DEA-C01 Practice Test 2 — 25 Questions

Question 1

A company uses Amazon RDS for MySQL as a source for AWS DMS to replicate data to S3. The replication task is failing with 'OutOfMemory' errors on the DMS instance. The source table has 10 million rows with large BLOB columns. Which THREE changes would most likely resolve the issue?

Accepted Answer

Set the LOB column settings to 'Limited LOB mode' and specify a max LOB size.. Option A is correct because setting LOB columns to 'Limited LOB mode' with a specified max LOB size prevents DMS from loading entire LOBs into memory. Instead, DMS truncates LOBs to the specified size, reducing memory consumption and avoiding OutOfMemory errors when replicating large BLOB columns from MySQL to S3.

Answer

Disable logging for the DMS task to free memory.

Answer

Enable Full LOB mode to handle LOBs more efficiently.

Question 2

A data engineer is setting up an Amazon Kinesis Data Analytics application to process streaming data from a Kinesis data stream named "input-stream". The application uses a reference data source from an S3 bucket. The engineer has attached the IAM policy shown in the exhibit to the application's IAM role. When starting the application, the engineer receives an 'AccessDeniedException' error. Which additional permission is required?

Accepted Answer

s3:GetObject on the S3 bucket containing the reference data. The Kinesis Data Analytics application needs to read reference data from the S3 bucket, which requires the s3:GetObject permission on the bucket and its objects. The error 'AccessDeniedException' indicates the IAM role lacks this specific permission to retrieve the reference data file. Option B correctly adds the missing s3:GetObject action to allow the application to fetch the reference data from S3.

Answer

kinesis:PutRecord on the input stream

Answer

kinesis:CreateStream on the input stream

Answer

kinesis:PutRecords on the input stream

Question 3

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that ingests JSON log data from web servers. The stream is configured to transform records with an AWS Lambda function and deliver to an Amazon S3 bucket. Recently, the stream has been failing with 'InvalidData' errors. Which action should the engineer take to resolve the issue?

Accepted Answer

Check the CloudWatch Logs for the Lambda function to identify transformation errors.. The 'InvalidData' error in Kinesis Data Firehose typically indicates that the Lambda function used for data transformation is failing or returning malformed records. By checking the CloudWatch Logs for the Lambda function, the engineer can identify specific transformation errors, such as incorrect JSON parsing, missing fields, or exceptions, which cause Firehose to reject the records. This is the most direct troubleshooting step because Firehose relies on the Lambda function to return valid transformed data in the expected format.

Answer

Verify the S3 bucket policy allows Firehose to write.

Answer

Increase the buffer size and interval in the Firehose delivery stream.

Answer

Change the data format to CSV in the Firehose configuration.

Question 4

A company is migrating a legacy data warehouse to Amazon Redshift. They need to choose a distribution style to minimize data movement during joins. Which THREE factors should they consider?

Accepted Answer

The size of the table (number of rows).. Option A is correct because the size of the table (number of rows) directly influences the distribution strategy. In Amazon Redshift, large tables benefit from a distribution style that evenly distributes rows across slices to avoid data skew, which can cause performance bottlenecks during joins. Choosing a distribution key that aligns with the join columns minimizes data movement, but the table size determines whether an ALL distribution (for small tables) or a KEY distribution (for large tables) is more appropriate to reduce shuffling.

Answer

The number of columns in the table.

Answer

The data type of the distribution key column.

Question 5

A data engineer is designing a data lake on Amazon S3. The data must be immutable and support high-throughput streaming ingestion. Which THREE features should the engineer consider? (Select THREE.)

Accepted Answer

S3 Lifecycle policies to transition data to Amazon S3 Glacier. S3 Object Lock in governance mode (Option D) is correct because it enforces immutability by preventing objects from being deleted or overwritten for a specified retention period, which is essential for a data lake requiring immutable data. S3 Multipart Upload API (Option C) is correct because it enables high-throughput streaming ingestion by allowing large objects to be uploaded in parallel parts, improving throughput and resilience. S3 Lifecycle policies to transition data to Amazon S3 Glacier (Option B) is correct because it supports cost-effective storage management for immutable data that is rarely accessed, aligning with the data lake's lifecycle needs.

