DEA-C01 Practice Test 34 — 15 Questions

Question 1

Refer to the exhibit. A CloudFormation stack outputs the Glue job name and S3 bucket names. The Glue job transforms CSV files from the raw bucket to Parquet in the processed bucket. However, the Glue job is failing with an error that it cannot write to the processed bucket. What is the most likely cause?

Accepted Answer

The Glue job does not have permission to write to the processed bucket. The Glue job's IAM role likely does not have s3:PutObject permission on the processed bucket. The CloudFormation outputs show the bucket names, but the role may only have read access to raw. The error is write-related, not read.

Answer

The raw data bucket is in a different region

Answer

The Glue job is not using the correct worker type

Answer

The Glue job is using an incorrect file format

Question 2

A company is building a data lake on S3 and needs to ingest data from on-premises Oracle database. The data is 5 TB and changes incrementally. The ingestion must capture changes in near real-time (less than 1 minute latency) and be cost-effective. Which approach should be used?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication to S3. Option B (Use AWS DMS with ongoing replication from Oracle to S3) is correct because DMS supports change data capture (CDC) with low latency. Option A (Full export using AWS Glue) does not capture incremental changes in near real-time. Option C (Use Kinesis Data Firehose with Oracle as source) is not directly possible without a connector. Option D (Use AWS DataSync) is for file transfers, not database CDC.

Answer

Use Amazon Kinesis Data Firehose with an Oracle JDBC connector

Answer

Use AWS Glue to perform a full table export daily

Answer

Use AWS DataSync to sync the Oracle data files to S3

Question 3

A data engineer runs a weekly AWS Glue ETL job that processes data from Amazon DynamoDB to Amazon S3. The job reads the entire table every time, which is slow and expensive. The job needs to process only items that changed since the last run. Which solution should the engineer implement?

Accepted Answer

Enable DynamoDB Streams and process change events with AWS Lambda to write to S3. Option D is correct because DynamoDB Streams captures changes (inserts, updates, deletes) and can be read by AWS Lambda to write to S3, enabling incremental processing. Option A (Scan with LastEvaluatedKey) still scans entire table. Option B (GSI) does not capture changes automatically. Option C (DMS) is overkill and not as seamless with DynamoDB Streams.

Answer

Use DynamoDB Scan with a LastEvaluatedKey to paginate and store the last scanned key to resume next time

Answer

Add a Global Secondary Index (GSI) on a timestamp attribute and query only new records

Answer

Use AWS Database Migration Service (DMS) with ongoing replication from DynamoDB to S3

Question 4

A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job fails intermittently with a 'MemoryError'. What is the MOST likely cause?

Accepted Answer

The Glue job worker type is too small for the data volume. The error suggests the job runs out of memory. Increasing the DPU count can allocate more memory per worker and help process larger data volumes without memory errors.

Answer

The Glue job uses too many DynamicFrames

Answer

The S3 output bucket is in a different region

Answer

The Kinesis stream has insufficient shards

Question 5

Which TWO AWS services can be used to transform data in transit during ingestion? (Choose 2.)

Accepted Answer

Amazon Kinesis Data Firehose with Lambda transformation. AWS Glue ETL can transform data before writing to target. Kinesis Data Firehose can invoke Lambda for transformation. Both transform data in transit.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Athena

Answer

AWS Data Pipeline

Question 6

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS to Amazon S3. The jobs run nightly and take 3 hours to complete. The data volume is growing by 20% each month. The engineer needs to reduce job runtime and cost. The source RDS is a db.r5.large instance. Which approach would be MOST effective?

Accepted Answer

Increase the number of Glue workers and choose a G.1X or G.2X worker type.. Option A is correct because increasing the number of Glue workers (DPUs) directly increases parallelism and reduces runtime, and you can choose a worker type with more memory if needed. Option B is wrong because RDS read replica does not help Glue processing speed. Option C is wrong because S3 Transfer Acceleration is for uploads, not Glue processing. Option D is wrong because reducing DPUs would increase runtime.

Answer

Reduce the number of DPUs to lower cost and accept longer runtime.

Answer

Create a read replica of the RDS instance and point the Glue job to the replica.

Answer

Enable S3 Transfer Acceleration on the destination bucket.

Question 7

A company uses AWS Glue for ETL jobs. The data engineer needs to ensure that the Glue job can access an S3 bucket in another account. What is the recommended approach?

Accepted Answer

Assign an IAM role to the Glue job with permissions to access the bucket, and configure the bucket policy to allow the role. Option A is correct because the Glue job's IAM role must have permissions to access the S3 bucket, and the bucket policy must allow the role. Option B is wrong because S3 bucket policies can allow cross-account access without requiring an IAM role in the other account. Option C is wrong because resource-based policies alone are not sufficient; the Glue job needs an IAM role with permissions. Option D is wrong because Glue jobs cannot use access keys; they use IAM roles.

Answer

Create an IAM role in the target account and have the Glue job assume that role

Answer

Configure the S3 bucket policy to allow the Glue job's IAM role and also set the Glue job's resource-based policy

Answer

Store AWS access keys for the target account in AWS Secrets Manager and have the Glue job retrieve them

Question 8

A company uses AWS Glue ETL jobs to transform data in S3. The job runs successfully but takes longer than expected. The data is in Parquet format and partitioned by date. Which change would most improve performance without increasing cost?

Accepted Answer

Enable pushdown predicates to filter partitions early.. Option B is correct because enabling pushdown predicates filters partitions at the source, reducing data scanned. Option A is wrong because increasing DPUs increases cost. Option C is wrong because converting to CSV increases data size and scan time. Option D is wrong because repartitioning may cause shuffling overhead.

