DEA-C01 Practice Test 13 — 15 Questions

Question 1

A company is using Kinesis Data Firehose to deliver data to an S3 bucket. The delivery stream is failing with 'S3 bucket access denied' errors. The bucket policy allows the Firehose service principal. What could be the issue?

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

Accepted Answer

The IAM role assigned to Firehose lacks s3:PutObject permission. Even though the S3 bucket policy allows the Firehose service principal, Kinesis Data Firehose uses an IAM role to write data. This role must have the s3:PutObject permission. Without it, Firehose will receive an access denied error. Option A is incorrect because VPC differences affect network connectivity, not IAM permissions. Option B is incorrect because SSE-KMS requires KMS permissions, but the error here is specifically about S3 access. Option C is incorrect because bucket name validation occurs during stream creation, not during data delivery.

Answer

The S3 bucket is in a different VPC

Answer

The S3 bucket uses SSE-KMS and Firehose does not have KMS permissions

Answer

The S3 bucket name contains invalid characters

Question 2

Refer to the exhibit. This log snippet is from a failed AWS Glue job. The job processes a large dataset in memory. What is the MOST likely cause of the OutOfMemoryError?

Accepted Answer

The Glue job is running with insufficient DPUs or worker type.. An OutOfMemoryError in AWS Glue typically occurs when the allocated DPUs or worker type are insufficient for the in-memory processing of a large dataset. Option B is incorrect because unsupported file formats cause parsing errors, not memory errors. Option C is incorrect because mismatched keys in a join cause data skew or incorrect results, but not directly an OutOfMemoryError. Option D is incorrect because too many partitions usually lead to small file overhead, not heap space exhaustion.

Answer

The input data is in an unsupported file format.

Answer

The job is attempting to join two tables with mismatched keys.

Answer

The job has too many partitions.

Question 3

A data engineer is using Amazon Athena to query data stored in Amazon S3. The data is partitioned by date, but the engineer notices that queries are scanning the entire bucket instead of only the relevant partitions. The table is defined in the AWS Glue Data Catalog. Which action should the engineer take to ensure that Athena only scans the necessary partitions?

Accepted Answer

Run the `MSCK REPAIR TABLE` command in Athena to update the partition metadata in the AWS Glue Data Catalog.. When Athena scans the entire bucket despite partitioning, it is often because the partition metadata in the AWS Glue Data Catalog is not up to date. Running `MSCK REPAIR TABLE` in Athena adds missing partitions to the catalog, allowing Athena to prune partitions and scan only the relevant data.

Answer

Increase the query timeout setting for the Athena workgroup to allow more time for scanning.

Answer

Convert the data to a columnar format such as Parquet and enable predicate pushdown.

Answer

Use AWS Glue crawlers to re-crawl the data and update the table schema.

Question 4

A company uses AWS Lake Formation to manage data lake permissions. The data lake contains sensitive customer data in the 'customer' database. The security team wants to ensure that only users with a specific tag 'access_level=analyst' can query the 'customer' table. Which combination of steps should the data engineer take to enforce this?

Accepted Answer

In Lake Formation, create an LF-tag 'access_level' with values 'analyst' and 'admin'. Grant 'SELECT' permission on the 'customer' table to the tag value 'analyst'. Associate the LF-tag with the 'customer' table.. Lake Formation LF-tags allow you to define metadata tags (key-value pairs) and grant permissions to those tags. By creating an LF-tag 'access_level' with values 'analyst' and 'admin', granting SELECT on the 'customer' table to the tag value 'analyst', and associating that LF-tag with the table, only principals who have the tag 'access_level=analyst' (or are granted via the tag) can query the table. This enforces tag-based access control at the Lake Formation permission layer, which is the intended mechanism for fine-grained, attribute-based access control in Lake Formation.

Answer

Create an IAM policy that conditionally allows 'glue:GetTable' based on the tag 'access_level=analyst'.

Answer

Apply a bucket policy on the S3 location of the 'customer' table that allows access only if the request carries the tag 'access_level=analyst'.