Answer

S3 Transfer Acceleration

Answer

S3 Cross-Region Replication (CRR)

Question 6

Which THREE storage classes in Amazon S3 are designed for infrequently accessed data with millisecond retrieval times? (Select THREE.)

Accepted Answer

S3 One Zone-IA. S3 One Zone-IA is designed for infrequently accessed data that requires millisecond retrieval times, but does not require the resilience of multiple Availability Zones. It stores data in a single AZ and offers the same low-latency performance as S3 Standard, making it suitable for non-critical, infrequently accessed data.

Answer

S3 Glacier Flexible Retrieval

Answer

S3 Glacier Deep Archive

Question 7

A company is designing a data lake on Amazon S3. Which TWO strategies improve query performance for Amazon Athena?

Accepted Answer

Partition the data by frequently queried columns such as date or region.. Partitioning data by frequently queried columns (e.g., date or region) allows Athena to prune the data scanned by only reading the relevant partitions, reducing the amount of data scanned and improving query performance. This is a core optimization for Athena, which charges based on data scanned and performs better with less I/O.

Answer

Enable S3 Versioning on the bucket.

Answer

Use server-side encryption with AWS KMS (SSE-KMS).

Answer

Store data in CSV format with header rows.

Question 8

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data is transformed using an AWS Lambda function. Recently, the transformation errors have increased due to Lambda timeouts. The data engineer needs to diagnose and resolve the issue without losing data. What should the engineer do?

Accepted Answer

Increase the Lambda function timeout and ensure that failed records are sent to a backup S3 bucket. Option A is correct because increasing the Lambda function timeout directly addresses the root cause of transformation errors (timeouts), and configuring a backup S3 bucket for failed records ensures no data loss. Kinesis Data Firehose can be configured to send failed records to a separate S3 bucket as a dead-letter queue, which preserves the data for later reprocessing while the primary transformation pipeline is fixed.

Answer

Enable Amazon CloudWatch Logs for the Lambda function to capture errors and store failed records in CloudWatch

Answer

Configure the Lambda function to write failed records to an Amazon SQS queue for later reprocessing

Answer

Modify the Lambda function to store failed records in Amazon S3 before processing

Question 9

A data engineer notices that an AWS Glue job processing data from an Amazon S3 bucket frequently fails with 'OutOfMemoryError'. The job reads CSV files, applies transformations, and writes Parquet to another S3 bucket. The job has 10 workers of type G.1X. Which change is MOST likely to resolve the issue?

Accepted Answer

Change the worker type from G.1X to G.2X. The G.1X worker type provides 16 GB of memory per worker. An OutOfMemoryError indicates that the job's memory requirements exceed this limit. Upgrading to G.2X doubles the memory per worker to 32 GB, directly addressing the memory shortage without changing the parallelism or incurring the overhead of additional workers.

Answer

Increase the number of workers to 20

Answer

Change the worker type from G.1X to G.8X

Answer

Enable the Spark UI to monitor memory and tune the job

Question 10

A data team runs a daily AWS Glue ETL job that processes data from an Amazon Redshift cluster and writes results to Amazon S3. The job completes successfully but takes 2 hours longer than expected. The job uses the JDBC connection to Redshift. The Redshift cluster is 4 dc2.large nodes. The Glue job has 10 workers of type G.1X. Which change would MOST likely reduce the job duration?

Accepted Answer

Use the S3 staging option in the Glue connection to unload data from Redshift to S3 first. The JDBC connection in AWS Glue reads data row-by-row from Redshift, which is slow for large datasets. By enabling the S3 staging option in the Glue connection, the job uses Redshift's UNLOAD command to export data to S3 in parallel, then Glue reads from S3. This bypasses the JDBC bottleneck and leverages Redshift's massively parallel processing (MPP) to export data much faster.

