DEA-C01 Data Ingestion and Transformation Practice Test 45 — 15 Questions

Question 1

Which THREE factors should be considered when choosing between AWS Glue and Amazon EMR for data transformation? (Choose three.)

Accepted Answer

EMR allows fine-grained control over cluster configuration and software.. Amazon EMR provides full control over cluster configuration, including the ability to customize software, install libraries, and tune Spark, Hadoop, or Hive parameters. This fine-grained control is essential for complex or specialized data transformation pipelines that require specific versions or custom configurations.

Answer

Glue automatically stores data in S3 after transformation.

Answer

EMR supports real-time stream processing with Spark Streaming.

Question 2

Which TWO AWS services can be used as sources for AWS Glue ETL jobs? (Choose two.)

Accepted Answer

Amazon S3. Amazon S3 is a fully managed object storage service that serves as a common source for AWS Glue ETL jobs. Glue can read data from S3 using its built-in crawlers and connectors, supporting formats like Parquet, JSON, CSV, and Avro. The Glue Data Catalog can reference S3 locations, and ETL scripts can directly read from S3 buckets via the s3:// protocol.

Answer

Amazon Route 53

Answer

Amazon CloudFront

Answer

Amazon API Gateway

Question 3

A data engineering team needs to ingest streaming data from an application into Amazon S3 for analytics. The data volume is moderate and the team wants the lowest operational overhead. Which AWS service should they use?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is a fully managed service for loading streaming data into S3 with no code required and minimal operational overhead. Option A is incorrect because Amazon SQS is a message queue service, not designed for streaming data ingestion into S3. Option B is incorrect because AWS Glue is primarily a batch ETL service, not suitable for real-time streaming. Option C is incorrect because Amazon Kinesis Data Streams requires custom consumers and more management, increasing operational overhead.

Answer

Amazon SQS

Answer

AWS Glue

Answer

Amazon Kinesis Data Streams

Question 4

A data pipeline uses AWS Glue to process large CSV files. The team notices that some jobs fail with out-of-memory errors. Which TWO configuration changes can help mitigate this issue?

Accepted Answer

Increase the number of DPUs for the Glue job.. Options B and C are correct: increasing the number of DPUs provides more memory, and enabling autoscaling allows the job to automatically scale resources as needed. Option A (reducing DPUs) would worsen the problem by limiting resources. Option D (converting to Parquet) can improve performance but is not a direct configuration change for the Glue job itself. Option E (job bookmarks) is for incremental processing and does not affect memory.

Answer

Reduce the number of DPUs to limit concurrency.

Answer

Convert input files from CSV to Parquet.

Answer

Enable job bookmarks.

Question 5

A data pipeline ingests JSON data from an S3 bucket using AWS Glue. The JSON files contain nested structures, and the team wants to flatten them for analysis in Amazon Athena. Which Glue transformation is most appropriate?

Accepted Answer

Relationalize. Relationalize is specifically designed to flatten nested JSON into relational tables. Option A (Map) applies a function to each record. Option B (Filter) removes records. Option C (Join) combines datasets.

Answer

Filter

Answer

Join

Answer

Map

Question 6

A company uses Amazon Kinesis Data Firehose to ingest data into an S3 bucket. The data is in JSON format and the team wants to convert it to Parquet before storage. Which TWO configurations are required?

Accepted Answer

Create a Glue table with the schema of the data.. Option B is correct because Firehose data format conversion relies on the AWS Glue Data Catalog: you must create a Glue table (with the appropriate schema and SerDe) that Firehose references so it knows how to interpret the incoming JSON records. Option E is correct because the actual conversion is enabled in the Firehose delivery stream configuration by turning on data format conversion and setting the output format to Parquet (with the Glue table as the schema source). Option A is not required because Kinesis Data Analytics is for SQL/Flink stream processing, not for Firehose's built-in format conversion. Option C is not required because Firehose performs the JSON-to-Parquet conversion natively via Glue, so a custom Lambda transformation is unnecessary. Option D is not required because Athena is a query service for reading data in S3, not a prerequisite for converting it during ingestion.

Answer

Use Kinesis Data Analytics to transform data to Parquet.

Answer

Configure a Lambda function to convert data on the fly.

Answer

Set up an Athena table to read the data.

