DEA-C01 Practice Exam 57 — 20 Questions

Question 1

A company uses AWS Database Migration Service (DMS) to migrate an on-premises PostgreSQL database to Amazon RDS for PostgreSQL. The migration is ongoing and uses change data capture (CDC). The engineer notices that the target database is falling behind the source by several hours. What is the MOST likely cause?

Accepted Answer

A large full load is being transferred, which delays the start of CDC.. Option B is correct because a large full load can delay the start of CDC, causing the target to fall behind. Option A is incorrect because increasing the replication instance size typically improves performance. Option C is wrong because CDC requires an active transaction log; disabling it would stop replication. Option D is wrong because parallel apply can actually speed up replication.

Answer

The target table has disabled parallel apply.

Answer

The replication instance is under-provisioned and needs to be larger.

Answer

Change data capture has been disabled on the source database.

Question 2

A company uses Amazon Kinesis Data Firehose to deliver log data to Amazon S3. The data is transformed by a Lambda function that adds a timestamp field. Recently, the Firehose delivery stream has been failing with 'Lambda invocation failed' errors. The Lambda function's CloudWatch Logs show that the function is timing out. What is the MOST likely cause?

Accepted Answer

The Firehose buffer size is too large, causing the Lambda function to receive too many records.. Option D is correct because Firehose sends batches of records to Lambda; if the batch size is too large, the function may exceed its timeout. Option A is wrong because the Lambda function is being invoked, so permissions are fine. Option B is wrong because the error is about Lambda invocation, not memory. Option C is wrong because a 1-minute timeout is typical and may be insufficient for large batches.

Answer

The Lambda function lacks permission to write to CloudWatch Logs.

Answer

The Lambda function timeout is set to 1 minute, which is adequate.

Answer

The Lambda function is running out of memory.

Question 3

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.. Option C is correct because API Gateway ingests HTTP POST, sends to Kinesis Data Streams for real-time consumption, and Firehose delivers to S3. Option A (SQS) is not ideal for real-time streaming. Option B (DynamoDB Streams) is for database changes. Option D (Lambda + S3) lacks real-time analytics capability.

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 4

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that sends data to an Amazon S3 bucket. The delivery stream has a buffer size of 5 MB and a buffer interval of 60 seconds. The data ingestion rate is 2 MB per second. The engineer notices that S3 objects are created every 60 seconds but each object is only about 2 MB. What should the engineer do to reduce the number of small S3 objects?

Accepted Answer

Increase the buffer size to 10 MB.. Option A is correct because increasing the buffer size to 10 MB will allow the stream to buffer more data before writing to S3, resulting in larger objects. Option B is wrong because decreasing the buffer interval would create objects more frequently, making the problem worse. Option C is wrong because switching to Kinesis Data Streams does not solve the buffering issue. Option D is wrong because reducing the buffer size would create even smaller objects.

Answer

Decrease the buffer interval to 30 seconds.

Answer

Reduce the buffer size to 2 MB.

Answer

Switch to Kinesis Data Streams and use a Lambda function to write to S3.

Question 5

A data engineer needs to ingest data from an on-premises Oracle database into Amazon S3 for analytics. The data changes frequently and the engineer wants to capture both initial load and incremental changes with minimal latency. Which TWO AWS services should be used together? (Choose TWO.)

Accepted Answer

AWS Database Migration Service (DMS). Option B and Option D are correct. AWS DMS can continuously replicate changes from Oracle to S3, and Amazon Kinesis Data Streams can provide low-latency streaming for near-real-time data. Option A is for batch file transfer, not real-time. Option C is for ETL, not replication. Option E is for event-driven processing but not direct database replication.

Answer

AWS Lambda

Answer

AWS Glue

Answer

AWS Transfer Family

Question 6

A company is using Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data includes a timestamp field. They want to partition the S3 objects by hour dynamically. The Firehose delivery stream is configured with a prefix like 'year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/hour=!{timestamp:HH}/'. However, the objects are not being partitioned as expected; all files end up in a single partition. What is the MOST likely cause?

Accepted Answer

Dynamic partitioning is not enabled in the Firehose delivery stream configuration.. Option D is correct because dynamic partitioning requires a separate configuration (enabling dynamic partitioning and specifying the partitioning keys). The prefix with timestamp expressions is used for S3 prefix customization but not for dynamic partitioning. Option A (time zone) could cause wrong hour but not single partition. Option B (buffer size) affects file size. Option C (IAM) would cause errors, not mispartitioning. The key is that dynamic partitioning must be explicitly enabled.

Answer

The buffer size is set too large, delaying file delivery.

Answer

The timestamp is in UTC but the prefix uses local time.

Answer

The IAM role for Firehose lacks permissions to write to S3 with dynamic prefixes.

Question 7

An e-commerce company is building a near-real-time dashboard to monitor customer clickstream data. The data is ingested via Amazon Kinesis Data Streams, transformed using AWS Lambda, and stored in Amazon S3. The team needs to query the data using Amazon Athena. Which THREE steps should be taken to optimize cost and performance? (Choose three.)

