DEA-C01 Mock Exam 76 — 25 Questions

Question 1

A data engineering team is designing a data ingestion pipeline for a social media analytics platform. The pipeline must handle up to 100,000 events per second with less than 1 second processing latency. Which TWO services should be used together to meet these requirements?

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

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Streams (Option E) is designed for real-time data ingestion at scale, supporting up to 1,000 records per second per shard with sub-second latency, making it suitable for 100,000 events per second when provisioned with sufficient shards. Amazon Kinesis Data Analytics for Apache Flink (Option D) can consume data directly from Kinesis Data Streams and perform low-latency stream processing (e.g., aggregations, filtering) with exactly-once semantics, meeting the <1 second processing latency requirement. Together, they form a fully managed, scalable pipeline for high-throughput, low-latency streaming analytics.

Answer

AWS Glue streaming ETL

Answer

Amazon Kinesis Data Firehose

Answer

Amazon SQS

Question 2

Which TWO are benefits of using Amazon S3 Object Lock? (Choose TWO.)

Accepted Answer

Helps meet regulatory requirements for write-once-read-many (WORM) storage.. Amazon S3 Object Lock helps meet regulatory requirements for write-once-read-many (WORM) storage by allowing you to set retention periods and legal holds on objects. This ensures that data cannot be deleted or overwritten for a specified duration, which is a common requirement for compliance frameworks such as SEC Rule 17a-4 or FINRA.

Answer

Encrypts objects at rest using AWS KMS.

Answer

Automatically transitions objects to lower-cost storage classes.

Answer

Enables automatic versioning of objects.

Question 3

A company uses AWS Lake Formation to manage data lakes on Amazon S3. The data engineer needs to grant a data analyst access to query specific columns in a table using Amazon Athena, but deny access to columns containing personally identifiable information (PII). Which Lake Formation feature should be used?

Accepted Answer

Column-level permissions in Lake Formation.. Lake Formation column-level permissions allow granting access to specific columns and denying access to others. This is the correct feature for restricting access to PII columns while allowing querying other columns. Row-level security (A) controls which rows are visible, not columns. Tag-based access control (C) is for resource categorization, not fine-grained column access. Cell-level security (D) is not a feature of Lake Formation or AWS Glue.

Answer

Row-level security filters.

Answer

Tag-based access control with Lake Formation tags.

Answer

Cell-level security with AWS Glue.

Question 4

A data engineer is troubleshooting an AWS Glue ETL job that fails with a memory error when processing a large dataset. Which approach can help reduce memory usage?

Accepted Answer

Increase the number of workers for the job. Increasing the number of workers distributes the workload across more resources, reducing memory pressure per worker. Option A is incorrect because using only one worker reduces parallelism and increases memory consumption per worker, worsening the issue. Option B is incorrect because reducing partitions increases the size of each partition, leading to higher memory usage per task. Option D is incorrect because although increasing the worker type to G.2X provides more memory per worker, it does not increase parallelism and may be less cost-effective than increasing the number of workers.

Answer

Set the job to use only one worker

Answer

Reduce the number of partitions in the data source

Answer

Increase the worker type to G.2X

Question 5

A company uses AWS Glue to transform data in Amazon S3. The transformation logic is complex and involves multiple steps. The data engineer wants to implement a workflow that handles dependencies and retries on failure. Which AWS service should be used to orchestrate the Glue jobs?

Accepted Answer

AWS Step Functions. AWS Step Functions is the best choice for orchestrating Glue jobs with dependencies and retries.

Answer

AWS Lambda

Answer

Amazon Managed Workflows for Apache Airflow (MWAA)

Answer

Amazon CloudWatch Events

Question 6

A data engineering team is using AWS DMS to migrate a 2 TB Oracle database to Amazon RDS for PostgreSQL. The migration must have minimal downtime and needs to capture ongoing changes after the full load. Which THREE resources are required for this task? (Choose three.)

Accepted Answer

A DMS source endpoint configured for Oracle.. A source endpoint for Oracle is required to connect to the source Oracle database. Option B is correct because an AWS DMS replication instance is needed to run the migration tasks. Option E is correct because a target endpoint for Amazon RDS PostgreSQL is required. Option C is incorrect because an AWS Snowball Edge device is used for offline data transfer, not for DMS ongoing replication. Option D is incorrect because an Amazon S3 bucket is not required for a direct DMS migration; it would be used only if staging in S3.