Answer

Use Lake Formation column-level filters to restrict access to columns based on the tag 'access_level=analyst'.

Question 5

A data engineer needs to ingest data from an on-premises Apache Kafka cluster into Amazon S3. The data volume is about 10 TB per day. The engineer wants to set up a managed Kafka connector. Which AWS service should they use?

Accepted Answer

Amazon MSK Connect. Amazon MSK Connect is a managed Kafka connector service that integrates with Amazon MSK or self-managed Apache Kafka clusters to stream data into Amazon S3 using Kafka Connect. It handles the 10 TB/day volume efficiently with auto-scaling and checkpointing, making it the correct choice for a managed connector setup.

Answer

AWS Database Migration Service

Answer

AWS Lambda with Kafka trigger

Answer

Amazon Kinesis Data Streams

Question 6

A company uses AWS Glue to transform data in S3. The transformation job reads Parquet files, filters rows, and writes to another S3 bucket. The job takes longer than expected. Which change would MOST likely reduce the job execution time?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job directly increases the parallelism of the Apache Spark-based execution environment. Since the job reads Parquet files, filters rows, and writes output, a bottleneck in compute capacity is the most likely cause of prolonged execution time. More DPUs allow Spark to distribute the workload across more executors, reducing overall runtime.

Answer

Use a single large file instead of multiple small files.

Answer

Reduce the number of partitions in the output data.

Answer

Convert the input files from Parquet to CSV format.

Question 7

A company uses Amazon Redshift for data warehousing. They notice that query performance has degraded over time. Which maintenance operation should be performed to improve performance?

Accepted Answer

Run the VACUUM command. The VACUUM command in Amazon Redshift reclaims space from deleted rows and re-sorts rows to restore the sort order, which improves query performance by reducing I/O and enabling more efficient scans. Over time, as data is updated or deleted, the physical storage becomes fragmented and sort order degrades, leading to slower queries. Running VACUUM (or VACUUM SORT ONLY, VACUUM DELETE ONLY, etc.) is the recommended maintenance operation to address this degradation.

Answer

Drop and recreate the table

Answer

Run the REINDEX command

Answer

Run the ANALYZE command

Question 8

A data engineer notices that an Amazon Redshift cluster is experiencing slow query performance. The engineer suspects that tables are not properly sorted. Which diagnostic query should the engineer run to identify unsorted rows?

Accepted Answer

SELECT * FROM SVV_TABLE_INFO ORDER BY unsorted DESC;. The `SVV_TABLE_INFO` system view in Amazon Redshift provides metadata about each table, including the `unsorted` column which shows the percentage of unsorted rows. By ordering by `unsorted DESC`, the engineer can quickly identify tables with the highest proportion of unsorted data, which directly impacts query performance due to inefficient zone maps and scan pruning.

Answer

SELECT * FROM PG_CATALOG;

Answer

SELECT * FROM STV_TBL_PERM;

Answer

SELECT * FROM STL_LOAD_ERRORS;

Question 9

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 10

A company uses AWS Glue crawlers to populate the Data Catalog from data in Amazon S3. The crawler fails to update the schema when new columns are added to the CSV files. What is the most likely cause?

Accepted Answer

The crawler uses a custom classifier that defines a fixed schema.. AWS Glue crawlers use classifiers to infer the schema of data in Amazon S3. If a custom classifier is used and it defines a fixed schema, the crawler will not detect new columns because the classifier's schema is static. The crawler relies on the classifier to parse the data and determine the schema; if the classifier does not include the new columns, they will be ignored. Therefore, the most likely cause is that the crawler uses a custom classifier with a fixed schema.

Answer

The S3 bucket has versioning enabled.

Answer

The crawler is configured to only crawl new partitions.

Answer

The IAM role for the crawler lacks permissions to read the new columns.

Question 11

A company has an Amazon RDS for PostgreSQL DB instance with a large table that is frequently updated. The data engineer needs to reduce storage costs by archiving old records that are no longer accessed. The archived records must be retained for 7 years due to compliance requirements. Which solution is MOST cost-effective?

