DEA-C01 Practice Test 17 — 15 Questions

Question 1

A data engineer is designing a data store for a real-time leaderboard application that requires sub-millisecond read and write latency. The leaderboard stores scores for millions of users and needs to be sorted by score. Which AWS service should the engineer use?

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

Accepted Answer

Amazon ElastiCache for Redis with a sorted set. Amazon ElastiCache for Redis provides a sorted set data structure (ZADD/ZRANGE commands) that maintains elements ordered by a numeric score with O(log N) complexity for both writes and reads, enabling sub-millisecond latency for real-time leaderboard updates and queries. This is the only option purpose-built for in-memory, sorted, real-time leaderboards at scale.

Answer

Amazon RDS for PostgreSQL with an index on score

Answer

Amazon DynamoDB with a global secondary index on score

Answer

Amazon Neptune with a graph model

Question 2

A data engineer is designing a data ingestion pipeline for streaming data from IoT devices. The devices send JSON messages every second. The engineer needs to ingest the data with low latency and store it in Amazon S3 in Parquet format. Which TWO services should the engineer use together?

Accepted Answer

Amazon Kinesis Data Streams. The correct answers are C and E. Amazon Kinesis Data Streams (KDS) provides low-latency, real-time ingestion of streaming data from IoT devices, handling JSON messages sent every second. Amazon Kinesis Data Firehose (KDF) can then consume data from KDS, automatically convert it to Parquet format, and deliver it to Amazon S3. This combination meets the low-latency and Parquet conversion requirements without custom code. Option A (Lambda) could transform data but requires additional management and is not necessary for Parquet conversion as Firehose handles it natively. Option B (Athena) is a query service, not for ingestion. Option D (Glue) is batch-oriented and not suited for low-latency streaming.

Answer

AWS Lambda

Answer

Amazon Athena

Answer

AWS Glue

Question 3

A data engineer needs to store semi-structured data (JSON logs) from thousands of IoT devices. The data must be schema-less, highly scalable, and support low-latency queries by device ID and timestamp. Which AWS service should the engineer use?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the correct choice because it is a fully managed NoSQL key-value and document database that natively supports semi-structured JSON data, schema-less design, and automatic scaling. Its partition key (device ID) and sort key (timestamp) enable low-latency, single-millisecond queries by device ID and timestamp, making it ideal for high-throughput IoT log ingestion.

Answer

Amazon RDS for PostgreSQL

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Amazon Redshift

Answer

Amazon S3

Question 4

An e-commerce application uses Amazon ElastiCache for Redis to cache product catalog data. The cache currently uses lazy loading. The team wants to ensure that frequently accessed product data is always fresh. Which caching strategy should they implement?

Accepted Answer

Write-through caching. Write-through caching ensures that data is written to the cache simultaneously with the database, guaranteeing that frequently accessed product data is always fresh. This strategy eliminates stale reads by synchronously updating the cache on every write, which directly addresses the requirement for freshness without relying on expiration or lazy population.

Answer

Set a TTL of 5 minutes for all cached items

Answer

Use database read replicas to serve data

Answer

Lazy loading with TTL

Question 5

A company uses AWS Glue to process JSON logs from S3. The logs have a nested structure and the schema evolves over time. The data engineer needs to ensure the Glue job can handle schema changes without failing. Which configuration should be used?

Accepted Answer

Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true. Setting '--enable-glue-datacatalog' allows the Glue job to use the Data Catalog as the metastore, and '--mergeDynamicColumns' (or the equivalent '--enable-schema-evolution' in newer Glue versions) instructs the job to dynamically merge new columns from the evolving JSON schema into the existing table schema during runtime, preventing job failures due to schema mismatches. This is specifically designed for nested, schema-evolving data like JSON logs, as it automatically reconciles differences between the source data and the catalog definition.

Answer

Manually update the table schema in the Glue Data Catalog before each run

Answer

Use Spark SQL with a static schema definition in the script

Answer

Enable AWS Glue Schema Registry and define a schema version

Question 6

A company runs a production Amazon RDS for PostgreSQL database. The database is experiencing performance degradation due to a high number of concurrent read queries. The data engineer needs to improve read performance without significantly increasing costs. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Create one or more read replicas in the same region.. Creating one or more read replicas in the same region offloads read traffic from the primary DB instance, directly addressing the high number of concurrent read queries. Read replicas are asynchronous copies that can serve SELECT traffic, improving read throughput without increasing the cost of the primary instance. This is a cost-effective scaling strategy because you pay only for the replica instances and can choose smaller instance sizes if read workloads are bursty.

