DEA-C01 Practice Questions 1 — 50 Questions

Question 1

An e-commerce company uses Amazon DynamoDB as the primary data store for its product catalog. The table has a simple primary key (ProductID) and handles 10,000 writes per second during peak hours. Recently, the engineering team noticed increased write latency and throttled requests during peak times. The table's provisioned write capacity is set to 12,000 WCU. What is the most likely cause of the throttling?

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

Accepted Answer

Write traffic is unevenly distributed across partitions. DynamoDB partitions data by the primary key's hash value. If write traffic is unevenly distributed across partitions (e.g., a few ProductIDs receive most writes), those hot partitions can exceed their individual throughput limits (3,000 WCU per partition for provisioned tables), causing throttling even when the table's total provisioned WCU of 12,000 is not fully utilized.

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The table has reached the maximum number of partitions

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DynamoDB Accelerator (DAX) is not configured

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A global secondary index is consuming write capacity

Question 2

A data engineer is designing a data lake on Amazon S3. Which feature should be used to manage the lifecycle of objects and move them to cheaper storage classes automatically?

Accepted Answer

S3 Lifecycle policies. S3 Lifecycle policies are the correct feature for automatically managing object lifecycles and transitioning objects to cheaper storage classes (e.g., from S3 Standard to S3 Glacier Deep Archive) based on age or other rules. This directly meets the requirement to move objects to cost-optimized storage without manual intervention.

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S3 Object Lock

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S3 Storage Class Analysis

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S3 Inventory

Question 3

A company needs to ingest data from an on-premises database to Amazon S3 with minimal impact on the source database. The data volume is several TB. Which AWS service is best suited for this task?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is best suited because it can continuously replicate data from an on-premises database to Amazon S3 with minimal impact on the source. DMS uses change data capture (CDC) to capture only incremental changes after an initial full load, avoiding heavy read loads on the source database. This makes it ideal for migrating several TB of data while keeping the source operational.

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AWS Direct Connect

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AWS Snowball Edge

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Amazon S3 Transfer Acceleration

Question 4

Order the steps to migrate an on-premises database to Amazon RDS using AWS DMS.

Accepted Answer

Step 1: Create a replication instance. Step 2: Configure source and target endpoints. Step 3: Create a migration task. Step 4: Start the migration task. Step 5: Validate the migrated data.. First, create the replication instance. Then configure endpoints, create the migration task, start it, and finally validate the migrated data.

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Step 1: Configure source and target endpoints. Step 2: Create a replication instance. Step 3: Create a migration task. Step 4: Start the migration task. Step 5: Validate the migrated data.

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Step 1: Create a replication instance. Step 2: Create a migration task. Step 3: Configure source and target endpoints. Step 4: Start the migration task. Step 5: Validate the migrated data.

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Step 1: Create a replication instance. Step 2: Configure source and target endpoints. Step 3: Start the migration task. Step 4: Create a migration task. Step 5: Validate the migrated data.

Question 5

A data engineer needs to ingest on-premises CSV files into Amazon S3 every hour. The files are less than 1 GB each. Which service is the most cost-effective and requires the least operational overhead?

Accepted Answer

AWS DataSync. AWS DataSync is the most cost-effective and least overhead option for scheduled, recurring transfers of on-premises CSV files to S3. It provides a simple agent-based setup, supports hourly scheduling, and handles files under 1GB efficiently without complex configuration. In contrast, Amazon Kinesis Data Firehose is designed for streaming data ingestion, not batch file transfers; AWS Snowball Edge is intended for large-scale offline data migrations, not hourly incremental transfers; and AWS Database Migration Service (DMS) is specialized for migrating databases, not file transfers.

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Amazon Kinesis Data Firehose

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AWS Snowball Edge

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AWS Database Migration Service (DMS)

Question 6

A company is using Amazon S3 to store sensitive customer data. The security team requires that all data be encrypted in transit and at rest. Additionally, they want to prevent any accidental public access. Which combination of actions should the data engineer take?

Accepted Answer

Enable default encryption with SSE-S3, enforce HTTPS only via bucket policy, and enable S3 Block Public Access.. Enabling default encryption with SSE-S3 ensures data is encrypted at rest automatically, enforcing HTTPS only via bucket policy ensures encryption in transit by rejecting HTTP requests, and enabling S3 Block Public Access prevents any accidental public exposure regardless of bucket policies or ACLs. This combination satisfies all security requirements: encryption in transit, encryption at rest, and prevention of public access.

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Enable default encryption with SSE-KMS, allow both HTTP and HTTPS, and set bucket ACLs to private.