Answer

Repartition the data by a different column.

Answer

Convert Parquet to CSV for faster serialization.

Answer

Increase the number of DPUs for the job.

Question 9

A data engineer is troubleshooting an AWS Glue job that reads from Amazon RDS MySQL and writes to Amazon S3. The job runs successfully but takes longer than expected. The engineer wants to optimize performance. Which THREE actions would improve job performance?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Options A, B, and D are correct. Increasing DPUs provides more parallelism. Using pushdown predicates reduces data scanned. Using JDBC fetch size reduces round trips. Option C is wrong because using a single connection per partition is the default and may cause contention; increasing connections can help but the default is fine. Option E is wrong because Parquet is already efficient; converting to CSV would make it worse.

Answer

Use a single JDBC connection per partition.

Answer

Convert the output format from Parquet to CSV.

Question 10

A company uses Kinesis Data Streams to ingest IoT data. The data volume varies, and occasionally the shard write throughput is exceeded, causing ProvisionedThroughputExceeded exceptions. The data engineer needs to handle these spikes without losing data. Which approach is most cost-effective and requires minimal code changes?

Accepted Answer

Use Kinesis Data Firehose as a consumer with retries and buffer settings. Option B (Use Kinesis Data Firehose as a consumer with retries and buffer settings) is correct because Firehose can buffer data and retry on failures, handling spikes. Option A (Increase shard count) is costly and requires manual scaling. Option C (Use Kinesis Client Library with exponential backoff) is a good practice but still requires custom code; Firehose is more managed. Option D (Send data to SQS and then to Kinesis) adds complexity and cost.

Answer

Implement custom retry logic using the Kinesis Client Library with exponential backoff

Answer

Increase the number of shards to handle peak throughput

Answer

Send data to an SQS queue first, then have a Lambda function write to Kinesis

Question 11

A data engineer needs to set up a data ingestion pipeline that reads from Amazon MSK (Managed Streaming for Kafka) and writes to Amazon S3 with transformations. The data is in Avro format and must be converted to Parquet. Which THREE components should be used together? (Choose THREE.)

Accepted Answer

AWS Lambda function to convert Avro to Parquet as a Firehose transformation. Options A, B, and D are correct. A: MSK is the source. B: Kinesis Data Firehose can consume from MSK via a custom endpoint and deliver to S3, with Lambda for transformation. D: Lambda can be used as a transformation function within Firehose to convert Avro to Parquet. C: Glue is not directly integrated with MSK as a Firehose source. E: Athena is for querying, not part of the ingestion pipeline.

Answer

Amazon Athena to convert the data format

Answer

AWS Glue ETL job to read from MSK

Question 12

A data engineer needs to design a data ingestion pipeline that captures streaming data from mobile app events into Amazon S3 for analytics. The pipeline must support real-time processing of events and allow for schema evolution over time. Which AWS services should the engineer use? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Analytics. Options A, C, and D are correct. Kinesis Data Streams ingests streaming data. Kinesis Data Analytics can perform real-time processing. Kinesis Data Firehose delivers data to S3. Option B (Glue) is batch-oriented and not real-time. Option E (AppFlow) is for SaaS data ingestion.

Answer

AWS Glue ETL jobs

Answer

AWS AppFlow

Question 13

A company uses AWS Glue DataBrew to clean and transform data for analytics. The source data is in Parquet format in Amazon S3. The transformation includes filtering rows and adding calculated columns. What is the MOST cost-effective way to run these transformations on a schedule?

Accepted Answer

Create a Glue DataBrew recipe and schedule the job using a cron expression. DataBrew jobs can be scheduled using a cron expression. This is the native way to run transformations automatically without additional services.

Answer

Use Amazon EMR with Spark

Answer

Create an AWS Lambda function triggered by S3 events

Answer

Use AWS Glue ETL with PySpark

Question 14

A company is designing a data ingestion pipeline for real-time IoT sensor data. The data volume peaks at 10,000 messages per second. The pipeline must process messages in order per sensor and persist raw data to Amazon S3 for archival. Which TWO services should be used together to meet these requirements? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. Options B and C are correct. Kinesis Data Streams captures data in order per shard (sensor), and Kinesis Data Firehose delivers to S3. Option A is wrong because SQS does not guarantee order per sensor. Option D is wrong because AppFlow is for SaaS integration. Option E is wrong because Lambda can process but not persist to S3 efficiently for archival.

Answer

AWS Lambda

Answer

Amazon AppFlow

Answer

Amazon Simple Queue Service (Amazon SQS)

Question 15

A data engineer is designing a data ingestion pipeline for real-time user activity logs. The logs are generated by a web application and must be ingested into Amazon S3 with minimal latency (under 1 minute). The logs also need to be queried in Amazon Athena. The engineer considers using Amazon Kinesis Data Firehose. Which TWO configurations are required to achieve near-real-time delivery to S3? (Choose TWO.)

Accepted Answer

Set the BufferIntervalInSeconds to 60 seconds.. Options A and D are correct. Set 'BufferIntervalInSeconds' to 60 seconds to flush every minute, and 'BufferSizeInMBs' to 1 MB to ensure small files are delivered quickly. Option B is wrong because enabling S3 compression does not affect delivery frequency. Option C is wrong because converting to Parquet is a transformation, not a delivery trigger. Option E is wrong because enabling error logging does not reduce latency.

Answer

Enable S3 compression (e.g., GZIP) on the delivery stream.

Answer

Enable Amazon CloudWatch error logging for the delivery stream.

Answer

Enable data format conversion to Parquet using AWS Glue.