Answer

Use Redshift Spectrum to query data directly from S3

Answer

Increase the Redshift cluster size to 8 nodes

Answer

Increase the number of Glue workers to 20

Question 11

A data engineer runs the above AWS CLI command and receives the output. The object is part of an S3 Lifecycle policy that transitions objects to Glacier Instant Retrieval after 30 days. The object was created on January 1, 2023. Why is the object still in STANDARD_IA storage class?

Accepted Answer

The object has not reached the transition age of 30 days yet. Option C is correct because the S3 Lifecycle rule transitions objects to Glacier Instant Retrieval after 30 days, but the object was created on January 1, 2023, and the current date (implied by the command output) is before January 31, 2023. The transition age is calculated from the object's LastModified date, not from any other timestamp, and the object must be at least 30 days old before S3 applies the transition. Since the object is only 20 days old (as of January 21, 2023, based on the output showing STANDARD_IA), it has not yet met the 30-day threshold.

Answer

The Lifecycle policy has a filter that excludes this object's prefix

Answer

Versioning is enabled and the current version is not the oldest

Answer

The metadata timestamp is used for lifecycle transitions instead of LastModified

Question 12

A data engineer is designing a data lake on Amazon S3 for analytics. The data includes sensitive PII that must be encrypted at rest. The company requires that the encryption keys be managed by the company's own hardware security module (HSM) and rotated every 90 days. Which TWO options meet these requirements? (Choose TWO.)

Accepted Answer

Use S3 server-side encryption with customer-provided keys (SSE-C). Option B is correct because SSE-C allows you to provide your own encryption keys, which can be managed and rotated from your own HSM. The keys are used server-side by S3 to encrypt objects at rest, but S3 does not store the keys—you manage them entirely, meeting the requirement for key management on your own HSM with 90-day rotation.

Answer

Use S3 server-side encryption with AWS KMS and an AWS managed key

Answer

Use client-side encryption with keys stored in AWS Secrets Manager

Answer

Use S3 server-side encryption with S3 managed keys (SSE-S3)

Question 13

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.)

Accepted Answer

Increase the number of DPUs and set 'spark.sql.shuffle.partitions' to a higher value.. Option A is correct because 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 14

A data engineer is troubleshooting an AWS Glue job that reads from an Amazon RDS for PostgreSQL database using a JDBC connection. The job fails with the error 'java.sql.SQLException: No suitable driver'. Which TWO actions should the engineer take to resolve this issue? (Select TWO.)

Accepted Answer

Verify that the connection string in the job's JDBC URL uses the correct format and includes the driver class. Option A is correct because the 'No suitable driver' error in JDBC indicates that the driver class specified in the JDBC URL is either missing or incorrect. For PostgreSQL, the JDBC URL must follow the format 'jdbc:postgresql://host:port/database' and the driver class must be 'org.postgresql.Driver'. If the URL is malformed or the driver class is not properly referenced, the Glue job cannot load the driver, leading to this specific SQLException.

Answer

Check that the Glue job's VPC and security groups allow outbound traffic to the RDS instance

Answer

Restart the Glue job with a higher timeout value

Answer

Update the IAM role associated with the Glue job to allow 'rds:*' permissions

Question 15

A company runs an Amazon EMR cluster with Spark jobs that process data from Amazon S3. The data engineer receives an alert that one of the Spark jobs failed with an OutOfMemoryError. The job processes large files and uses the default Spark configurations. Which configuration change is MOST likely to resolve the issue?

Accepted Answer

Increase the spark.executor.memory configuration.. Option A is correct because increasing spark.executor.memory allocates more memory per executor, directly addressing the OutOfMemoryError when processing large files. Option B (increasing executors) does not increase memory per executor, so each executor remains susceptible to OOM. Option C (disabling dynamic resource allocation) would prevent the cluster from adding resources dynamically, potentially worsening the situation. Option D (decreasing cores per executor) reduces parallelism but does not increase memory per executor; the OOM occurs because each executor lacks sufficient memory, not because of too many cores.

Answer

Increase the number of executors.

Answer

Disable dynamic resource allocation.

Answer

Decrease the number of cores per executor.