Answer

An AWS Snowball Edge device for initial data transfer.

Answer

An Amazon S3 bucket for staging the data.

Question 7

A data engineer is designing a data ingestion pipeline that uses AWS DMS to migrate data from an on-premises Oracle database to Amazon S3 in Parquet format. The engineer needs to ensure that data is continuously replicated with minimal latency. Which THREE steps should the engineer take? (Choose three.)

Accepted Answer

Configure a DMS task with a transformation rule to convert to Parquet.. Options A, B, and D are correct. To continuously replicate data with minimal latency from Oracle to Amazon S3 in Parquet format, the engineer should: (A) configure a DMS task with a transformation rule to convert to Parquet, (B) specify an S3 bucket as the target endpoint with data format set to Parquet, and (D) enable change data capture (CDC) on the source database. Option C (using AWS SCT) is incorrect because SCT is for schema conversion, not for DMS replication. Option E (full load only) is incorrect because it does not provide continuous replication.

Answer

Use AWS Schema Conversion Tool (SCT) to convert the schema.

Answer

Perform a full load only, without CDC.

Question 8

A data engineer needs to ingest streaming data from thousands of devices sending JSON messages via HTTP POST. The data should be stored in Amazon S3 with minimal latency and also be available for real-time analytics. Which combination of services is MOST appropriate?

Accepted Answer

Amazon API Gateway, Amazon Kinesis Data Streams, and Kinesis Data Firehose.. It combines Amazon API Gateway to ingest HTTP POST messages, Amazon Kinesis Data Streams for real-time analytics, and Kinesis Data Firehose to deliver the data to Amazon S3 with minimal latency. Option A (DynamoDB with Streams) is meant for database change tracking, not direct HTTP streaming. Option B (SQS + Lambda) introduces latency and lacks native streaming analytics. Option C (Lambda directly via API Gateway) does not provide the buffering and streaming analytics capabilities needed.

Answer

Amazon DynamoDB with DynamoDB Streams and Lambda.

Answer

Amazon SQS and AWS Lambda to write to S3.

Answer

AWS Lambda directly writing to S3 via API Gateway.

Question 9

A company uses Amazon Redshift for data warehousing. They notice that queries are running slowly, and the STL_LOAD_ERRORS table shows many 'Parse error' entries. The data is loaded from Amazon S3 using COPY commands. What is the MOST likely cause of the parse errors?

Accepted Answer

The source data files have a different schema or delimiter than what is specified in the COPY command.. Parse errors during COPY typically indicate that the source data does not match the target table schema (e.g., data type mismatch or delimiter issues). Option B is incorrect because insufficient compute nodes would cause performance degradation, not parse errors. Option C is incorrect because IAM permission issues would cause access denied errors, not parse errors. Option D is incorrect because unsupported compression formats would cause decompression errors, not parse errors.

Answer

The Redshift cluster does not have enough compute nodes to process the data.

Answer

The IAM role used by Redshift does not have permission to decrypt the S3 objects.

Answer

The source data files are compressed using an unsupported compression format.

Question 10

Refer to the exhibit. A data engineer runs the command on an Amazon S3 bucket used for data lake storage. The engineer is concerned about accidental overwrites of objects. What does the output indicate?

Accepted Answer

Versioning is enabled, so previous versions of objects are preserved.. The Status 'Enabled' indicates versioning is turned on for the bucket, preserving previous versions. Option B is wrong because versioning does not automatically delete old versions; they are retained until explicitly deleted. Option C is wrong because versioning does not enable encryption; encryption is a separate setting. Option D is wrong because MFA Delete is not displayed in this status output; it is a different bucket property.

Answer

Old versions will be automatically deleted after a retention period.

Answer

Objects are encrypted at rest by default.

Answer

MFA Delete is disabled, meaning anyone can delete objects permanently.

Question 11

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 re-sorts rows according to the sort key and reclaims disk space from deleted rows, which can improve query performance. Option B is incorrect because dropping and recreating the table is a heavy operation that requires redefining the table and reloading data; it is not a standard maintenance operation for performance. Option C is incorrect because Redshift uses sort keys and distribution keys instead of indexes; there is no REINDEX command in Redshift. Option D is incorrect because the ANALYZE command updates table statistics to help the query optimizer, but it does not physically reorganize the data; thus it may help with query planning but does not directly improve performance from data fragmentation.

