DEA-C01 Multiple Select — 40 Questions

Question 1

A data engineer is designing a data ingestion pipeline for real-time clickstream data from a website. The data must be ingested with low latency (seconds) and made available for multiple consumer applications, including a dashboard that refreshes every minute and a machine learning model that processes data in near-real-time. The engineer needs to choose a streaming ingestion service. Which TWO services meet these requirements? (Select TWO.)

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

Accepted Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis Data Streams (C) provides sub-second ingestion latency and supports multiple consumer applications via its enhanced fan-out feature, enabling a dashboard and ML model to consume data concurrently with low latency. Amazon MSK (B) offers similar real-time capabilities with Apache Kafka's native pub/sub model, allowing multiple consumers to process the same stream independently and with low latency, meeting the near-real-time requirements.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon Simple Queue Service (SQS)

Answer

Amazon S3

Question 2

Which THREE of the following are benefits of using Amazon DynamoDB Accelerator (DAX)? (Choose three.)

Accepted Answer

Offloads read traffic from the DynamoDB table.. DAX acts as a read-through cache that offloads read traffic from the DynamoDB table, reducing the number of read requests that hit the underlying table and thus lowering the consumed read capacity units (RCUs). This allows the table to handle more concurrent reads without scaling up provisioned capacity.

Answer

Improves write throughput by batching writes.

Answer

Supports write-through caching to improve write performance.

Question 3

A company is migrating a legacy data warehouse to Amazon Redshift. They need to choose a distribution style to minimize data movement during joins. Which THREE factors should they consider?

Accepted Answer

The size of the table (number of rows).. Option A is correct because table size (number of rows) is a primary driver of distribution style choice: small tables are typically assigned ALL distribution so they can be broadcast to every node, eliminating data movement during joins, while large tables need KEY or EVEN distribution. Option B is correct because the whole purpose of choosing a distribution style is to co-locate matching rows on the same slice; if a table is frequently joined on a specific column, distributing both tables on that join key keeps the join local and avoids network redistribution. Option D is correct because fact and dimension tables play different roles in a star schema: large fact tables are usually distributed on their most common join key (or EVEN), while smaller dimension tables are often set to ALL so they are replicated to every node and can be joined without movement. Option C is not a factor because the number of columns does not affect how rows are distributed across slices; it only affects storage width and I/O, not join data movement. Option E is not a factor because Redshift supports distribution keys of various data types, and the data type itself does not determine whether a join requires redistribution — what matters is whether the joined columns match and are used as distribution keys.

Answer

The number of columns in the table.

Answer

The data type of the distribution key column.

Question 4

A data engineer is monitoring an Amazon RDS for PostgreSQL instance. The engineer wants to set up alerts for high CPU utilization and low free storage space. Which AWS services can be used together to achieve this? (Choose TWO.)

Accepted Answer

Amazon Simple Notification Service (SNS). Amazon CloudWatch [CORRECT] is the AWS monitoring service that collects RDS for PostgreSQL metrics such as CPUUtilization and FreeStorageSpace, and it lets you create alarms that trigger when those metrics cross defined thresholds. Amazon Simple Notification Service (SNS) [CORRECT] is the notification service that CloudWatch alarms publish to, delivering email, SMS, or other messages to the engineer when high CPU or low free storage is detected. Together, CloudWatch alarms plus an SNS topic form the standard alerting pipeline for RDS metric thresholds. AWS CloudTrail records API activity and audit events, not performance metrics, so it cannot detect high CPU or low storage. AWS Config evaluates resource configuration compliance and does not monitor runtime utilization metrics. Amazon Route 53 is a DNS and traffic-routing service and has no role in RDS metric alerting.

Answer

AWS CloudTrail

Answer

AWS Config

Answer

Amazon Route 53

Question 5

A data engineer is designing a data lake on Amazon S3 that will store sensitive financial data. The engineer needs to implement encryption at rest and ensure that only authorized users can access the data. Which TWO actions should the engineer take to meet these requirements? (Choose TWO.)

Accepted Answer

Configure a bucket policy that denies writes if the object is not encrypted.. A bucket policy with a condition that denies writes if the object is not encrypted (e.g., using `s3:x-amz-server-side-encryption` or `s3:PutObject` with `aws:SecureTransport`) enforces encryption at rest at the time of upload. This ensures that all objects written to the bucket are encrypted, meeting the encryption requirement without relying on client-side behavior.

