DEA-C01 Mock Exam 81 — 25 Questions

Question 1

A company runs a real-time analytics platform using Amazon Kinesis Data Streams. The data is consumed by multiple consumers: one for real-time dashboard (using Lambda) and one for long-term storage (using Firehose to S3). The Kinesis stream has 10 shards. Each record is 1 KB, and the total incoming data rate is 5 MB/s. The Lambda consumer is falling behind and processing latency exceeds 10 seconds. Which TWO actions should be taken to resolve the issue?

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

Accepted Answer

Increase the number of shards to 20. Increasing the number of shards from 10 to 20 doubles the stream's read capacity, allowing the Lambda consumer to poll more data per second and reduce backlog. Option C is correct because enabling enhanced fan-out provides each consumer with a dedicated 2 MB/s read throughput per shard, eliminating contention between the Lambda consumer and the Firehose consumer, which is critical when multiple consumers read from the same stream.

Answer

Increase the Lambda function's memory allocation

Answer

Switch to using Kinesis Client Library (KCL) instead of Lambda

Answer

Decrease the batch size in the Lambda event source mapping

Question 2

A data engineer needs to restrict access to an Amazon S3 bucket so that only objects encrypted with a specific AWS KMS key can be uploaded. Which S3 bucket policy condition should be used?

Accepted Answer

s3:x-amz-server-side-encryption-aws-kms-key-id. The correct condition is s3:x-amz-server-side-encryption-aws-kms-key-id (option A). This condition key allows you to require that objects uploaded to the S3 bucket are encrypted with a specific AWS KMS key by checking the key ID used in the encryption header. Option B (kms:ViaService) restricts KMS key usage to specific AWS services but does not enforce a key ID on S3 objects. Option C (s3:x-amz-server-side-encryption) only checks whether server-side encryption is enabled, not the specific key. Option D (kms:EncryptionContext) is used to enforce encryption context, not the key ID.

Answer

kms:ViaService

Answer

s3:x-amz-server-side-encryption

Answer

kms:EncryptionContext

Question 3

A data engineer needs to store semi-structured JSON log files from multiple sources and query them using SQL. The data is rarely updated and access frequency is low. Which storage solution is MOST cost-effective?

Accepted Answer

Amazon S3 with Amazon Athena for querying.. Amazon S3 with Athena is the most cost-effective solution because the data is semi-structured JSON, rarely updated, and accessed infrequently. S3 provides low-cost storage for static data, and Athena uses a serverless, pay-per-query model, eliminating the need for a running cluster or provisioned capacity. This combination avoids the fixed costs of Redshift, DynamoDB, or RDS, making it ideal for low-frequency SQL querying of archival logs.

Answer

Amazon Redshift with JSON ingestion and compression.

Answer

Amazon DynamoDB with JSON documents.

Answer

Amazon RDS for PostgreSQL with JSONB columns.

Question 4

A company uses AWS Glue to process data in Amazon S3. The Glue job fails with an error indicating that the partition keys in the catalog do not match the actual S3 partition structure. What is the most likely cause?

Accepted Answer

The Glue Data Catalog partition metadata is outdated after the S3 structure changed. The Glue Data Catalog stores partition metadata separately from the actual S3 partition layout. When the S3 partition structure changes (e.g., new partitions are added or existing ones are renamed) without updating the catalog, the Glue job reads stale partition metadata, leading to a mismatch error. The job fails because it expects partitions based on the catalog, not the live S3 structure.

Answer

The IAM role does not have permissions to read the S3 data

Answer

The data files are encrypted with SSE-KMS

Answer

The table name in the catalog is different from the one used in the job

Question 5

A data engineer needs to design a data ingestion pipeline that ingests CSV files from an Amazon S3 bucket, transforms the data by adding a timestamp column, and loads it into an Amazon Redshift table. The pipeline should run automatically whenever a new file is uploaded to the S3 bucket. Which AWS service should be used to trigger the transformation?

Accepted Answer

AWS Lambda. Amazon S3 can be configured to send events directly to AWS Lambda when a new CSV file is uploaded. Lambda then executes the transformation (adding a timestamp column) and loads the data into Redshift. Option B (Step Functions) is not triggered directly by S3 events without an intermediate service like Lambda. Option C (EventBridge) can route S3 events to Lambda, but a direct S3 event notification to Lambda is simpler and more common; however, the key point is that Lambda is the direct trigger. Option D (SQS) requires a separate process to poll the queue and invoke Lambda; it is not a direct trigger.

Answer

AWS Step Functions

Answer

Amazon EventBridge

Answer

Amazon Simple Queue Service (SQS)

Question 6

A company wants to ensure that an IAM user can only launch Amazon EC2 instances of a specific instance type. Which THREE IAM policy elements are required to define this permission? (Choose THREE.)