Accepted Answer

Convert the data to Apache Parquet or ORC format.. B is correct because partitioning by date reduces the amount of data scanned by Athena. C is correct because converting data to Parquet or ORC reduces storage size and improves query performance. E is correct because compressing data reduces storage costs and scanning. A is wrong because JSON is not optimal; columnar formats are better. D is wrong because Glue Data Catalog is needed for Athena, but it is not an optimization step; it's a prerequisite.

Answer

Use AWS Glue Data Catalog to store the table metadata.

Answer

Store the data in JSON format for flexibility.

Question 8

A data engineer is designing a data ingestion pipeline to load clickstream data from an Amazon S3 bucket into an Amazon Redshift cluster. The data arrives in 5-minute batches. Which TWO actions should the engineer take to ensure data consistency and avoid duplicates? (Select TWO.)

Accepted Answer

Use the STL_LOAD_ERRORS system table to monitor and resolve load errors.. Options B and D are correct. Using the STL_LOAD_ERRORS table helps identify errors, and using a staging table with a MERGE/upsert pattern ensures idempotent loads. Option A (disabling WLM) is unrelated. Option C (loading into a single slice) reduces performance. Option E (using SORTKEY) improves query performance but not consistency.

Answer

Define a SORTKEY on the target table to improve deduplication.

Answer

Disable workload management (WLM) to maximize resources.

Answer

Load data into a single slice to maintain order.

Question 9

A company uses AWS Glue to process data from Amazon S3. The Glue job fails with a 'SchemaDetectionException'. The data engineer wants to ensure the schema is correctly inferred. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Use the Glue Data Catalog as the source for schema definition.. Options A and D are correct. Option A ensures the job reads all files for schema inference. Option D uses the Glue Data Catalog to store a consistent schema. Option B is wrong because adding a separate column does not help schema detection. Option C is wrong because converting to Parquet may change the schema but does not guarantee correct inference. Option E is wrong because increasing parallelism does not affect schema detection.

Answer

Add a column with a default value to the data.

Answer

Increase the number of Glue DPUs to speed up processing.

Answer

Convert all input files to Parquet format.

Question 10

A company is ingesting real-time financial transactions into Amazon Kinesis Data Streams. The data is then consumed by a Kinesis Data Analytics for Apache Flink application that calculates running totals. The application is experiencing high latency and checkpoint failures. Which TWO steps should the engineer take to improve performance and reliability? (Select TWO.)

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Options B and D are correct. Increasing the number of shards increases throughput and parallelism, helping reduce latency. Increasing the number of KPUs (Kinesis Processing Units) for the Flink application provides more compute resources, addressing checkpoint failures. Option A (decreasing checkpoint interval) may increase checkpoint overhead. Option C (using Fan-Out) is for multiple consumers, not for a single Flink job. Option E (reducing batch size) may not help with overall throughput.

Answer

Enable enhanced fan-out for the Flink application.

Answer

Reduce the batch size of records processed per checkpoint.

Answer

Decrease the checkpoint interval to reduce state size.

Question 11

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS to Amazon S3. The job runs successfully but the data in S3 is missing some records that exist in the source. The engineer notices that the job uses a JDBC connection and the query extracts data based on a timestamp column. What is the MOST likely cause of the missing records?

Accepted Answer

The timestamp column includes time portion and the job is using an exclusive upper bound.. Option B is correct because if the timestamp column includes time, records with the same timestamp may be missed if the job uses an exclusive upper bound. Option A is wrong because the job can read from different tables. Option C is wrong because the S3 bucket permissions would cause failure, not missing records. Option D is wrong because connection pooling does not cause missing records.

Answer

The S3 bucket lacks write permissions.

Answer

The JDBC connection uses connection pooling, causing some records to be dropped.

Answer

The Glue job is configured to only read from one table.

Question 12

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, C, and E are correct. Option A enables continuous replication using CDC. Option C uses a transformation to convert to Parquet. Option E uses a target endpoint for S3 with parquet format. Option B is wrong because SCT is for schema conversion, not for DMS replication. Option D is wrong because a full load only is not continuous replication.

Answer

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

Answer

Perform a full load only, without CDC.

Question 13

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 is correct because a DMS replication instance is needed to run the migration tasks. B is correct because a source endpoint for Oracle is required. E is correct because a target endpoint for RDS PostgreSQL is required. C is wrong because an S3 bucket is not required unless using S3 as a target. D is wrong because a Snowball device is for large offline transfers, not for DMS.

Answer

An AWS Snowball Edge device for initial data transfer.

Answer

An Amazon S3 bucket for staging the data.

Question 14

A company is using AWS Glue to catalog data in Amazon S3. The data is in CSV format with varying schemas. The Data Engineering team wants to ensure the Glue Data Catalog is updated automatically when new partitions are added to S3. Which TWO actions should be taken? (Choose two.)

Accepted Answer

Enable partition indexing on the Glue Data Catalog.. A is correct because enabling partition indexing in the Glue Data Catalog allows partition pruning and automatic updates. C is correct because configuring a Glue crawler with a schedule will automatically discover new partitions. B is wrong because setting up an S3 event notification to trigger Lambda for manual updates is not as efficient as crawler scheduling. D is wrong because using AWS Glue ETL jobs to update the catalog is not automatic. E is wrong because Amazon Athena does not update the catalog.

