DEA-C01 Practice Test 39 — 15 Questions

Question 1

Which TWO options are valid methods to ingest on-premises relational database data into Amazon S3 for analytics? (Choose 2.)

Accepted Answer

AWS Glue ETL job with JDBC connection to source. AWS Glue ETL jobs can connect to on-premises relational databases via JDBC, extract data, and write it directly to Amazon S3 in formats like Parquet or ORC. This is a fully managed, serverless approach suitable for batch ingestion and transformation of structured data for analytics.

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AWS Snowball Edge

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Amazon Kinesis Data Streams with Direct Put

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Amazon AppFlow

Question 2

Refer to the exhibit. A data engineer runs this CLI command to check an object's metadata. The engineer wants to verify if the object is eligible for lifecycle transition to S3 Glacier based on its age. What additional information is needed?

Accepted Answer

The current date. To determine whether an S3 object is eligible for lifecycle transition to S3 Glacier based on age, you need the object's creation date (LastModified) and the current date to compute its age. The exhibit presumably shows the object's LastModified timestamp, so the missing piece is the current date to calculate how many days old the object is relative to the lifecycle rule's transition threshold.

Answer

The ETag value

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The metadata archive flag

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The ContentLength value

Question 3

A company is designing a data lake on Amazon S3. Which TWO strategies improve query performance for Amazon Athena?

Accepted Answer

Partition the data by frequently queried columns such as date or region.. Option C is correct because partitioning the S3 data by frequently filtered columns such as date or region lets Athena prune partitions and scan only the relevant prefixes, drastically reducing the amount of data read and thus improving query speed and lowering cost. Option D is correct because columnar formats like Parquet or ORC allow Athena to read only the columns referenced in the query and benefit from compression and predicate pushdown, minimizing I/O compared to row-based formats. Options A and B do not improve query performance: S3 Versioning only retains object versions for recovery, and SSE-KMS only encrypts data at rest, adding no scan optimization (and KMS calls can even add latency). Option E is incorrect because CSV is a row-based, uncompressed text format that forces Athena to scan entire rows and cannot skip unneeded columns, making queries slower and more expensive than Parquet/ORC.

Answer

Enable S3 Versioning on the bucket.

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Use server-side encryption with AWS KMS (SSE-KMS).

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Store data in CSV format with header rows.

Question 4

A data engineer needs to store sensitive data in Amazon S3 and automatically classify the data using a managed service. The data is uploaded via an S3 bucket. Which AWS service can automatically detect and classify sensitive data?

Accepted Answer

Amazon Macie. Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to automatically discover, classify, and protect sensitive data stored in Amazon S3. It continuously evaluates S3 buckets and identifies sensitive content such as PII, credentials, and financial data, generating findings that can be routed to EventBridge or Security Hub. This directly matches the requirement to automatically detect and classify sensitive data in S3.

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AWS WAF

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Amazon Inspector

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AWS Shield

Question 5

A data engineer needs to store transaction data that requires strong consistency, ACID transactions, and complex join queries. Which AWS service is most appropriate?

Accepted Answer

Amazon RDS for PostgreSQL. Amazon RDS for PostgreSQL is the most appropriate choice because it provides full ACID transaction support, strong consistency, and the ability to perform complex join queries using standard SQL. Unlike NoSQL or data warehouse solutions, PostgreSQL is a relational database that excels at enforcing referential integrity and supporting multi-table joins with advanced indexing.

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Amazon DynamoDB

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Amazon S3

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Amazon Redshift

Question 6

A company uses AWS Glue to transform data in S3. The Glue job fails with memory errors. Which THREE actions can help resolve this?

Accepted Answer

Optimize the transformation to use pushdown predicates.. Options A, B, and C are correct. Using pushdown predicates (A) reduces the amount of data read by filtering at the data source, which can alleviate memory pressure. Using a larger worker type (B), such as G.2X, increases the memory available per worker, directly addressing out-of-memory errors. Increasing the number of DPUs (C) adds more workers, distributing the memory load. Option D (increasing job timeout) does not solve memory issues, and Option E (decreasing DPUs) would reduce available memory, making the problem worse.