Question 16

A data engineer runs an AWS Glue crawler that is configured to crawl an S3 bucket named 'my-data-lake' and update the Glue Data Catalog. The crawler fails with an access denied error. The IAM role attached to the crawler has the policy shown in the exhibit. What is the likely cause of the failure?

Accepted Answer

The policy does not allow logging to CloudWatch Logs.. AWS Glue crawlers require permissions to write logs to CloudWatch Logs for monitoring and debugging. Without the `logs:CreateLogGroup`, `logs:CreateLogStream`, and `logs:PutLogEvents` actions, the crawler fails with an access denied error even if it has S3 and Glue Data Catalog permissions. The IAM policy shown does not include these CloudWatch Logs permissions, making option C the correct answer.

Answer

The policy does not allow glue:CreateTable on the 'my-data-lake' database.

Answer

The policy does not allow s3:PutObject on the 'my-data-lake' bucket.

Answer

The policy does not allow s3:ListBucket on the 'my-data-lake' bucket.

Question 17

A data engineer needs to ingest streaming data from an e-commerce application into Amazon S3 for near-real-time analytics. The solution must handle variable throughput and allow reprocessing of failed records. Which TWO AWS services should the engineer use? (Choose two.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is correct because it provides a durable, scalable, real-time data ingestion layer that can handle variable throughput by sharding, and it retains data for up to 365 days, enabling reprocessing of failed records by replaying from a specific sequence number or timestamp.

Answer

Amazon SQS

Answer

Amazon DynamoDB Streams

Answer

Amazon RDS

Question 18

A data engineer runs a Spark job on Amazon EMR that reads data from Amazon S3 and writes results back to S3. The job fails with an 'S3AccessDenied' error. The engineer verifies that the IAM role attached to the EMR cluster has s3:GetObject and s3:PutObject permissions on the relevant buckets. What is the MOST likely cause of the error?

Accepted Answer

The IAM role does not have s3:ListBucket permission on the bucket.. The IAM role attached to the EMR cluster must have the s3:ListBucket permission on the bucket to allow the Spark job to enumerate objects when reading from S3. Without this permission, even with s3:GetObject and s3:PutObject, the job fails with an 'S3AccessDenied' error because the S3 list operation is required for directory listing and file discovery.

Answer

S3 Transfer Acceleration is not enabled on the bucket.

Answer

EMRFS consistent view is not configured.

Answer

The S3 bucket is in a different AWS Region than the EMR cluster.

Question 19

A data engineer notices that an Amazon Kinesis Data Firehose delivery stream is failing to deliver data to an Amazon S3 bucket. The CloudWatch metrics show 'DeliveryToS3.Success' is 0 and 'S3.BucketExists' is 1. What is the MOST likely cause?

Accepted Answer

The IAM role for Firehose lacks s3:PutObject permission.. The metric 'S3.BucketExists' is 1, confirming the S3 bucket exists, so the issue is not bucket existence. With 'DeliveryToS3.Success' at 0, the failure is in the write operation. The IAM role assumed by Firehose must have the s3:PutObject permission to deliver data; lacking it would cause all delivery attempts to fail silently, matching the observed metrics.

Answer

The S3 bucket has an ACL that denies access to Firehose.

Answer

The Firehose delivery stream Lambda transformation function is failing.

Answer

The S3 bucket does not exist.

Question 20

A data engineer is monitoring an Amazon Kinesis Data Analytics for Apache Flink application that processes streaming data. The application is falling behind (increasing 'MillisBehindLatest') and the CPU utilization of the Flink task managers is consistently above 80%. Which THREE actions should the engineer take to improve performance? (Choose THREE.)

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Increasing the number of shards in the Kinesis data stream (Option A) directly increases the ingestion capacity and parallelism source for the Flink application. With more shards, the application can read data from more partitions concurrently, reducing the backlog indicated by 'MillisBehindLatest'. This is a fundamental scaling action for Kinesis-based Flink applications.

Answer

Decrease the checkpoint interval to reduce state size.

Answer

Decrease the number of task managers to reduce CPU contention.