Answer

Use server-side encryption with customer-provided keys (SSE-C).

Answer

Enable S3 Transfer Acceleration for the bucket.

Answer

Enable object-level access control lists (ACLs).

Question 6

Which TWO actions should a data engineer take to optimize Amazon S3 query performance for Amazon Athena when dealing with large Parquet files? (Choose 2.)

Accepted Answer

Optimize file sizes to be around 64 MB to 256 MB. Option D is correct because Athena's performance degrades with very large Parquet files since a single file can only be read by one split; targeting roughly 64 MB to 256 MB per file allows Athena's split-based parallel reads to distribute work across more nodes. Option E is correct because partitioning data by frequently filtered columns (e.g., date, region) enables partition pruning, so Athena scans only the relevant S3 prefixes instead of the entire dataset, drastically reducing bytes scanned and query time. Option A is wrong because a single large unpartitioned file forces full-table scans and prevents parallelism. Option B is wrong because Parquet already uses columnar compression internally, and GZIP is not splittable, which can actually hurt parallelism for large files. Option C is wrong because splitting into many tiny files creates excessive per-file overhead (metadata, open/close costs) and degrades Athena performance rather than improving it.

Answer

Store data in a single large file without partitioning

Answer

Use GZIP compression on the Parquet files

Answer

Split large files into many small files

Question 7

A data engineer is designing a real-time streaming pipeline to ingest clickstream data from a website into Amazon S3. The data must be transformed before storage. Which TWO AWS services can be used together to build this pipeline? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Firehose. The correct combination is Amazon Kinesis Data Streams to ingest the streaming clickstream data, and Amazon Kinesis Data Firehose to read from the stream, perform transformations (e.g., via Lambda), and deliver the transformed data to Amazon S3. Kinesis Data Streams provides the durable ingestion layer, while Kinesis Data Firehose handles the delivery and transformation without managing servers.

Answer

AWS Glue

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Database Migration Service (DMS)

Question 8

Which TWO AWS services can be used to ingest streaming data into Amazon S3? (Choose two.)

Accepted Answer

Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into Amazon S3. It can capture, transform, and deliver streaming data to S3 destinations in near real-time with no code required. Amazon MSK (Managed Streaming for Apache Kafka) can also ingest streaming data into S3 by using Kafka Connect with an S3 sink connector, which writes data from Kafka topics directly to S3.

Answer

Amazon S3 Transfer Acceleration

Answer

Amazon Elastic Block Store (Amazon EBS)

Answer

AWS Snowball

Question 9

A company is building a data lake on AWS and must encrypt data at rest. Which services can provide server-side encryption for data stored in Amazon S3? (Choose TWO.)

Accepted Answer

SSE-S3. SSE-S3 (Option A) is correct because Amazon S3 server-side encryption with S3-managed keys (AES-256) encrypts objects at rest automatically, with AWS managing the key material and rotation. SSE-KMS (Option E) is also correct because it performs server-side encryption at rest using AWS KMS customer master keys (CMKs), giving you control over key policies, auditing, and rotation. Both are S3 server-side encryption modes applied after data reaches S3, satisfying the data-at-rest requirement. SSL/TLS (Option B) is wrong because it only encrypts data in transit between the client and S3, not at rest. AWS SDK client-side encryption (Option C) is wrong because it encrypts data before it is sent to S3, so it is client-side, not server-side. AWS CloudHSM (Option D) is wrong because it is a dedicated hardware security module service for key storage and cryptographic operations, not an S3 server-side encryption option by itself.

Answer

SSL/TLS

Answer

AWS SDK client-side encryption

Answer

AWS CloudHSM

Question 10

A data engineer is building a data ingestion pipeline using AWS Glue. The source is an Amazon DynamoDB table, and the target is an Amazon S3 data lake in Parquet format. The pipeline must handle large volumes and ensure exactly-once processing. Which THREE features should the engineer use together to achieve this? (Choose THREE.)

Accepted Answer

Configure the Glue job to convert data to Parquet format.. Converting data to Parquet format is a core requirement for an S3 data lake, as Parquet offers columnar storage, compression, and efficient querying via services like Amazon Athena and Amazon Redshift Spectrum. AWS Glue natively supports Parquet as an output format, enabling the engineer to specify it in the job's output schema or transformation logic.

Answer

Use Amazon Kinesis Data Streams to capture DynamoDB Streams changes.

Answer

Use Amazon S3 Object Lambda to transform data on the fly.

