enable VPC S3 endpoint for the Glue job.

Answer

Update the IAM role to allow access to S3.

Answer

Change the S3 access mode from S3A to EMRFS.

Question 16

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by a custom consumer application that writes to Amazon S3 every 5 minutes. The consumer is falling behind and processing lag is increasing. Which action is MOST effective to reduce the lag?

Accepted Answer

Increase the number of shards in the Kinesis stream. The consumer is falling behind because the stream's throughput capacity is insufficient for the incoming data volume. Increasing the number of shards in the Kinesis stream directly increases the total read capacity (each shard provides 2 MB/s read throughput and 5 transactions/second), allowing the consumer to process more data in parallel and reduce lag.

Answer

Switch to Amazon Kinesis Data Firehose to deliver data directly to S3

Answer

Increase the batch size of records written to S3

Answer

Reduce the retention period of the stream

Question 17

A company runs an Amazon RDS for PostgreSQL database. To meet disaster recovery requirements, they set up a cross-Region read replica. The replica has been lagging by several minutes. Which action is MOST effective to reduce the replica lag?

Accepted Answer

Increase the instance size (memory and CPU) of the replica.. Increasing the instance size (memory and CPU) of the cross-Region read replica is the most effective action because replica lag in Amazon RDS for PostgreSQL is often caused by the replica being unable to keep up with the volume of write-ahead log (WAL) data arriving from the primary. A larger replica instance provides more compute and memory resources to apply WAL changes faster, reducing the replay lag. This directly addresses the bottleneck at the replica side without impacting the primary database.

Answer

Enable Multi-AZ on the primary database.

Answer

Increase the instance size of the primary database.

Answer

Decrease the instance size of the replica to reduce cost.

Question 18

A company uses AWS Lake Formation to manage access to data in S3. A data analyst reports being unable to query a table in Amazon Athena, receiving an 'Access Denied' error. The analyst has SELECT permission on the table in Lake Formation. What additional configuration is MOST likely causing the issue?

Accepted Answer

The IAM role used by Athena does not have S3 GetObject permission on the underlying data. In AWS Lake Formation, even when a user has SELECT permission on a table, the IAM role that Athena uses must have S3 GetObject permission on the underlying data files. Lake Formation manages permissions at the metadata level, but the actual data access is enforced by S3 bucket policies and IAM. Without GetObject permission, Athena fails with 'Access Denied'. Option A is incorrect because Athena typically has access to the Glue Data Catalog if the table is visible; the error is about data access. Option C is incorrect because SELECT permission usually includes DESCRIBE, and the error message is about access denied, not missing describe. Option D is incorrect because the table is registered with Lake Formation since the analyst has SELECT permission.

Answer

Athena does not have permission to access the Glue Data Catalog

Answer

The analyst does not have DESCRIBE permission

Answer

The table is not registered with Lake Formation

Question 19

A company stores sensitive user data in an Amazon RDS for PostgreSQL DB instance. A security audit requires that all data be encrypted at rest. The database is currently unencrypted. What is the MOST operationally efficient way to enable encryption at rest?

Accepted Answer

Take a snapshot of the DB instance, copy it with encryption enabled, and restore the snapshot to a new DB instance.. RDS for PostgreSQL does not support enabling encryption at rest on an existing unencrypted DB instance directly. The only way to achieve this is by taking a snapshot of the unencrypted instance, creating an encrypted copy of that snapshot, and then restoring it to a new encrypted DB instance. This method is operationally efficient as it uses native RDS snapshot copy and restore capabilities without requiring additional infrastructure or manual data migration.

Answer

Create a read replica with encryption enabled and promote it.

Answer

Modify the DB instance and enable encryption in the console.

Answer

Modify the DB parameter group to include encryption parameters and reboot the instance.

Question 20

Which THREE are valid considerations when troubleshooting data loss in an AWS Glue ETL job? (Choose three.)

Accepted Answer

Job bookmarks may be skipping new data if not configured properly.. Options A, D, and E are correct. Job bookmarks may skip new data if not configured properly (A). Dynamic frame transformations can drop rows if errors occur during processing (D). Incorrect mapping of source columns to target columns can cause data loss (E). Option B is incorrect because disabling server-side encryption on S3 does not cause data loss; it affects data at rest encryption but not data integrity during ETL. Option C is incorrect because setting the job timeout too low may cause the job to fail prematurely, but it does not directly cause data loss; data can be reprocessed after adjusting the timeout.

Answer

Server-side encryption is disabled on the S3 bucket.

Answer

The job timeout is set too low.