Question 7

A data engineering team is designing a batch processing workflow using AWS Glue. The job reads from an S3 bucket, transforms data, and writes to another S3 bucket. The job runs daily and processes new data incrementally. Which THREE features should they use to optimize performance and cost?

Accepted Answer

Enable Glue job autoscaling.. Option B is correct because Glue job autoscaling dynamically adjusts the number of workers (DPUs) based on the workload, which reduces cost during lighter stages and improves throughput during heavier ones for a daily incremental job. Option D is correct because predicate pushdown and column pruning let Glue read only the needed partitions, rows, and columns from Parquet/ORC on S3, cutting I/O, shuffle, and DPU-hours. Option E is correct because Glue job bookmarks persist state so the job processes only new or changed data since the last run, which is exactly the incremental daily pattern described and avoids reprocessing old S3 objects. Option A is not marked correct because converting all input data to Parquet is a broad storage-format change, not a Glue job performance/cost feature, and it may add conversion overhead. Option C is not marked correct because manually increasing DPUs for every run is static over-provisioning that raises cost rather than optimizing it, unlike autoscaling.

Answer

Convert all input data to Parquet format before processing.

Answer

Manually increase the number of DPUs for each run.

Question 8

A data engineer runs an AWS Glue job that reads from a JDBC connection to a PostgreSQL database. The job fails with a 'Connection timed out' error. The Glue job runs in a VPC with the appropriate security group. What is the most likely cause?

Accepted Answer

The network ACL associated with the Glue job's subnet is blocking outbound traffic.. A network ACL (NACL) is a stateless subnet-level firewall that can block outbound traffic. If the NACL associated with the Glue job's subnet does not allow outbound traffic to the PostgreSQL database's port (e.g., 5432), the connection times out. Since the security group is stated to be appropriate, the NACL is the most likely remaining network-layer cause.

Answer

The Glue job does not have permission to access the database.

Answer

The security group does not allow inbound traffic from the Glue job.

Answer

The database credentials are incorrect.

Question 9

A data pipeline ingests streaming data from thousands of IoT devices into Kinesis Data Streams. The data must be transformed using a simple field mapping before being stored in S3. Which service should be used to perform the transformation with minimal operational overhead?

Accepted Answer

Kinesis Data Firehose with a Lambda transformation. Kinesis Data Firehose can invoke a Lambda function to perform simple field mapping transformations before delivering data to S3, minimizing operational overhead. Option A is wrong because AWS Lambda invoked directly by the Kinesis stream requires custom logic for S3 delivery and stream management, increasing overhead. Option B is wrong because AWS Glue ETL jobs are designed for batch processing and are more complex to set up for streaming transformations. Option C is wrong because Kinesis Data Analytics is used for real-time analytics with SQL or Flink, not simple field mapping transformations.

Answer

AWS Lambda function invoked by the Kinesis stream

Answer

AWS Glue ETL job

Answer

Kinesis Data Analytics

Question 10

A data engineer is using AWS Glue ETL to transform data from an S3 data lake. The job fails with a memory error. Which approach should be used to resolve this issue without major code changes?

Accepted Answer

Increase the number of DPUs allocated to the Glue job. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job provides more memory and parallelism. Option A is wrong because rewriting in PySpark is a major code change. Option B is wrong because using a smaller file format may not address memory issues. Option D is wrong because using a different service is unnecessary.

Answer

Rewrite the ETL script in PySpark instead of Scala

Answer

Change the input file format from CSV to Parquet

Answer

Use Amazon EMR instead of AWS Glue

Question 11

A company stores IoT sensor data in S3 as JSON files. They need to convert the data to Parquet format for efficient querying with Amazon Athena. Which AWS service can perform this transformation with minimal effort?

Accepted Answer

AWS Glue ETL job. AWS Glue ETL jobs can easily convert JSON to Parquet with built-in transforms. Option A is wrong because Kinesis Data Firehose is for streaming data ingestion, not batch transformations. Option B is wrong because Amazon Athena is a query engine, not a transformation service. Option D is wrong because AWS Lambda is for small, event-driven transformations and is not ideal for large-scale batch conversion.