Answer

Increase the job timeout.

Answer

Decrease the number of DPUs.

Question 7

Arrange the steps to create an AWS Glue job that transforms data from Amazon S3 to Amazon Redshift in the correct order.

Accepted Answer

Step 1: Catalog the source data with a crawler. Step 2: Prepare the ETL script. Step 3: Configure the job with connections. Step 4: Run the job. Step 5: Verify the results in Amazon Redshift.. First, catalog the source data with a crawler. Then, prepare the ETL script. Configure the job with connections, run it, and finally verify the results in Redshift.

Answer

Step 1: Prepare the ETL script. Step 2: Catalog the source data with a crawler. Step 3: Configure the job with connections. Step 4: Run the job. Step 5: Verify the results in Amazon Redshift.

Answer

Step 1: Configure the job with connections. Step 2: Catalog the source data with a crawler. Step 3: Prepare the ETL script. Step 4: Run the job. Step 5: Verify the results in Amazon Redshift.

Answer

Step 1: Catalog the source data with a crawler. Step 2: Configure the job with connections. Step 3: Prepare the ETL script. Step 4: Run the job. Step 5: Verify the results in Amazon Redshift.

Question 8

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.

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AWS Lambda

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Amazon S3

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AWS Glue

Question 9

A company uses AWS KMS to encrypt sensitive data stored in S3. To meet compliance requirements, they need to ensure that the encryption keys are automatically rotated every year. Which type of KMS key should they use?

Accepted Answer

Customer managed key with automatic rotation enabled. Customer managed keys with automatic rotation enabled support automatic annual rotation, meeting the compliance requirement. AWS managed keys rotate automatically every year, but they cannot be controlled or customized by the customer, so they are not the best choice when the customer needs to manage the key policy or rotation schedule. Custom key stores (CloudHSM) do not support automatic rotation. Option A (customer managed key with manual rotation) requires manual intervention to rotate, not automatic. Therefore, D is the correct answer.

Answer

Customer managed key with manual rotation

Answer

AWS managed key

Answer

Custom key store (CloudHSM) key

Question 10

A company stores sensitive customer data in an S3 bucket. The security team requires that all data be encrypted at rest using a customer-managed AWS KMS key. However, when a data engineer attempts to upload an object using the AWS CLI, the upload fails with an access denied error. The engineer has s3:PutObject permission on the bucket. Which additional permission is most likely missing?

Accepted Answer

kms:GenerateDataKey. When S3 encrypts an object with SSE-KMS using a customer-managed key, the caller must have kms:GenerateDataKey permission on that KMS key so S3 can obtain a data key to encrypt the object. The s3:PutObject permission alone authorizes the S3 API call but does not grant the KMS operation needed to produce the encryption key, so the upload fails with AccessDenied. Granting kms:GenerateDataKey on the CMK resolves the failure.

Answer

kms:CreateKey

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kms:Decrypt

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s3:PutObjectAcl

Question 11

A company runs a data pipeline that uses AWS Lambda to process files uploaded to an S3 bucket. Recently, some files have been processed multiple times. The Lambda function is triggered by S3 event notifications. What is the MOST likely cause of duplicate processing?

Accepted Answer

S3 event notifications are delivered at least once.. S3 event notifications are delivered using an at-least-once delivery model, meaning the same event can be delivered more than once, which causes Lambda to be invoked multiple times for the same object. This is the most likely cause of duplicate processing when the function itself is not idempotent. The root cause is the delivery guarantee, not the function's error handling.

Answer

The Lambda function has a high error rate and retries.

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The Lambda function is not idempotent.

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The Lambda function has a reserved concurrency setting.

Question 12

An AWS Glue job that performs data transformation on large Parquet files in Amazon S3 is taking a long time to complete. The job uses the default number of DPUs. Which change would most likely improve the job's performance?