Answer

Enable Multi-AZ deployment for automatic failover.

Answer

Increase the allocated storage size to improve IOPS.

Answer

Delete unnecessary indexes to reduce write overhead.

Question 7

A data engineer needs to ingest data from an on-premises Oracle database into Amazon S3 on a daily basis. The data volume is approximately 500 GB per day. The source database is behind a firewall that does not allow direct internet access. Which service should the engineer use to transfer the data securely?

Accepted Answer

AWS DataSync with a network path through AWS Direct Connect or VPN.. AWS DataSync can transfer data from on-premises storage to AWS over a network path established through AWS Direct Connect or a VPN, which is necessary because the source database is behind a firewall that blocks direct internet access. DataSync automates the movement of large datasets (up to 500 GB daily) and integrates with Oracle databases via a supported agent, handling incremental transfers efficiently. This makes it the correct choice for secure, scheduled, and high-volume data ingestion into Amazon S3.

Answer

AWS Database Migration Service (AWS DMS) with ongoing replication from Oracle to S3.

Answer

Amazon S3 Transfer Acceleration with a public endpoint.

Answer

AWS Snowball Edge device for daily transfers.

Question 8

Refer to the exhibit. A data engineer is analyzing a query performance issue on an Amazon Redshift table. The table 'sales' has 100 million rows. The query is performing a full table scan. Which optimization should the engineer apply to improve query performance?

Accepted Answer

Use a compound sort key on (sale_date, region).. The query is performing a full table scan, which indicates that Redshift cannot efficiently prune blocks. A compound sort key on (sale_date, region) orders rows by sale_date first, then by region, allowing Redshift to skip large portions of data when filtering on the leading column (sale_date). This minimizes the number of blocks scanned and dramatically improves query performance for range-based or equality filters on the leading column.

Answer

Change DISTKEY to region.

Answer

Use an interleaved sort key on (sale_date, region).

Answer

Change DISTSTYLE to ALL.

Question 9

A data engineering team is using Amazon DynamoDB to store user session data for a web application. The application experiences sudden spikes in traffic, causing throttling on the DynamoDB table. The team wants to minimize throttling without over-provisioning read/write capacity. Which solution should the team implement?

Accepted Answer

Enable DynamoDB Accelerator (DAX) caching layer.. DynamoDB Accelerator (DAX) is a fully managed, in-memory cache that reduces read latency from single-digit milliseconds to microseconds. By caching frequently accessed items, DAX offloads read requests from the DynamoDB table, directly reducing the read capacity consumption and thus minimizing throttling during traffic spikes without requiring over-provisioning of read capacity units.

Answer

Enable DynamoDB Time to Live (TTL) to automatically delete expired items.

Answer

Disable auto scaling and manually set a high provisioned capacity.

Answer

Use Amazon RDS read replicas to offload read traffic.

Question 10

A data engineer needs to monitor the performance of an RDS for PostgreSQL database. Which THREE CloudWatch metrics are most useful for this purpose?

Accepted Answer

CPUUtilization. CPUUtilization is a critical metric for monitoring RDS for PostgreSQL because high CPU usage can indicate inefficient queries, insufficient instance size, or contention. Sustained high CPU can lead to performance degradation and increased query latency, making it essential for capacity planning and troubleshooting.

Answer

FreeStorageSpace

Answer

NetworkThroughput

Question 11

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a fleet of EC2 instances running a custom application that processes the records and writes to DynamoDB. The application is experiencing high latency and records are being processed slower than they are produced. The stream has 5 shards. Which action would MOST effectively improve processing speed?

Accepted Answer

Increase the number of shards in the Kinesis stream.. The bottleneck is the number of shards in the Kinesis stream. Each shard provides a fixed read capacity of 2 MB/s and 5 read transactions per second. With only 5 shards, the total read throughput is limited regardless of how many EC2 instances consume the data. Increasing the number of shards increases the total read capacity, allowing more records to be consumed in parallel and reducing processing latency.