Answer

Drop and recreate the table

Answer

Run the REINDEX command

Answer

Run the ANALYZE command

Question 12

A data engineer is tasked with designing a disaster recovery solution for a data lake stored in Amazon S3. The data lake contains sensitive customer data that must be replicated to a different AWS Region. The engineer needs to ensure that all objects, including those with encryption using SSE-KMS, are replicated. Which solution meets the requirements?

Accepted Answer

Enable S3 Cross-Region Replication (CRR) with the appropriate KMS key and IAM role.. S3 Cross-Region Replication (CRR) can replicate objects encrypted with SSE-KMS if the appropriate KMS key is configured and the IAM role has the necessary permissions for encryption operations. Option A (S3 Batch Operations) is designed for one-time bulk actions, not ongoing replication. Option C (S3 Transfer Acceleration) only speeds up data transfer but does not provide replication. Option D (AWS CLI s3 sync) is a manual, scheduled copy process and does not offer automatic, continuous replication.

Answer

Use S3 Batch Operations to copy objects to the destination bucket.

Answer

Use S3 Transfer Acceleration to copy objects across regions.

Answer

Use the AWS CLI s3 sync command scheduled in a cron job.

Question 13

A data engineer is designing a disaster recovery strategy for an Amazon RDS for PostgreSQL database that is used in a data pipeline. The database must have a Recovery Point Objective (RPO) of less than 1 minute and a Recovery Time Objective (RTO) of less than 5 minutes. Which TWO actions should the engineer take?

Accepted Answer

Enable automated backups with point-in-time recovery.. Options B and C are correct. Multi-AZ deployment with a standby instance enables automatic failover, typically achieving an RTO of under 1-2 minutes. Combined with automated backups and point-in-time recovery, which allow database restoration to any point within seconds (RPO of less than 1 minute), the requirements are met. Option A is incorrect because manual snapshots are not frequent enough to guarantee RPO under 1 minute and restoring from a snapshot takes longer than 5 minutes. Option D is incorrect because a read replica in a different Availability Zone is not designed for automatic failover; it is for read scaling and requires manual promotion. Option E is incorrect because Amazon Aurora Global Database is a different service; this question is about Amazon RDS for PostgreSQL, and cross-Region replication for RDS does not provide automatic failover and typically has higher RTO.

Answer

Take frequent manual snapshots and copy them to another Region.

Answer

Create a read replica in a different Availability Zone.

Answer

Use cross-Region replication with Amazon Aurora Global Database.

Question 14

A company uses Amazon EMR to run Spark jobs on data stored in Amazon S3. The data engineer notices that the jobs are running slower than expected. The engineer suspects that the S3 storage class might be affecting performance. Which THREE factors can impact read performance from S3? (Choose three.)

Accepted Answer

Use of S3 Select to retrieve only a subset of data.. Options B, D, and E are correct. B: S3 Select can retrieve only a subset of data, reducing the amount scanned and improving read performance. D: Larger average object sizes allow better throughput by leveraging parallel requests and reducing overhead. E: Storing data in compressed formats reduces the amount of data transferred, improving read performance. Option A is incorrect because S3 Transfer Acceleration is designed to improve upload speed over long distances, not read performance. Option C is incorrect because S3 Object Lock prevents object deletion or overwrite but does not affect read performance.

Answer

Use of S3 Transfer Acceleration.

Answer

Use of S3 Object Lock.

Question 15

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by an AWS Lambda function that processes records and stores results in Amazon DynamoDB. Recently, the Lambda function has been failing with ProvisionedThroughputExceededException errors. Which action should the data engineer take to resolve this issue?

Accepted Answer

Enable auto scaling on the DynamoDB table to handle increased write capacity.. Enabling DynamoDB auto scaling dynamically adjusts throughput to match demand. Option B is wrong because reducing the number of shards would lower the ingestion rate, which is not appropriate for handling high traffic. Option C is wrong because increasing the batch size in the Lambda event source mapping would process more records per invocation, but the underlying issue is DynamoDB throughput, not batch size. Option D is wrong because discarding records that cause throttling would lead to data loss.

Answer

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

Answer

Increase the batch size in the Lambda event source mapping to process more records per invocation.