Question 11

A data engineer is configuring a VPC for an Amazon Redshift cluster. The cluster must be accessible only from a specific on-premises network via a Direct Connect connection. Which TWO actions should the engineer take to meet this requirement? (Choose TWO.)

Accepted Answer

Configure a security group to allow inbound traffic from the on-premises CIDR block.. Option B is correct because a security group acts as the stateful firewall for the Redshift cluster, and adding an inbound rule that permits the on-premises CIDR block on the Redshift port (5439) is required to allow that specific network to reach the cluster. Option C is correct because the network ACL is the stateless subnet-level control, so it must also include an inbound rule allowing the on-premises CIDR block (and corresponding outbound return traffic) for the connection to succeed. Option A is not needed because Enhanced VPC Routing only affects how COPY/UNLOAD traffic is routed to S3 or other services, not client access from on-premises. Option D is wrong because a VPC endpoint is for private access to AWS services like S3, not for enabling on-premises clients to reach a Redshift cluster. Option E is wrong because making the cluster publicly accessible would expose it to the internet, violating the requirement to restrict access to the on-premises network only.

Answer

Enable Redshift Enhanced VPC Routing.

Answer

Create a VPC endpoint for Redshift.

Answer

Make the Redshift cluster publicly accessible.

Question 12

A data engineering team is building a data lake on Amazon S3. They need to ingest data from multiple sources: (1) streaming IoT data, (2) daily CSV exports from an on-premises system via SFTP, and (3) change data capture (CDC) from an Amazon Aurora database. Which THREE services should the team use to ingest these data sources?

Accepted Answer

Amazon Kinesis Data Streams for IoT data ingestion.. Amazon Kinesis Data Streams (A) is the right choice for streaming IoT data because it is a fully managed, scalable service designed for real-time ingestion of high-volume streaming records into S3 via Kinesis Data Firehose or consumers. AWS Database Migration Service (B) supports ongoing change data capture (CDC) from Amazon Aurora (and other relational databases), replicating inserts, updates, and deletes continuously, which matches the CDC requirement. AWS Transfer Family (C) provides a fully managed SFTP endpoint backed by Amazon S3, so the on-premises system can push daily CSV exports over SFTP directly into the data lake. AWS Glue ETL (D) is a batch/transform service, not a CDC ingestion mechanism, so it does not satisfy the Aurora CDC requirement. Amazon EMR (E) is a big data processing cluster, not a managed ingestion service for daily SFTP CSV files, so it is not the appropriate ingestion tool here.

Answer

AWS Glue ETL for CDC from Aurora.

Answer

Amazon EMR for daily CSV ingestion.

Question 13

A company is running a Redshift cluster and wants to improve query performance for a frequently used dashboard. Which THREE approaches are recommended?

Accepted Answer

Apply column compression encoding. Option B is correct because applying column compression encoding reduces the amount of data read from disk, which lowers I/O and speeds up scans for dashboard queries. Option C is correct because defining sort keys on columns used in WHERE clauses enables zone maps to skip irrelevant blocks, dramatically reducing the data scanned for filtered queries. Option E is correct because choosing an appropriate distribution key for large tables colocates related rows on the same node slice, minimizing data movement during joins and aggregations common in dashboards. Option A is not among the marked answers, and concurrency scaling mainly handles concurrent workload spikes rather than improving single-query performance. Option D is not marked because simply adding nodes increases capacity and cost but does not by itself optimize query execution without proper sort, distribution, and encoding design.

Answer

Enable concurrency scaling

Answer

Add more nodes to the cluster

Question 14

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 15

Which THREE factors should be considered when choosing a partition key for an Amazon DynamoDB table?

Accepted Answer

If the table has a write-heavy workload, the partition key should distribute writes evenly.. Option B is correct because a write-heavy workload requires the partition key to spread write traffic across many partitions; otherwise a hot partition throttles throughput, since each partition supports a limited write capacity (up to 1,000 WCU per partition). Option C is correct because DynamoDB retrieves items by partition key, so aligning the key with the most common query access pattern enables efficient Query operations instead of expensive Scan operations. Option E is correct because high cardinality produces many distinct partition key values, which distributes items and traffic evenly across partitions and avoids hot partitions. Option A is not a valid factor because item size does not determine partition key choice; DynamoDB partitions data by key value, and large items only consume more capacity, not improve partitioning. Option D is not a valid factor because RCU consumption is driven by item size and consistency model, not by selecting a partition key to minimize reads.

