DEA-C01 Practice Test 18 — 15 Questions

Question 1

A data engineer needs to ingest data from an Amazon RDS MySQL database into a data lake on Amazon S3. The engineer wants to perform an initial full load and then capture incremental changes. Which TWO AWS services can be combined to achieve this?

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

Accepted Answer

Amazon S3. Amazon S3 is the target data lake storage layer, not a service that performs data ingestion or change data capture. However, it is listed as a correct option because the question asks which services can be combined to achieve the goal, and S3 is the essential destination for the data lake. The actual ingestion and CDC are handled by AWS DMS, which writes full load and incremental changes directly to S3 in formats like Parquet or CSV.

Answer

Amazon Kinesis Data Firehose

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AWS Glue

Answer

AWS Data Pipeline

Question 2

A company needs to ingest data from multiple SaaS applications (Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate (~100 GB per day). The pipeline must handle schema changes, deduplicate records, and provide low latency (under 1 hour). Which THREE services should be used? (Choose THREE.)

Accepted Answer

Amazon AppFlow. Amazon AppFlow is the correct choice because it is a fully managed integration service specifically designed to transfer data from SaaS applications like Salesforce and Marketo to AWS services such as Amazon S3. It supports incremental transfers, handles schema changes automatically via its schema evolution feature, and can achieve sub-hour latency for moderate data volumes (~100 GB/day) without custom coding.

Answer

Amazon Kinesis Data Streams

Answer

AWS Database Migration Service (DMS)

Question 3

A data engineer needs to ingest streaming data from thousands of IoT devices into Amazon S3 in near real-time. The data must be processed with minimal latency and stored in a columnar format for analytics. Which service should the engineer use to ingest the data?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is designed to ingest streaming data and deliver it directly to Amazon S3 with near-real-time latency (typically 60 seconds or more, depending on buffer settings). It automatically converts data to columnar formats like Parquet or ORC, which is essential for analytics, and requires no custom code for the delivery pipeline, minimizing operational overhead.

Answer

Amazon Kinesis Data Analytics

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Amazon Simple Queue Service (SQS)

Answer

Amazon Kinesis Data Streams with a Lambda consumer

Question 4

A data engineer needs to ingest data from an Amazon RDS MySQL database into a data lake on Amazon S3. The engineer wants to perform an initial full load and then capture incremental changes. Which TWO AWS services can be combined to achieve this?

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

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

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AWS Lambda with Kafka trigger

Answer

Amazon Kinesis Data Streams

Question 5

A data engineer is ingesting streaming data from an IoT fleet into Amazon S3 using Amazon Kinesis Data Firehose. The data arrives as JSON, but the downstream analytics require Parquet format. Which Firehose transformation should the engineer configure?

Accepted Answer

Configure a Lambda function as a data transformation in Firehose to convert JSON to Parquet.. Amazon Kinesis Data Firehose can invoke an AWS Lambda function as a data transformation step to convert incoming JSON records to Parquet format before delivery to S3. This is the native, serverless way to perform record-level format conversion within the Firehose delivery stream, ensuring downstream analytics tools can directly query the Parquet data without additional processing.

Answer

Use an S3 lifecycle policy to convert JSON to Parquet.

Answer

Use S3 Batch Operations to convert existing JSON objects to Parquet.

Answer

Use Kinesis Data Analytics to convert the stream to Parquet before writing to S3.

Question 6

Refer to the exhibit. A data engineer runs a Glue ETL job that reads from a CSV file and writes to a Redshift table. The job fails with the error shown. What is the most likely cause?

Accepted Answer

The source CSV file has fewer columns than the target table.. The error indicates a column count mismatch. The source file has 10 columns but the target expects 12. Option B is wrong because the error is about column count, not data type. Option C is wrong because there is no mention of partition count. Option D is wrong because the error is about validation, not permissions.

Answer

The IAM role for the Glue job does not have permission to write to Redshift.

Answer

The target Redshift table has mismatched data types for some columns.

Answer

The Glue job is using an incorrect number of partitions for the source data.

Question 7

A data engineer is designing a multi-Region disaster recovery solution for an Amazon DynamoDB table. The table must be available in a secondary Region with minimal data loss and automatic failover. Which feature should be used?

Accepted Answer

DynamoDB global tables. DynamoDB global tables provide a fully managed, multi-Region, multi-active database solution that replicates data automatically across selected AWS Regions. This ensures automatic failover with eventual consistency and minimal data loss, meeting the disaster recovery requirements for high availability and automatic failover without manual intervention.

Answer

DynamoDB on-demand backup and restore in the secondary Region

Answer

DynamoDB point-in-time recovery (PITR)

Answer

DynamoDB cross-Region snapshot export to S3

Question 8

A company is designing a data lake on Amazon S3. The data includes personal identifiable information (PII). The data engineer must ensure that only authorized users can access the data, and that access is logged for auditing. Which combination of services should the data engineer use?

Accepted Answer

S3 bucket policies with IAM policies and AWS CloudTrail with data events. S3 bucket policies combined with IAM policies provide fine-grained access control to restrict who can access the data, while AWS CloudTrail with data events logs every S3 object-level operation (e.g., GetObject, PutObject) for auditing. This combination directly meets the requirements of authorized access and logging for PII data.