Accepted Answer

Action. To define a permission in an IAM policy, the required elements are Effect (allow/deny), Action (e.g., ec2:RunInstances), and Resource (e.g., '*'). While a Condition can further restrict the permission (e.g., to a specific instance type), it is not mandatory. Therefore, the three required elements are Effect, Action, and Resource.

Answer

Principal

Answer

Condition

Question 7

A data engineering team is designing a data lake on Amazon S3 with a folder structure that separates raw, transformed, and curated data. The team needs to implement lifecycle policies to minimize storage costs while ensuring that data in the 'raw' zone is retained for 90 days before being moved to Amazon S3 Glacier Deep Archive. Additionally, data in the 'curated' zone should be deleted after 365 days. What is the MOST cost-effective way to achieve these requirements?

Accepted Answer

Create a single lifecycle policy with two rules: one to transition raw zone objects to S3 Glacier Deep Archive after 90 days, and another to delete curated zone objects after 365 days.. A single S3 lifecycle policy can contain multiple rules, each applying to different prefixes. This allows you to transition raw zone objects to S3 Glacier Deep Archive after 90 days and delete curated zone objects after 365 days, minimizing storage costs without needing separate policies. S3 lifecycle policies are evaluated per object based on creation date, and using one policy reduces management overhead.

Answer

Configure S3 Intelligent-Tiering on both prefixes with automatic archiving to Glacier Deep Archive after 90 days in raw and deletion after 365 days in curated.

Answer

Create separate lifecycle policies for each prefix: one to transition raw to S3 Glacier Deep Archive after 90 days, another to delete curated after 365 days.

Answer

Create a lifecycle policy that transitions raw zone data to S3 Standard-IA after 90 days and deletes curated zone data after 365 days.

Question 8

A company needs to transfer 50 TB of historical data from an on-premises HDFS cluster to Amazon S3. The network bandwidth is limited to 1 Gbps, and the transfer must complete within 10 days. The data is compressible. Which solution is MOST appropriate?

Accepted Answer

Use AWS DataSync over a Direct Connect connection.. With a 1 Gbps link, transferring 50 TB would take approximately 111 hours (about 4.6 days) theoretically, well within the 10-day window. AWS DataSync over a Direct Connect connection provides a high-speed, secure, and reliable method to transfer large datasets online. DataSync automates the HDFS-to-S3 transfer, handles compression, and can saturate the 1 Gbps link, making it the most appropriate solution. Snowball Edge involves physical shipment and logistics, which would likely exceed the 10-day deadline. Options B and C also rely on internet bandwidth and are less efficient than DataSync.

Answer

Use Amazon S3 Transfer Acceleration with multipart uploads.

Answer

Use multiple parallel AWS CLI sync commands over the internet.

Answer

Use AWS Snowball Edge to physically ship the data.

Question 9

Refer to the exhibit. A data engineer applies this S3 bucket policy to an S3 bucket. What is the effect of this policy?

Accepted Answer

Allows only HTTPS requests to get and put objects, and denies HTTP requests.. The bucket policy allows GetObject and PutObject actions only when the request uses HTTPS, and explicitly denies all S3 actions when the request uses HTTP due to the condition `aws:SecureTransport=false`. Therefore, only HTTPS requests for Get and Put are permitted. Option A is incorrect because the policy does not restrict by IP addresses. Option C is incorrect because both Get and Put are allowed over HTTPS, not just Get. Option D is incorrect because the policy does not grant anonymous access; it requires secure transport and does not allow HTTP.

Answer

Allows access only from specific IP addresses.

Answer

Allows only GetObject actions over HTTPS.

Answer

Allows anonymous access to get and put objects over HTTP.

Question 10

A company is using AWS Glue to catalog data in Amazon S3. The data is in CSV format with varying schemas. The Data Engineering team wants to ensure the Glue Data Catalog is updated automatically when new partitions are added to S3. Which TWO actions should be taken? (Choose two.)

Accepted Answer

Enable partition indexing on the Glue Data Catalog.. A is correct because enabling partition indexing in the Glue Data Catalog allows partition pruning and automatic updates. C is correct because configuring a Glue crawler with a schedule will automatically discover new partitions. B is wrong because setting up an S3 event notification to trigger Lambda for manual updates is not as efficient as crawler scheduling. D is wrong because using AWS Glue ETL jobs to update the catalog is not automatic. E is wrong because Amazon Athena does not update the catalog.

Answer

Set up an S3 event notification to trigger a Lambda function that updates the Glue Data Catalog.

Answer

Run Amazon Athena queries with MSCK REPAIR TABLE to add partitions.

Answer

Use AWS Glue ETL jobs to write data and update the catalog simultaneously.

