DEA-C01 Data Ingestion and Transformation Practice Test 43 — 15 Questions

Question 1

A company needs to transfer 20 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited and the transfer must complete within one week. Which service should the company use?

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

Accepted Answer

AWS Snowball Edge. AWS Snowball Edge is a petabyte-scale data transport solution that uses physical devices to transfer large amounts of data into and out of AWS, bypassing network bandwidth limitations. For 20 TB over a limited network within one week, Snowball Edge is the most practical and cost-effective choice. It supports up to 80 TB usable capacity per device and provides encryption and tamper-resistant security.

Answer

Amazon S3 Transfer Acceleration

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AWS Direct Connect with DataSync

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AWS DataSync over a VPN connection

Question 2

Which THREE factors should a data engineer consider when choosing between AWS Glue and Amazon EMR for a data transformation job? (Choose three.)

Accepted Answer

The level of control over the execution environment and dependencies. When choosing between AWS Glue and Amazon EMR for a data transformation job, key considerations include: the level of control over the execution environment (EMR offers more customization, while Glue is managed), the deployment model (Glue is serverless, EMR is cluster-based), and the cost structure (Glue charges per DPU, EMR charges per EC2 instance). Options A and B are not differentiating factors because both services support Apache Spark and can output to S3.

Answer

The ability to output results to Amazon S3

Answer

The support for Apache Spark

Question 3

Which TWO actions can improve the performance of an AWS Glue ETL job that processes large datasets in Amazon S3? (Choose two.)

Accepted Answer

Increase the number of DPUs allocated to the job.. Increasing the number of DPUs allocates more processing power to the Glue job, which can speed up data processing for large datasets. Option D is correct because columnar file formats like Parquet or ORC are more efficient for analytical queries, reduce I/O, and allow better compression compared to row-based formats. Option A is incorrect: increasing crawler frequency only affects the metadata catalog update frequency, not the ETL job performance. Option B is incorrect: using a single Availability Zone for the S3 bucket does not improve performance and may reduce availability. Option E is incorrect: using a single large file can reduce parallelism, as distributed processing benefits from splitting data into multiple files to be processed in parallel by different executors.

Answer

Increase the frequency of the Glue crawler.

Answer

Use a single Availability Zone for the S3 bucket.

Answer

Use a single large file instead of many small files.

Question 4

Refer to the exhibit. A data engineer runs this CLI command on an S3 bucket. The data is ingested from multiple sources. Which AWS service would be best to process these files in a single batch transformation?

Accepted Answer

AWS Glue. AWS Glue is designed for batch ETL processing, capable of handling multiple files of varying sizes in a single transformation job. Option A is wrong because Lambda has limitations on execution time (15 minutes) and memory (10 GB), making it unsuitable for large-scale batch transforms. Option B is wrong because Kinesis Data Analytics is for real-time stream processing, not batch. Option C is wrong because Athena is an interactive query service for ad-hoc analysis of data in S3, not for batch transformations.

Answer

AWS Lambda

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Amazon Kinesis Data Analytics

Answer

Amazon Athena

Question 5

A data engineer needs to ingest streaming data from thousands of IoT devices and immediately process each record with minimal latency. Which AWS service should be used as the ingestion point?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed as a massively scalable, low-latency ingestion service for streaming data, capable of handling thousands of producers (IoT devices) and delivering records to consumers within milliseconds. It durably stores data in shards for up to 365 days and integrates natively with Lambda, Kinesis Data Analytics, and Firehose for immediate processing. This makes it the canonical ingestion point for real-time IoT pipelines on AWS.

Answer

AWS Lambda

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Amazon S3

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AWS Glue

Question 6

A company wants to schedule a nightly batch job to copy data from an on-premises PostgreSQL database to Amazon S3. The solution must minimize operational overhead. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it provides a fully managed ETL service that can connect to on-premises PostgreSQL via JDBC, extract data, and write it to Amazon S3 with minimal configuration. Glue's built-in scheduler can run the job nightly, eliminating the need to manage servers or orchestration infrastructure, which directly meets the requirement to minimize operational overhead.

Answer

AWS Data Pipeline

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Amazon EMR

Answer

AWS Database Migration Service (AWS DMS) with ongoing replication

Question 7

A company is ingesting streaming data into Kinesis Data Streams. The consumer application experiences high latency due to a single shard bottleneck. What is the most effective way to reduce latency?

Accepted Answer

Increase the number of shards in the data stream.. Increasing the number of shards in the Kinesis data stream distributes the incoming records across more shards, allowing the consumer application to process them in parallel and reducing the per-shard bottleneck that causes high latency. Each shard supports up to 1 MB/s or 1,000 records/s ingress, so adding shards directly increases throughput capacity. This is the most direct and effective way to address a single-shard bottleneck.