Accepted Answer

Export old records using pg_dump and store in S3 Glacier Deep Archive.. Exporting old records via pg_dump and storing them in S3 Glacier Deep Archive provides the lowest-cost storage for data that must be retained for 7 years but is never accessed. S3 Glacier Deep Archive offers retrieval times of 12–48 hours at a storage cost of approximately $0.00099/GB/month, far cheaper than any RDS storage tier. This approach removes the archived data from the RDS instance, reducing provisioned storage costs while meeting compliance requirements.

Answer

Use RDS native backup and restore to keep a separate backup.

Answer

Enable storage autoscaling on the RDS instance.

Answer

Move old records to a separate table in the same RDS instance.

Question 12

A data engineer is designing a multi-region disaster recovery plan for an Amazon DynamoDB table. The table stores critical user profile data and must have a Recovery Point Objective (RPO) of less than 1 minute and a Recovery Time Objective (RTO) of less than 5 minutes. Which solution meets these requirements?

Accepted Answer

Use DynamoDB global tables to replicate data to another region.. DynamoDB global tables provide active-active multi-region replication with sub-second latency, ensuring that data written in one region is automatically replicated to other regions within seconds. This meets the RPO of less than 1 minute and RTO of less than 5 minutes because the table is already available in the secondary region for immediate reads and writes, with no manual restore or failover steps required.

Answer

Configure DynamoDB Streams and a Lambda function to replicate data to another region.

Answer

Use DynamoDB on-demand backup and restore to another region.

Answer

Enable point-in-time recovery (PITR) and restore to another region.

Question 13

A data pipeline uses AWS Glue to read from an Amazon S3 bucket containing millions of small CSV files (each < 1 MB). The ETL job is slow. Which optimization would most improve performance?

Accepted Answer

Use S3 file grouping to combine small files. AWS Glue (and Spark generally) performs poorly with millions of tiny files because each file requires a separate S3 GET request, metadata operation, and task scheduling overhead. S3 file grouping (via 'groupFiles' and 'groupSize' job parameters) coalesces multiple small files into a single read per Spark partition, drastically reducing the number of S3 operations and task overhead. This directly addresses the root cause of the slowness.

Answer

Write the ETL script using PySpark instead of Scala

Answer

Increase the number of Glue workers

Answer

Use the G.1X worker type for more memory

Question 14

A company uses AWS Glue to run ETL jobs daily. The data engineer wants to reduce costs by optimizing the job configuration. Which two actions will help reduce costs? (Choose TWO.)

Accepted Answer

Use G.1X worker type instead of G.2X. G.1X workers provide 16 GB of memory and 4 vCPUs, while G.2X workers provide 32 GB and 8 vCPUs. For many ETL jobs, especially those with smaller data volumes or less complex transformations, G.1X workers are sufficient, and using them instead of G.2X directly reduces the cost per DPU-hour since AWS Glue pricing is based on DPU capacity. This optimization lowers costs without sacrificing performance if the job is not memory- or CPU-bound.

Answer

Increase the job timeout to 48 hours

Answer

Enable Spark UI logging for debugging

Answer

Increase the number of job retries to handle transient failures

Question 15

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time using custom Python code before being stored in Amazon S3. Which AWS service should be used to perform this transformation?

Accepted Answer

Kinesis Data Analytics for Apache Flink. Amazon EMR with Spark Streaming, which is optimized for large-scale batch and stream processing but is not the simplest or most direct service for this specific use case. Option B is AWS Lambda, which can be used for simple transformations but has limitations on execution time and complexity. Option D is Kinesis Data Firehose with custom data transformation, which supports only built-in transformations or Lambda functions, not arbitrary custom Python code directly. Option C, Kinesis Data Analytics for Apache Flink, is correct because it allows running custom Apache Flink applications, which support custom Python code via the Apache Flink Python API, for real-time data transformation.

Answer

Amazon EMR with Spark Streaming

Answer

AWS Lambda function triggered by Kinesis Data Streams

Answer

Kinesis Data Firehose with custom data transformation