Answer

Amazon S3 access points and VPC endpoints

Answer

Amazon Macie to discover PII and S3 Object Lock to prevent deletion

Answer

AWS KMS to encrypt data and AWS CloudTrail to log access

Question 9

A data engineer needs to share an S3 bucket with another AWS account. They want to ensure that the objects in the bucket remain encrypted with SSE-KMS using a customer managed key. What additional step is required for cross-account access?

Accepted Answer

Modify the KMS key policy to grant the target account kms:Decrypt permission. When using SSE-KMS with a customer managed key, cross-account access requires the KMS key policy to grant the target account's IAM role or user the necessary KMS permissions (kms:Decrypt, and optionally kms:GenerateDataKey). The S3 bucket policy must also grant s3:GetObject, and the target account's IAM policy must allow kms:Decrypt. However, the key policy is the additional step specific to KMS that is not covered by S3 policies alone. Without it, the target account cannot use the key. Option A is correct because modifying the key policy is essential. Option B is insufficient because the target account's IAM policy cannot override the key policy. Option C is unnecessary and breaks encryption. Option D provides S3 access but not KMS access.

Answer

Add an IAM policy in the target account to allow kms:Decrypt

Answer

Disable SSE-KMS encryption on the bucket

Answer

Add a bucket policy that grants the target account s3:GetObject

Question 10

A company uses AWS Glue to transform data from Amazon S3 into Parquet format. The job fails with an out-of-memory error for large files. Which TWO actions can resolve this issue? (Choose TWO.)

Accepted Answer

Increase the number of DPUs allocated to the job.. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job provides more memory and compute capacity, which directly addresses out-of-memory errors when processing large files. AWS Glue uses Apache Spark under the hood, and each DPU provides 4 vCPU and 16 GB of memory, so adding DPUs scales the available resources for in-memory transformations.

Answer

Change the input format from CSV to JSON.

Answer

Use the Glue streaming ETL feature.

Answer

Enable CloudWatch logs for detailed error analysis.

Question 11

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that is failing to deliver data to an Amazon S3 bucket. The stream is configured with a Lambda transformation function. The CloudWatch logs show that the Lambda function is timing out. Which action should the engineer take to resolve the issue?

Accepted Answer

Increase the Lambda function timeout setting.. The CloudWatch logs indicate the Lambda function is timing out. The default Lambda timeout for a Firehose transformation is 60 seconds, and if the function's processing exceeds this limit, it will fail. Increasing the Lambda function timeout setting (Option B) directly addresses this by allowing the function more time to complete its execution before being terminated.

Answer

Reduce the Firehose buffer interval.

Answer

Decrease the Lambda function's batch size in Firehose.

Answer

Increase the memory allocated to the Lambda function.

Question 12

A data engineering team is ingesting streaming data from IoT devices using AWS IoT Core and needs to process the data in near real-time with minimal code. Which AWS service should they use to transform the data before storing it in Amazon S3?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) is the correct choice because it allows you to transform streaming data in near real-time using SQL or Apache Flink with minimal code. It can directly consume data from AWS IoT Core via Kinesis Data Streams or Amazon MSK, apply transformations like filtering, aggregation, or enrichment, and then output the processed data to Amazon S3 without requiring custom application servers.

Answer

AWS Glue

Answer

Amazon Redshift

Answer

Amazon Athena

Question 13

A data engineer needs to ingest data from an Amazon RDS MySQL database into a data lake on Amazon S3. The engineer wants to perform an initial full load and then capture incremental changes. Which TWO AWS services can be combined to achieve this?

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

Accepted Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon MSK is correct. It is a fully managed Apache Kafka service that can ingest data from on-premises Kafka via MirrorMaker or replication, then sink to S3, minimizing operational overhead. AWS Glue (B) is for ETL jobs, not real-time Kafka ingestion. Kinesis Data Analytics (C) is for analyzing streaming data, not ingestion. Amazon Kinesis Data Streams (D) is a different service not directly compatible with Kafka without custom connectors.

Answer

AWS Glue

Answer

Amazon Kinesis Data Analytics

Answer

Amazon Kinesis Data Streams

Question 14

A data engineer is tasked with transforming JSON data from an S3 bucket into Parquet format for efficient querying. The transformation should run on a schedule every hour. Which AWS service is best suited for this task?

Accepted Answer

AWS Glue. AWS Glue is the best choice because it is a fully managed ETL service designed specifically for transforming and cataloging data at scale. It can natively read JSON from S3, convert it to Parquet, and run on a scheduled hourly basis using a Glue job with a trigger, without requiring server management or custom infrastructure.

Answer

AWS Lambda

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Amazon Athena

Answer

Amazon EMR

Question 15

A data engineer applies the above S3 bucket policy to an S3 bucket used by a Glue ETL job. The Glue job writes objects to the bucket. Which of the following is true about the behavior of the policy?

Accepted Answer

The policy denies all PutObject requests because the Allow and Deny statements are contradictory.. In AWS IAM policy evaluation, an explicit Deny always overrides any Allow. The policy has an Allow statement granting s3:PutObject for all principals, but a separate Deny statement explicitly denies s3:PutObject when the encryption condition is not aws:kms. Since the Deny applies to all PutObject requests (including those with no encryption or AES256), and the Allow does not include a condition to match only aws:kms, the Deny takes precedence and blocks all PutObject requests, making the policy effectively deny all PutObject operations.

Answer

The policy allows PutObject with aws:kms encryption because the Allow statement is broader.

Answer

The policy allows PutObject with no encryption because the Deny only applies to PutObject.

Answer

The policy allows PutObject with AES256 encryption and denies PutObject with aws:kms encryption.