Answer

Kinesis Data Firehose

Answer

Amazon Athena

Answer

AWS Lambda

Question 12

A company is designing a data ingestion pipeline for clickstream data from a website. The data must be ingested in near real-time. Which TWO services can be used together to build this pipeline?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams (A) is correct because it is designed for real-time streaming ingestion of high-volume data such as clickstream events, allowing producers to continuously write records that can be processed within milliseconds by consumers. Amazon Kinesis Data Firehose (D) is correct because it can consume streaming data and reliably load it into destinations like S3, Redshift, or Elasticsearch in near real-time, making it a natural complement to Kinesis Data Streams for building an end-to-end ingestion pipeline. Together, Kinesis Data Streams captures and buffers the clickstream events while Firehose delivers them to downstream storage or analytics services with minimal latency. Amazon SQS (B) is a message queue for decoupling applications but is not purpose-built for real-time streaming analytics pipelines. Amazon S3 (C) is a storage service, not an ingestion mechanism, and Amazon DynamoDB (E) is a NoSQL database, so neither serves as the streaming ingestion component required here.

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon S3

Answer

Amazon DynamoDB

Question 13

A company is migrating a legacy on-premises ETL pipeline to AWS. The pipeline processes daily batch files from an FTP server. The data must be transformed using complex business logic before being loaded into Amazon Redshift. Which THREE AWS services should be used for this migration?

Accepted Answer

Amazon Redshift. AWS Transfer Family (E) is correct because it provides a fully managed FTP/FTPS/SFTP endpoint that can land the legacy daily batch files directly into Amazon S3, replacing the on-premises FTP server without changing the source's protocol. AWS Glue (C) is correct because it is a serverless ETL service whose Spark-based jobs and Glue Data Catalog can implement the complex business logic transformations on the batch data before loading. Amazon Redshift (B) is correct because it is the target data warehouse where the transformed data is loaded for analytics, and Glue can write to it via the Redshift JDBC/ODBC connection or the Redshift data source. Amazon Athena (A) is not appropriate because it is an interactive query service over S3, not an ETL engine for applying complex transformation logic. Amazon Kinesis Data Streams (D) is not appropriate because it is designed for real-time streaming ingestion, whereas this pipeline processes daily batch files.

Answer

Amazon Athena

Answer

Amazon Kinesis Data Streams

Question 14

A company uses Kinesis Data Firehose to deliver streaming data to S3. They need to transform the data by adding a timestamp and removing sensitive fields. Which TWO approaches can achieve this?

Accepted Answer

Use AWS Glue ETL to process data after delivery to S3. Option E is correct because Kinesis Data Firehose natively supports Lambda-based data transformation: you attach a Lambda function to the delivery stream, and Firehose invokes it synchronously on each record (or batch) before delivery, allowing the function to add a timestamp and strip sensitive fields. Option C is correct because AWS Glue ETL can process the data after it lands in S3, using Spark-based jobs to enrich records with timestamps and drop sensitive columns, which is a valid post-delivery transformation approach. Option A is not correct because Kinesis Data Analytics is for real-time SQL/Flink analytics on streams, not for modifying records delivered by Firehose. Option B is not correct because S3 Select only filters and projects data at rest using SQL on individual objects; it cannot add timestamps or rewrite/remove fields. Option D is not correct because Redshift Spectrum queries data in S3 for analytics and does not transform or modify the delivered objects.

Answer

Use Kinesis Data Analytics to transform the stream

Answer

Use S3 Select to transform data at rest

Answer

Use Amazon Redshift Spectrum to transform data

Question 15

A company is streaming IoT sensor data to Amazon Kinesis Data Streams. The data is JSON with a schema that changes occasionally. They want to load the data into Amazon S3 in Parquet format partitioned by date and sensor_id. Which approach is MOST cost-effective and operationally efficient?

Accepted Answer

Use Amazon Kinesis Data Firehose with a schema from AWS Glue Data Catalog to convert to Parquet and enable dynamic partitioning by date and sensor_id.. Amazon Kinesis Data Firehose is the most cost-effective and operationally efficient solution because it natively supports converting JSON to Parquet using a schema from the AWS Glue Data Catalog and can dynamically partition data by date and sensor_id. Firehose handles buffering, compression, and delivery to S3 without requiring custom code or infrastructure management.

Answer

Use Amazon EMR to read from Kinesis Data Streams and write to S3 in Parquet format.

Answer

Use a Lambda function to transform records to Parquet and write to S3.

Answer

Use a custom Kinesis Client Library application on EC2 to buffer and write Parquet files to S3.