Accepted Answer

Increase 'Max capacity' (number of DPUs) for the job.. AWS Glue allocates DPUs (Data Processing Units) to run the job, and increasing Max capacity adds more compute and memory resources that can parallelize the transformation across partitions. For large Parquet files with the default DPU count, the job is likely CPU- or memory-bound, so scaling DPUs is the most direct performance improvement. Parquet is already a columnar, compressed format, so the bottleneck is compute, not I/O format.

Answer

Use 'coalesce' to reduce the number of output files.

Answer

Reduce the number of partitions in the source data.

Answer

Change the input format from Parquet to CSV.

Question 13

A data engineer is using AWS Glue to process a large dataset in Amazon S3. The dataset consists of many small JSON files (average 100 KB each) stored in a single prefix. The Glue job reads these files, performs transformations, and writes the output to Parquet in another S3 location. The job is running slowly and consuming many DPUs. Which action should the data engineer take to improve performance?

Accepted Answer

Use AWS Glue's `groupFiles` option to group multiple small files into larger chunks for processing.. The `groupFiles` option in AWS Glue allows the job to group multiple small files into a single partition, reducing the number of tasks and the overhead of reading many small files. This directly addresses the performance bottleneck caused by many small JSON files, improving job speed and reducing DPU consumption. Other options do not solve the small file issue.

Answer

Convert the JSON files to Parquet using an AWS Glue crawler before processing.

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Enable AWS Glue job bookmarks to skip already processed files.

Answer

Increase the number of DPUs allocated to the job to enable more parallel tasks.

Question 14

A data engineer is using AWS Database Migration Service (AWS DMS) to replicate ongoing changes from an on-premises Oracle database to an Amazon S3 bucket in near real time. The source table has a primary key and the database is configured for ARCHIVELOG mode. The engineer needs change data capture (CDC) to capture INSERT, UPDATE, and DELETE operations. Which AWS DMS task setting should be used to capture ongoing changes?

Accepted Answer

Set the migration type to 'full-load-and-cdc' and enable supplemental logging on the source table.. To capture ongoing changes from Oracle to S3 with AWS DMS, the task must be configured with migration type 'full-load-and-cdc' and supplemental logging must be enabled on the source. Supplemental logging provides the additional column data needed for UPDATE and DELETE operations. Without it, CDC cannot function correctly. The other options either use incorrect migration types or omit the critical supplemental logging requirement.

Answer

Set the migration type to 'full-load' and configure ongoing replication using Oracle GoldenGate.

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Set the migration type to 'cdc' only and rely on the Oracle redo logs without supplemental logging.

Answer

Set the migration type to 'full-load-and-cdc' and increase the commit rate to reduce latency.

Question 15

A data engineer is designing a data pipeline that processes streaming data. The pipeline must be able to handle duplicate records and ensure exactly-once processing semantics. Which THREE AWS services or features should the engineer consider? (Choose three.)

Accepted Answer

Amazon EMR with Apache Flink for exactly-once semantics.. Option A is correct because Amazon EMR running Apache Flink supports exactly-once state consistency via its checkpointing mechanism (distributed snapshots aligned with barriers), so a streaming pipeline built on Flink can guarantee exactly-once processing even with duplicate or replayed records. Option C is correct because Kinesis Data Streams assigns each record a unique sequence number within a shard, and consumers can use those sequence numbers (or the KCL's checkpointing) to detect and skip already-processed records, enabling deduplication and effectively-once consumption. Option E is correct because Kinesis Data Analytics for Apache Flink inherits Flink's exactly-once checkpointing and, when combined with idempotent sinks (e.g., writing to DynamoDB with conditional writes or to S3 with deterministic keys), prevents duplicate records from producing duplicate downstream effects. Option B is not appropriate because Kinesis Data Firehose delivers at-least-once and its automatic retries can actually introduce duplicates rather than deduplicate them. Option D is not appropriate because DynamoDB Streams provides change data capture with at-least-once delivery and no built-in deduplication or exactly-once guarantee for a streaming pipeline.

Answer

Amazon Kinesis Data Firehose with automatic retries.

Answer

Amazon DynamoDB Streams for change data capture.