Answer

Use the Kinesis Client Library (KCL) to automatically distribute shards among instances.

Answer

Increase the EC2 instance size to provide more CPU and memory.

Answer

Add more EC2 instances consuming from the same stream without changing shard count.

Question 12

A company uses Amazon Redshift for analytics. The data engineer notices that queries are slow and the system is experiencing high disk usage. The engineer suspects that the distribution style is suboptimal. Which action should the engineer take to improve query performance?

Accepted Answer

Use the DISTSTYLE AUTO setting and analyze query patterns to let Redshift choose.. DISTSTYLE AUTO allows Amazon Redshift to automatically assign distribution styles (KEY, EVEN, or ALL) based on query patterns and table size, optimizing data distribution for improved query performance. This is particularly effective when the engineer suspects suboptimal distribution but lacks detailed knowledge of the ideal key, as Redshift analyzes workload patterns to reduce data movement and disk usage.

Answer

Convert all tables to use SORTKEY on the most frequently filtered column.

Answer

Increase the number of nodes in the cluster to distribute data across more slices.

Answer

Set all tables to DISTSTYLE EVEN to distribute data evenly.

Question 13

A data engineer needs to grant an IAM user read-only access to a specific KMS key for decrypting S3 objects. Which policy element should be used?

Accepted Answer

Add a statement to the KMS key policy allowing the IAM user to call kms:Decrypt. KMS key policies are resource-based policies that directly grant permissions to IAM users or roles to perform actions on the key. To grant an IAM user read-only access (kms:Decrypt) to a specific KMS key, the recommended approach is to add a statement in the key policy that allows that user to call kms:Decrypt. Option A is incorrect because an IAM policy alone cannot grant KMS permissions unless the key policy explicitly allows it (by default, key policies must allow IAM policies). Option C is incorrect because bucket policies cannot grant kms:Decrypt permissions; they can only specify conditions for using KMS keys. Option D is incorrect because SCPs are used in AWS Organizations to set permission boundaries, not to grant permissions.

Answer

Attach an IAM policy to the user allowing kms:Decrypt

Answer

Add a bucket policy that grants kms:Decrypt

Answer

Use an SCP in AWS Organizations to allow kms:Decrypt

Question 14

A data engineer is ingesting data from an Amazon RDS for PostgreSQL database into Amazon S3 using AWS Glue. The Glue job reads the entire table each time it runs, which takes several hours. The team wants to reduce the job duration by reading only new or updated records. Which approach should the engineer adopt?

Accepted Answer

Enable job bookmarks in AWS Glue and use a column with timestamps as the bookmark key to read only incremental data.. AWS Glue job bookmarks track previously processed data using a specified column (e.g., a timestamp column) as the bookmark key. When enabled, the job reads only new or updated records since the last run, significantly reducing job duration by avoiding full table scans. This directly addresses the requirement to read incremental data from the PostgreSQL source.

Answer

Partition the table in the source database by date and read only the latest partition.

Answer

Increase the number of Glue workers to improve parallel reads.

Answer

Use Amazon Kinesis Data Streams to capture changes from PostgreSQL.

Question 15

A company uses AWS Glue to run ETL jobs that process data from Amazon RDS for MySQL and load it into Amazon S3. The job runs daily and processes incremental changes using the JDBC connection. Recently, the job has been failing with a 'Communications link failure' error. The RDS instance is in a private subnet. Which step should the engineer take first to diagnose the issue?

Accepted Answer

Check the security group and network ACL rules for the RDS instance and the Glue connection.. The 'Communications link failure' error typically indicates a network connectivity issue between AWS Glue and the RDS instance. Since the RDS instance is in a private subnet, the Glue job must be able to reach it via a VPC endpoint or a Glue connection that uses network configuration. Checking the security group (inbound rules for the RDS instance allowing traffic from Glue's elastic network interfaces) and network ACLs (ensuring ephemeral ports are open) is the first logical step to diagnose connectivity.

Answer

Verify that the IAM role used by Glue has the correct permissions to access RDS.

Answer

Change the Glue job type from Spark to Python shell.

Answer

Check that the JDBC driver is compatible with the Glue version.