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Use client-side encryption, enforce HTTPS via bucket policy, and enable S3 Block Public Access.

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Enable default encryption with SSE-S3, allow HTTP and HTTPS, and use bucket ACLs to block public access.

Question 7

A company is using AWS DMS to replicate data from an on-premises Oracle database to Amazon RDS for MySQL. The replication is working, but the target table has a different schema. Which DMS feature should be used to transform the source schema to match the target?

Accepted Answer

Use DMS transformation rules. AWS DMS transformation rules allow you to modify the schema, table, or column names and data types during the migration process. This feature is specifically designed to handle schema transformations within the DMS task itself, enabling you to map the source Oracle schema to the target MySQL schema without external tools or services.

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Use AWS Schema Conversion Tool (SCT)

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Use AWS Glue ETL jobs

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Use AWS Lambda triggers

Question 8

An IAM policy includes the above resource ARN for CloudWatch Logs. A data engineer needs to allow a Lambda function to create log streams and put logs to the log group 'my-log-group'. However, the Lambda function is failing with access denied. What is the issue?

Accepted Answer

The ARN does not include the log-stream portion.. The ARN provided in the policy is for the log group itself (arn:aws:logs:region:account-id:log-group:my-log-group), but CloudWatch Logs requires separate permissions for the log-stream subresource. To allow a Lambda function to create log streams and put logs, the ARN must include the log-stream portion, typically with a wildcard like arn:aws:logs:region:account-id:log-group:my-log-group:log-stream:*. Without this, the IAM policy does not grant access to the log-stream actions (CreateLogStream, PutLogEvents), causing an access denied error even if the log group ARN is correct.

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The region in the ARN does not match the Lambda function's region.

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The Lambda function does not have an execution role.

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The ARN is incorrectly formatted because of the wildcard.

Question 9

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.)

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.

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Amazon Kinesis Data Firehose

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Amazon Simple Queue Service (SQS)

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Amazon S3

Question 10

A company uses AWS Glue to run ETL jobs daily. The data is stored in S3 as Parquet files partitioned by date. Recently, jobs have failed with the error 'No such file or directory' for certain partitions. What is the MOST likely cause?

Accepted Answer

A partition folder was deleted or not created by the upstream process.. The error 'No such file or directory' indicates that the Glue ETL job is attempting to read a specific S3 partition path that does not exist. Since the data is partitioned by date and the job runs daily, the most likely cause is that the upstream process failed to create or accidentally deleted the partition folder for that date. Glue's dynamic frame or Spark DataFrame will throw this error when it tries to list or read files from a missing prefix.

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The schema has changed and Glue cannot parse the data.

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The files are compressed with an unsupported codec.

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The IAM role does not have s3:GetObject permission.

Question 11

A data engineering team notices that an AWS Glue ETL job, which processes hourly data from an S3 bucket, is taking progressively longer to run. The job reads Parquet files partitioned by date and hour. Which action is MOST likely to improve the job's performance?

Accepted Answer

Enable pushdown predicate filtering on the job's data source.. Pushdown predicate filtering allows AWS Glue to push filter conditions down to the data source, so only the relevant partitions and rows are read from S3. Since the data is partitioned by date and hour, predicate pushdown minimizes the amount of data scanned, reducing I/O and improving job performance. This is especially effective for Parquet, which supports columnar pruning and predicate pushdown.

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Convert Parquet files to CSV to improve read performance.

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Increase the number of DPUs for the job.

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Switch from Spark to Python shell for simpler processing.

Question 12

A company uses an Amazon RDS for MySQL DB instance with Multi-AZ deployment. The primary DB instance fails unexpectedly. What happens to the database endpoint?

Accepted Answer

The existing endpoint continues to work and automatically points to the standby DB instance.. In a Multi-AZ RDS deployment, the DNS endpoint remains unchanged during a failover. When the primary DB instance fails, Amazon RDS automatically updates the DNS record to point to the standby instance in the other Availability Zone. This ensures the application can continue using the same endpoint without any manual intervention, providing high availability.

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A new endpoint is created for the standby and the application must use the new endpoint.

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The database becomes unavailable until the primary is restored from a snapshot.

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The existing endpoint is deleted and a new endpoint is provided after manual DNS update.

Question 13

A data engineer is troubleshooting an issue where an Amazon Redshift query returns an error: 'ERROR:  permission denied for relation table_name'. The user has been granted SELECT on the table. What is the most likely cause?

