DEA-C01 Data Ingestion and Transformation Practice Test 33 — 15 Questions

Question 1

A company needs to transfer 50 TB of historical data from an on-premises HDFS cluster to Amazon S3. The network bandwidth is limited to 1 Gbps, and the transfer must complete within 10 days. The data is compressible. Which solution is MOST appropriate?

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

Accepted Answer

Use AWS DataSync over a Direct Connect connection.. With a 1 Gbps link, transferring 50 TB would take approximately 111 hours (about 4.6 days) theoretically, well within the 10-day window. AWS DataSync over a Direct Connect connection provides a high-speed, secure, and reliable method to transfer large datasets online. DataSync automates the HDFS-to-S3 transfer, handles compression, and can saturate the 1 Gbps link, making it the most appropriate solution. Snowball Edge involves physical shipment and logistics, which would likely exceed the 10-day deadline. Options B and C also rely on internet bandwidth and are less efficient than DataSync.

Answer

Use Amazon S3 Transfer Acceleration with multipart uploads.

Answer

Use multiple parallel AWS CLI sync commands over the internet.

Answer

Use AWS Snowball Edge to physically ship the data.

Question 2

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The delivery occasionally fails due to 'ThrottlingException' from S3. What should the team do to resolve this issue without losing data?

Accepted Answer

Increase the Firehose buffer size and buffer interval to reduce the number of S3 PUT requests.. The ThrottlingException from S3 indicates that Kinesis Data Firehose is sending too many PUT requests to S3. Increasing the buffer size or buffer interval causes Firehose to accumulate more records before writing, reducing the number of PUT requests and preventing throttling. Option A (S3 Transfer Acceleration) improves transfer speed, not request rate limits. Option B (disabling error logging) does not reduce API calls and hides issues. Option C (deliver to DynamoDB) is not supported by Kinesis Data Firehose. Therefore, option D is correct.

Answer

Enable S3 Transfer Acceleration on the destination bucket.

Answer

Disable error logging in Firehose to reduce API calls.

Answer

Configure Firehose to deliver data to Amazon DynamoDB instead.

Question 3

A data engineer needs to transform JSON data into CSV format using AWS Glue. The transformation is simple and must be executed on a schedule. Which Glue component is MOST suitable?

Accepted Answer

Glue ETL job. A Glue ETL job is the most suitable component because it can execute a script (Python/Scala) to transform JSON data into CSV format and can be scheduled to run on a recurring basis. Glue Crawlers only discover and catalog metadata, not transform data. The Glue Data Catalog is a metadata repository, not a transformation tool. A Glue Development Endpoint is used for interactive development and testing, not for scheduled production jobs.

Answer

Glue Crawler

Answer

Glue Data Catalog

Answer

Glue Development Endpoint

Question 4

A company uses AWS DMS to migrate an on-premises Oracle database to Amazon RDS for PostgreSQL. The migration completes, but the target table has more rows than the source. Which is the MOST likely cause?

Accepted Answer

Target tables lack unique constraints, causing DMS to insert duplicate rows.. When target tables lack unique constraints (like primary keys or unique indexes), AWS DMS cannot identify duplicate records during change data capture (CDC). If the same change is applied multiple times (e.g., due to recovery after a failure), DMS may insert duplicate rows, causing the target to have more rows than the source. Option A is incorrect because binary replication is not used; DMS uses logical replication. Option B is incorrect because case sensitivity differences could lead to missing rows, not extra rows. Option C is incorrect because a full load alone would not cause duplicates; the issue arises during ongoing replication without unique constraints.

Answer

DMS used binary replication which included extra metadata rows.

Answer

Oracle and PostgreSQL handle case sensitivity differently.

Answer

DMS performed a full load instead of ongoing replication.

Question 5

A company wants to ingest data from SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate and updates occur frequently. Which AWS service is BEST suited for this task?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is specifically designed to securely transfer data from SaaS applications like Salesforce and Marketo to AWS services such as Amazon S3. It handles authentication, data transformation, and scheduling. Amazon Kinesis Data Streams is for real-time streaming data, not batch ingestion from SaaS. AWS Database Migration Service (DMS) is for migrating databases, not SaaS data. AWS Glue is an ETL service that can process data but is not optimized for direct SaaS ingestion.

Answer

Amazon Kinesis Data Streams

Answer

AWS Database Migration Service (DMS)

Answer

AWS Glue

Question 6

A company uses AWS Lambda to process events from an S3 bucket. The Lambda function writes transformed data to another S3 bucket. Occasionally, the Lambda invocation fails with 'ResourceNotFoundException'. What is the MOST likely cause?

Accepted Answer

The destination S3 bucket does not exist or the Lambda function's IAM role lacks permissions.. The destination bucket may not exist or the Lambda function's IAM role lacks permissions to write to it. Option A is wrong because Lambda timeouts would cause 'Timeout' error. Option C is wrong because S3 event notifications are reliable. Option D is wrong because the source bucket exists since it triggered the event.

Answer

The Lambda function timed out.

Answer

The S3 event notification is misconfigured.

Answer

The source S3 bucket has versioning disabled.

Question 7

A company uses AWS Glue to process large datasets. The Glue job occasionally fails with 'DiskFull' errors. Which TWO actions should the engineer take to resolve this issue? (Choose two.)

Accepted Answer

Increase the number of workers for the Glue job.

Answer

Store intermediate results in Amazon S3 instead of local disk.

Answer

Enable job bookmark to skip already processed data.

Answer

Use a Python shell job instead of Spark.

Question 8

A company needs to ingest streaming data from an existing Amazon Kinesis Data Streams into Amazon S3 with partitioning by date. Which TWO services can accomplish this with minimal coding? (Choose two.)