Answer

Wait for automatic scaling to add shards.

Answer

Use the Kinesis Client Library (KCL) to process records.

Answer

Switch to Amazon Kinesis Data Firehose.

Question 8

A data engineering team uses AWS Glue ETL jobs to process data daily. They notice that job run times are increasing as data volume grows. Which action will most effectively improve performance without changing the code?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs (Data Processing Units) for the Glue job directly scales the compute resources allocated to the job, which can improve performance and reduce run times as data volume grows. This does not require code changes. Job bookmarks help skip already processed data but do not address increasing data volume. Smaller instance types and more files in S3 may not effectively improve performance.

Answer

Use a smaller instance type for the Glue job.

Answer

Enable job bookmark to skip previously processed data.

Answer

Split the data into more files in S3.

Question 9

A company uses Amazon Kinesis Data Firehose to deliver data to an S3 bucket. The data contains personally identifiable information (PII) that must be redacted before storage. Which TWO actions can achieve this requirement? (Choose TWO.)

Accepted Answer

Use AWS Glue ETL to read from the S3 bucket and write redacted data to another S3 bucket.. Option A is correct because AWS Glue ETL jobs can read the raw data from the S3 bucket, apply transformation logic (such as masking or removing PII fields), and write the redacted output to another S3 bucket, satisfying the requirement that PII be redacted before storage in the destination. Option E is correct because Kinesis Data Firehose supports Lambda-based record transformation, where a Lambda function is invoked on each incoming record to redact PII before Firehose delivers the data to S3, which is the most direct in-stream solution. Option B is not correct because Amazon Athena is a query service that reads data already stored in S3; it cannot redact PII before storage. Option C is not correct because Amazon Macie discovers and classifies sensitive data but does not automatically redact or modify it. Option D is not correct because AWS DMS is designed for database migration and replication, not for transforming and redacting PII in a Firehose-to-S3 data pipeline.

Answer

Use Amazon Athena to query the data and redact PII on the fly.

Answer

Use Amazon Macie to discover and automatically redact PII before storage.

Answer

Use AWS Database Migration Service (AWS DMS) to replicate data and apply transformations.

Question 10

A data engineer reviews the Glue job configuration. The job fails when processing large datasets. The error message indicates out-of-memory in the executors. Which change to the job configuration will most directly address this issue?

Accepted Answer

Change the worker type from Standard to G.2X.. The job fails due to out-of-memory errors in the executors. The current configuration uses 5 Standard workers, each with 16 GB of memory. Changing the worker type to G.2X provides 32 GB per worker, doubling the memory per executor and directly addressing the OOM issue. Increasing the number of workers (option C) adds more executors but does not increase memory per executor, which may not resolve the issue if each executor still runs out of memory. Options B and D do not affect memory.

Answer

Increase the timeout from 30 to 60 minutes.

Answer

Increase the number of workers from 5 to 10.

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Set MaxRetries to 3.

Question 11

A company uses AWS Glue to catalog data in Amazon S3. The data arrives in Parquet format, but the crawler fails to update the schema when new columns are added. What is the most likely cause?

Accepted Answer

The crawler configuration has 'Update the table definition' set to 'Ignore the change'.. AWS Glue crawlers have a table-update behavior controlled by the 'Update the table definition in the Data Catalog' setting (SchemaChangePolicy.UpdateBehavior). If it is set to 'Ignore the change' (LOG), the crawler detects new columns but does not modify the table definition, so the schema appears stale. Setting it to 'Update the table definition' (UPDATE_IN_DATABASE) allows the crawler to add new columns to the catalog table.

Answer

The Glue Data Catalog is not configured to accept schema changes.

Answer

Parquet files do not support schema evolution.

Answer

The S3 bucket has versioning disabled.

Question 12

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 component designed to run transformation scripts (Spark or Python shell) on data, and it can be scheduled via triggers. For a simple JSON-to-CSV transformation on a schedule, an ETL job is the correct choice.

Answer

Glue Crawler

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Glue Data Catalog

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Glue Development Endpoint

Question 13

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 purpose-built for ingesting data from SaaS applications such as Salesforce, Marketo, Slack, and Google Analytics into AWS services like Amazon S3. It provides native connectors, supports scheduled or event-driven flows, and handles authentication and incremental transfers without custom code. For moderate-volume, frequently updated SaaS data landing in S3, AppFlow is the most direct and operationally simple fit.

Answer

Amazon Kinesis Data Streams

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AWS Database Migration Service (DMS)

Answer

AWS Glue

Question 14

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 15

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