Accepted Answer

The user does not have USAGE permission on the schema.. In Amazon Redshift, to access a table, a user must have USAGE permission on the schema containing the table, in addition to SELECT or other table-level permissions. Without USAGE on the schema, the user receives a 'permission denied for relation' error even if SELECT is granted. Option D is correct. Option A (session timeout) would cause a different error or disconnection. Option B (no CONNECT permission) would prevent connecting to the database. Option C (wrong schema) would result in a 'schema not found' error, not a permission denied error.

Answer

The user's session has timed out.

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The user does not have CONNECT permission on the database.

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The table is in a different schema than expected.

Question 14

A company has an Amazon RDS for MySQL database that is experiencing performance issues due to a large number of read requests. The application is read-heavy and can tolerate eventually consistent reads. Which action will reduce the load on the primary database with the least operational overhead?

Accepted Answer

Create a read replica in the same region. Creating a read replica in the same region offloads read traffic from the primary RDS instance to a read-only copy, which directly addresses the read-heavy workload. Since the application can tolerate eventually consistent reads, the slight replication lag is acceptable, and this solution requires minimal operational overhead—just a few clicks in the AWS console or a single API call—without any application code changes.

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Use Amazon ElastiCache for caching

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Enable Multi-AZ deployment

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Increase the instance size of the primary DB

Question 15

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.

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Improves write throughput by batching writes.

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Supports write-through caching to improve write performance.

Question 16

A data engineer needs to encrypt data in transit between an Amazon RDS for MySQL instance and an application. Which solution should be used?

Accepted Answer

Use SSL/TLS to connect to the RDS instance. Encryption in transit for an RDS for MySQL instance is achieved by connecting with SSL/TLS, which encrypts the wire protocol between the application and the database. RDS MySQL supports TLS and provides an rds-ca certificate that the client uses to verify the server. Enabling SSL/TLS on the connection is the direct, correct answer for data in transit.

Answer

Enable encryption at rest using AWS KMS

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Store the data in Amazon S3 with server-side encryption

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Use AWS CloudHSM to generate and store encryption keys

Question 17

Refer to the exhibit. A data engineer has attached this bucket policy to an S3 bucket named data-lake-bucket. The engineer wants to allow only GET requests from the corporate network (10.0.0.0/16) over HTTPS. However, users report that they cannot access objects even when connected to the corporate network. What is the issue?

Accepted Answer

The Deny statement blocks all requests that are not using HTTPS, including those from the corporate network.. The Deny statement with `aws:SecureTransport` set to `false` blocks all HTTP requests. Since the Allow statement only permits GET requests from the corporate network (10.0.0.0/16) but does not require HTTPS, any request from that network that uses HTTP is denied by the explicit Deny. The Deny statement overrides the Allow, so even legitimate corporate users are blocked if they use HTTP.

Answer

The Deny statement should include a condition on the source IP.

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The Allow statement should include a condition for SecureTransport.

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The Allow statement should specify s3:GetObject instead of s3:GetObject.

Question 18

A company is using AWS Glue to catalog data stored in Amazon S3. The data is partitioned by year, month, and day. A data analyst reports that new partitions are not automatically discovered by the Glue crawler. The crawler runs on a schedule every hour. What is the MOST likely reason for the missing partitions?

Accepted Answer

The IAM role used by the crawler does not have permission to list the S3 bucket.. For a Glue crawler to discover new partitions in S3, its IAM role must have s3:ListBucket (and GetObject) permissions on the bucket and prefix. If the role lacks ListBucket, the crawler cannot enumerate the partition folders and will silently miss new partitions even though it runs on schedule. This is the most common cause of 'crawler runs but doesn't find new data' issues.

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The Glue Data Catalog is not configured to use a Hive metastore.

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The number of partitions exceeds the Glue catalog limit of 100,000.

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The crawler schedule is set to run too frequently.

Question 19

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.

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The data type of the distribution key column.

Question 20

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by a custom consumer application that writes to Amazon S3 every 5 minutes. The consumer is falling behind and processing lag is increasing. Which action is MOST effective to reduce the lag?

Accepted Answer

Increase the number of shards in the Kinesis stream. The consumer is falling behind because the stream's throughput capacity is insufficient for the incoming data volume. Increasing the number of shards in the Kinesis stream directly increases the total read capacity (each shard provides 2 MB/s read throughput and 5 transactions/second), allowing the consumer to process more data in parallel and reduce lag.

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Switch to Amazon Kinesis Data Firehose to deliver data directly to S3

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Increase the batch size of records written to S3

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Reduce the retention period of the stream