Answer

Configure the Lambda function to discard records that cause throttling errors.

Question 16

A company runs an Amazon EMR cluster with Spark jobs. One job fails with 'Container killed by YARN for exceeding memory limits'. The data engineer has already increased the executor memory. What is the NEXT best step to resolve the issue?

Accepted Answer

Increase the YARN container memory allocation (yarn.nodemanager.resource.memory-mb).. Increasing the yarn.nodemanager.resource.memory-mb allows YARN to allocate larger containers, preventing the kill. Option A: Setting spark.executor.memoryOverhead increases off-heap memory but may still exceed YARN limits if they are not also raised. Option C: Reducing partitions decreases parallelism and may reduce memory per executor, but does not address the root cause of insufficient container memory. Option D: Increasing driver memory is not relevant because the issue is with executors.

Answer

Set spark.executor.memoryOverhead to a higher value.

Answer

Decrease the number of Spark partitions.

Answer

Increase the driver memory.

Question 17

A company runs a data lake on Amazon S3 with AWS Glue and Amazon Athena. The data engineer notices that queries are slow and scanning large amounts of data. Which THREE actions should the engineer take to optimize query performance and reduce costs?

Accepted Answer

Compress data files using gzip or snappy.. The correct actions to optimize query performance and reduce costs are C, D, and E. Compressing data (C) reduces the amount of data scanned, lowering costs and improving I/O. Partitioning (D) by frequently filtered columns (e.g., date, region) allows Athena to prune partitions, scanning only relevant data. Using columnar formats like Parquet or ORC (E) improves compression and enables column pruning, reducing scan size and improving performance. Option A (increasing query timeout) does not reduce data scanned or improve performance; it only allows queries to run longer before failing. Option B (increasing DPUs in a Glue job) is unrelated to Athena query performance; DPUs are for Glue ETL jobs, not Athena queries.

Answer

Increase the query timeout in Athena.

Answer

Increase the number of DPUs in the Glue job.

Question 18

A data engineer needs to grant a data scientist access to query a Glue Data Catalog database but must prevent the data scientist from seeing the underlying S3 data locations. Which approach should be used?

Accepted Answer

Use AWS Lake Formation to grant SELECT permission on the database and tables without granting S3 access. Lake Formation can be used to grant SELECT permission on the database and tables, and by using column-level and row-level filters, but to hide S3 locations, the data scientist should not have direct S3 access. Lake Formation does not require the user to see the S3 path. Granting IAM read-only access to S3 would expose locations. Using a VPC endpoint does not hide locations. Glue resource policies cannot hide S3 locations.

Answer

Use a Glue resource policy to restrict access to the database

Answer

Grant the data scientist IAM permissions to access the Glue Data Catalog and the underlying S3 data

Answer

Create a VPC endpoint for Glue and S3 to restrict network access

Question 19

A company receives streaming clickstream data from its website. The data must be ingested with low latency and transformed in real time before being stored in Amazon S3. Which AWS service combination is most suitable for this use case?

Accepted Answer

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics. Amazon Kinesis Data Streams ingests streaming clickstream data with low latency, and Amazon Kinesis Data Analytics performs real-time transformations using SQL or Apache Flink. The processed data can then be stored in Amazon S3 via a Kinesis Data Firehose delivery stream, meeting the requirement for low-latency ingestion and real-time transformation.

Answer

Amazon S3 with S3 Object Lambda

Answer

Amazon Kinesis Data Firehose with AWS Lambda for transformation

Answer

AWS Glue jobs triggered by Amazon S3 events

Question 20

A data engineer needs to grant an IAM user access to query a specific table in Amazon Athena, but the user should not be able to view other tables in the same database. Which method should the engineer use?

Accepted Answer

Use AWS Lake Formation to grant SELECT permission on the specific table to the user. Lake Formation provides fine-grained table-level permissions. Option A is wrong because IAM policies alone cannot restrict access to a specific table in Athena without Lake Formation. Option C is wrong because S3 bucket policies do not control Athena table access. Option D is wrong because Workgroup policies do not provide table-level security.

Answer

Attach an IAM policy that allows athena:StartQueryExecution and restrict the query by table name

Answer

Apply an S3 bucket policy that restricts access to the table's underlying data

Answer

Create a separate Athena workgroup with a query limit that only allows queries on that table