Accepted Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue Streaming

Answer

Amazon Kinesis Data Analytics

Answer

Amazon S3 Transfer Acceleration

Question 9

A company uses AWS DMS to continuously replicate data from an on-premises SQL Server to Amazon Aurora MySQL. The replication lag is increasing. Which THREE actions can reduce the lag? (Choose three.)

Accepted Answer

Use parallel apply on the target endpoint.

Answer

Enable DMS validation.

Answer

Enable Multi-AZ for the DMS replication instance.

Question 10

A company ingests 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

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Analytics for Apache Flink enables real-time stream processing with custom Python code via Apache Flink's Python API, making it suitable for complex transformations. Option A (Amazon EMR) is wrong as it requires significant setup and is not a fully managed streaming service. Option B (Amazon Kinesis Data Firehose) is wrong because although it can invoke Lambda for simple transformations, it is limited in complexity and not designed for rich Python custom logic. Option C (AWS Glue) is wrong because it is primarily a batch ETL service and lacks native real-time stream processing capabilities.

Answer

Amazon EMR

Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue

Question 11

A data engineer is troubleshooting a daily batch ingestion pipeline that uses AWS Glue to read CSV files from Amazon S3 and write Parquet files to another S3 bucket. The job runs successfully but takes significantly longer than expected. The engineer notices that the input data is highly skewed with many small files. Which is the most effective optimization to reduce job duration?

Accepted Answer

Enable the 'groupFiles' option in the S3 source configuration. The 'groupFiles' option in AWS Glue's S3 source configuration allows Glue to combine small files into larger splits, reducing the number of tasks and the overhead associated with processing many small files. This directly addresses the high skew and small file issue, significantly reducing job duration. Option A is incorrect because changing the output format from Parquet to JSON would likely increase file size and processing time, not reduce it. Option C is incorrect because increasing DPUs may improve parallelism but does not solve the file-level overhead; it could even be wasteful if the bottleneck is task scheduling. Option D is incorrect because enabling the schema registry is unrelated to file grouping; it is used for schema management and validation.

Answer

Change the output format to JSON

Answer

Increase the number of DPUs allocated to the job

Answer

Enable the 'use_glue_schema_registry' option

Question 12

A company wants to ingest real-time clickstream data from a website into Amazon S3 with minimal code. The data should be delivered within 60 seconds of generation. Which AWS service should be used?

Accepted Answer

Amazon Kinesis Data Firehose. (Amazon Kinesis Data Firehose) is correct because it is a fully managed service that can ingest real-time clickstream data and deliver it to Amazon S3 in near-real-time (typically less than 60 seconds). Option B (AWS DMS) is for database migration, not streaming ingestion. Option C (Amazon Kinesis Data Streams) requires custom consumers to process and deliver data to S3, adding code overhead. Option D (S3 Transfer Acceleration) only speeds up uploads but does not provide streaming ingestion capabilities.

Answer

AWS Database Migration Service (DMS)

Answer

Amazon Kinesis Data Streams

Answer

Amazon S3 Transfer Acceleration

Question 13

A company is using Amazon Kinesis Data Firehose to ingest data into Amazon S3. The data must be transformed from JSON to Parquet format before delivery. Which feature should be enabled on the Firehose delivery stream?

Accepted Answer

Format conversion (Parquet/ORC). Amazon Kinesis Data Firehose has a built-in format conversion feature that can automatically convert input data from JSON to Parquet or ORC format before delivery to Amazon S3. Option A (Amazon Kinesis Data Analytics) is for real-time stream processing, not format conversion within Firehose. Option B (Amazon S3 event notifications) triggers notifications on S3 events, not data transformation. Option D (AWS Lambda transformation) allows custom code for data transformation but is not specifically for converting JSON to Parquet; the built-in format conversion is the appropriate feature for this task.

Answer

Amazon Kinesis Data Analytics

Answer

Amazon S3 event notifications

Answer

AWS Lambda transformation

Question 14

A data engineer is designing a streaming ingestion pipeline using Amazon Kinesis Data Streams. The stream has 10 shards, and the data volume is expected to grow by 50% over the next month. The engineer needs to ensure that the pipeline can scale without manual intervention. Which approach should be used?

Accepted Answer

Switch the Kinesis stream to on-demand capacity mode. Kinesis Data Streams on-demand capacity mode automatically scales the number of shards based on the incoming traffic pattern, eliminating the need for manual intervention. Option A is incorrect because CloudWatch Alarms can trigger a Lambda function to add shards via the UpdateShardCount API, but this approach requires custom code and does not provide automatic scaling without manual setup. Option B is incorrect because Auto Scaling groups are used for EC2 instances, not for Kinesis stream shards. Option D is incorrect because Lambda functions can be used to scale shards programmatically, but this still requires custom implementation and is not a native automatic scaling feature.

Answer

Set up a CloudWatch Alarm to trigger a Lambda function to add shards

Answer

Use an Auto Scaling group to add more shards

Answer

Configure the stream to use a Lambda function that scales shards

Question 15

A company needs to ingest data from multiple SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data sources have different schemas and update frequencies. Which AWS service should be used to build this ingestion pipeline with minimal code?

Accepted Answer

Amazon AppFlow. Amazon AppFlow (Option D) is the correct answer because it is purpose-built for ingesting data from SaaS applications like Salesforce and Marketo into Amazon S3 with minimal code. It supports various source connectors, handles schema variations, and allows scheduling based on update frequencies. AWS Data Pipeline (A) requires more manual configuration and code for connectors. AWS Glue (B) has connectors but is more complex for this use case, often requiring additional ETL scripting. Amazon Kinesis Data Firehose (C) is designed for streaming data, not batch extraction from SaaS APIs.

Answer

AWS Data Pipeline

Answer

AWS Glue

Answer

Amazon Kinesis Data Firehose