Answer

Set up an S3 event notification to trigger a Lambda function that updates the Glue Data Catalog.

Answer

Run Amazon Athena queries with MSCK REPAIR TABLE to add partitions.

Answer

Use AWS Glue ETL jobs to write data and update the catalog simultaneously.

Question 15

A company is migrating on-premises Apache Kafka clusters to Amazon MSK. The migration must be seamless with no data loss. The team is using MirrorMaker 2 to replicate data from on-premises Kafka to MSK. Which THREE configurations are necessary to ensure exactly-once semantics and minimal downtime? (Choose three.)

Accepted Answer

Set offsets.topic.replication.factor to 3 for the consumer offsets topic.. A is correct because enabling replication.factor ensures data durability in MSK. C is correct because enabling exactly-once semantics in MirrorMaker prevents duplicates. E is correct because setting offsets.topic.replication.factor ensures consumer offsets are replicated. B is wrong because auto.create.topics.enable should be true to allow topic creation. D is wrong because SSL is optional and not required for exactly-once.

Answer

Set auto.create.topics.enable to false to prevent automatic topic creation.

Answer

Enable TLS encryption between on-premises and MSK.

Question 16

A data engineer is running a Spark job on Amazon EMR. The job reads from S3, processes data, and writes to S3. The job is taking longer than expected. The engineer notices that the job is spending a lot of time in the 'GC' (garbage collection) phase. Which configuration change is most likely to improve performance?

Accepted Answer

Increase the spark.executor.memory setting.. Option A is correct because increasing executor memory reduces GC frequency. Option B is wrong because it reduces parallelism. Option C is wrong because it reduces memory per task. Option D is wrong because it reduces memory and may increase GC.

Answer

Increase the spark.sql.shuffle.partitions.

Answer

Decrease the number of executor cores.

Answer

Decrease the spark.executor.memoryOverhead.

Question 17

A company needs to tag all resources created in a specific AWS account to enforce data governance policies. Which AWS service can automatically enforce tagging rules?

Accepted Answer

AWS Organizations SCPs. Option B is correct. AWS Organizations service control policies (SCPs) can enforce tagging across accounts. Option A is wrong because Resource Access Manager is for sharing resources. Option C is wrong because Systems Manager is for management. Option D is wrong because Service Catalog is for IT services.

Answer

AWS Service Catalog

Answer

AWS Resource Access Manager

Answer

AWS Systems Manager

Question 18

A data engineer is troubleshooting an AWS Glue crawler that is not correctly inferring the schema of CSV files stored in Amazon S3. The files have headers, but the crawler is treating the header row as data. The crawler is configured with a custom classifier that has a CSV classifier with 'Column header' set to 'Use first row as header'. What is the most likely reason the crawler is not recognizing the header?

Accepted Answer

The CSV classifier's 'Quote symbol' setting does not match the files.. The CSV classifier may not be applied if the file does not match the classifier's 'Quote symbol' or 'Allow single column' settings. Option D is correct because if the classifier expects a quote symbol but the file has none, it may not match. Option A is wrong because the classifier is set to use first row as header. Option B is wrong because the crawler does not require the header to be uppercase. Option C is wrong because the number of columns is not a header recognition issue.

Answer

The CSV files have a varying number of columns across rows.

Answer

The CSV files have a different delimiter than the default comma.

Answer

The header row contains uppercase letters.

Question 19

A data engineer is monitoring an AWS Glue ETL job that processes data from an S3 bucket and writes to a Redshift table. The job completes successfully but takes longer than expected. The engineer notices that the job uses 10 DPUs and the data size is 500 GB. The job runs in standard mode. Which change would MOST reduce job duration?

Accepted Answer

Increase the number of DPUs to 20.. Option D is correct because increasing DPUs allows more parallelism if the job is distributed. Option A is wrong because reducing the number of partitions may increase data skew. Option B is wrong because using a smaller instance type reduces resources. Option C is wrong because converting to CSV increases file size and processing time.

Answer

Use a smaller worker type like G.1X.

Answer

Change the output format from Parquet to CSV.

Answer

Reduce the number of partitions in the data.

Question 20

A data engineer is monitoring an Amazon Redshift cluster and notices that queries are taking longer than expected. The engineer checks the system tables and sees that many queries are waiting for 'WLM' resources. What is the most likely cause and recommended fix?

Accepted Answer

The WLM queue concurrency is set too low; increase the concurrency level.. Option D is correct because WLM queue wait indicates concurrency throttling. Option A is wrong because disk space is unrelated. Option B is wrong because sort keys improve scan efficiency, not concurrency. Option C is wrong because distribution style affects data movement, not queue wait.

Answer

The table sort keys are poorly designed; recreate tables with better sort keys.

Answer

The distribution style is set to ALL; change to KEY distribution.

Answer

The cluster is running low on disk space; resize the cluster.