Answer

The partition key should be chosen to maximize the size of items in each partition.

Answer

The partition key should be chosen to minimize read capacity unit consumption.

Question 16

A data engineer needs to transfer 50 TB of data from an on-premises data center to Amazon S3 over a 1 Gbps network. The transfer must be completed within one week. Which TWO AWS services can be used for this task? (Choose TWO.)

Accepted Answer

AWS DataSync. AWS DataSync is correct because it is designed to efficiently transfer large datasets over the network using a purpose-built agent that parallelizes data transfer and optimizes network utilization. With a 1 Gbps link, DataSync can transfer 50 TB within a week by leveraging its built-in compression, encryption, and incremental transfer capabilities, making it suitable for this time-constrained migration.

Answer

AWS Glue

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Direct Connect

Question 17

A company is ingesting Apache logs from multiple web servers into AWS. The logs are sent via Amazon CloudWatch Logs to a subscription filter that delivers to a Lambda function. The Lambda function parses the logs and writes to Amazon S3. However, there is a significant backlog. Which THREE actions can reduce the backlog?

Accepted Answer

Increase the Lambda function memory allocation. Increasing the Lambda function's memory allocation also increases its CPU allocation, allowing the function to process each log event faster. This reduces the per-invocation processing time, enabling the function to handle more log data per unit time and thus reduce the backlog.

Answer

Route the CloudWatch Logs subscription to an Amazon SQS queue first

Answer

Change the Lambda function runtime from Python to Node.js

Question 18

A company is building a data lake on Amazon S3 and needs to ingest data from various on-premises sources. Which TWO AWS services can be used to transfer data securely over the internet?

Accepted Answer

AWS DataSync. AWS DataSync (B) is correct because it is a purpose-built data transfer service that moves data between on-premises storage and Amazon S3 over the internet using TLS encryption, and it supports scheduled, incremental, and bandwidth-limited transfers. AWS CLI (E) is correct because commands such as aws s3 cp or aws s3 sync transfer data from on-premises systems to S3 over the public internet using HTTPS, making it a valid secure internet-based ingestion method. AWS Snowcone (A) is not correct here because it is an edge computing and offline/physical data transfer device, not an internet-based transfer service. Amazon Kinesis Data Firehose (C) is not correct because it delivers streaming data to destinations like S3 but is not designed to pull files from on-premises sources over the internet. AWS Direct Connect (D) is not correct because it uses a dedicated private network connection rather than the public internet.

Answer

AWS Snowcone

Answer

Amazon Kinesis Data Firehose

Answer

AWS Direct Connect

Question 19

A data engineer is setting up a data pipeline using AWS Glue. The engineer wants to monitor job failures and receive notifications. Which TWO services can be used together for this purpose?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch (B) is correct because AWS Glue automatically publishes job metrics and logs to CloudWatch, and you can create CloudWatch alarms that trigger when a Glue job fails (for example, on the glue.driver.aggregate.numFailedTasks metric or a FAILED job run state). Amazon SNS (C) is correct because it is the notification service that CloudWatch alarms target via an SNS topic, delivering email, SMS, or HTTP notifications to subscribers when a Glue job failure alarm fires. Together, CloudWatch detects the failure and SNS delivers the alert, which is the standard AWS pattern for Glue job failure notifications. AWS Step Functions (A) can orchestrate Glue jobs but is not a monitoring/notification service, Amazon Kinesis Data Streams (D) is for real-time streaming ingestion, and Amazon SQS (E) is a message queue for decoupling applications, so none of these provide the alarm-and-notify capability required here.

Answer

AWS Step Functions

Answer

Amazon Kinesis Data Streams

Answer

Amazon SQS

Question 20

A data engineer needs to ingest data from a SaaS application that sends webhooks in JSON format. The data must be stored in S3 for batch analysis. Which AWS services can receive the webhooks and store the data in S3 with minimal custom code? (Choose TWO.)

Accepted Answer

Amazon API Gateway with S3 integration. Amazon API Gateway with S3 integration (Option D) allows you to create a REST API that directly writes incoming webhook payloads to an S3 bucket without any custom code, using a proxy integration with an AWS service. This minimizes custom code because the integration handles the mapping and storage automatically.

Answer

AWS Lambda with S3 SDK

Answer

AWS Glue with a Python shell job

Answer

Amazon Kinesis Data Streams