Question 11

A company runs an AWS Glue ETL job that reads data from Amazon S3, transforms it, and writes back to S3 in a different partition structure. The job uses the 'spark.sql.shuffle.partitions' option set to 200. After the job completes, the output has many small files. The data engineer wants to minimize the number of output files while maintaining job performance. Which action should the engineer take?

Accepted Answer

Use 'coalesce(n)' with n based on target file size (e.g., 128 MB) before writing.. 'coalesce(n)' reduces the number of partitions without triggering a full shuffle, allowing you to control the number of output files based on a target file size (e.g., 128 MB). This minimizes small files while preserving job performance, as coalesce is a narrow transformation that avoids the overhead of a shuffle. In contrast, 'repartition(n)' would cause a full shuffle, degrading performance.

Answer

Enable S3 multipart upload for the Glue job.

Answer

Increase the 'spark.sql.shuffle.partitions' to 500.

Answer

Reduce the 'spark.sql.shuffle.partitions' to 50.

Question 12

A data engineer is designing a data store for a real-time analytics application that requires sub-millisecond read and write latency. The data is key-value in nature and the workload is both read-heavy and write-heavy. Which AWS service is most suitable?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the most suitable service because it is a fully managed NoSQL key-value and document database designed for single-digit millisecond latency at any scale, and with features like DynamoDB Accelerator (DAX) it can achieve sub-millisecond read latency. It supports both read-heavy and write-heavy workloads through its distributed architecture and auto-scaling capabilities, making it ideal for real-time analytics applications.

Answer

Amazon ElastiCache for Redis

Answer

Amazon RDS for MySQL

Answer

Amazon S3

Question 13

A data engineer is troubleshooting an AWS Glue job that reads from Amazon RDS MySQL and writes to Amazon S3. The job runs successfully but takes longer than expected. The engineer wants to optimize performance. Which THREE actions would improve job performance?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Increasing the number of DPUs (Data Processing Units) allocated to the AWS Glue job provides more parallel processing capacity, allowing the job to process data faster. This is a direct way to improve performance when the job is CPU or memory-bound, as Glue distributes the workload across the allocated DPUs.

Answer

Use a single JDBC connection per partition.

Answer

Convert the output format from Parquet to CSV.

Question 14

A company uses Amazon Kinesis Data Firehose to deliver data to an S3 bucket. The data must be delivered within 60 seconds of ingestion. Currently, the delivery takes 3 minutes due to large buffer sizes. How should the engineer adjust the Firehose configuration?

Accepted Answer

Decrease the buffer interval to 60 seconds.. Amazon Kinesis Data Firehose delivers data to S3 based on either a buffer size threshold or a buffer interval (in seconds), whichever is reached first. To ensure delivery within 60 seconds, you must decrease the buffer interval to 60 seconds, which forces Firehose to flush data to S3 every 60 seconds regardless of buffer size. The current 3-minute delay is caused by the buffer interval being larger than 60 seconds, so reducing it directly meets the requirement.

Answer

Increase the buffer interval to 120 seconds.

Answer

Increase the buffer size to 128 MB.

Answer

Decrease the buffer size to 1 MB.

Question 15

A company uses Amazon Kinesis Data Streams to ingest clickstream data. The data is consumed by an AWS Lambda function that processes each record and writes to an Amazon DynamoDB table. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' from DynamoDB. The Lambda function uses the AWS SDK to batch write items in batches of 25. The DynamoDB table has on-demand capacity mode. The stream has 10 shards, and the Lambda function is configured with a batch size of 100 and 5 concurrent invocations per shard. What step should the team take to resolve the issue?

Accepted Answer

Reduce the Lambda batch size to 25 and implement exponential backoff with jitter in the Lambda code.. The issue is DynamoDB throttling due to high write traffic from Lambda. The DynamoDB table is on-demand, which can throttle if bursts exceed sustained limits. Reducing the Lambda batch size from 100 to 25 decreases the number of records processed per invocation, lowering the instantaneous write rate. Implementing exponential backoff with jitter in the Lambda code allows retries on throttled requests, making the system more resilient. Option A is not required because on-demand mode automatically scales, and switching to provisioned can be costly. Option C is incorrect because increasing shards would increase parallelism and worsen throttling. Option D is incorrect because increasing concurrency would also increase write pressure on DynamoDB.

Answer

Switch the DynamoDB table from on-demand to provisioned capacity with a high write capacity unit (WCU) value.

Answer

Increase the number of Kinesis shards to 20 to reduce the load per shard.

Answer

Increase the Lambda function's reserved concurrency to allow more parallel executions.

Question 16

Which TWO AWS services can be used to ingest streaming data from a mobile application into Amazon S3 for near-real-time analytics? (Choose 2.)

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to load streaming data directly into Amazon S3, Redshift, Elasticsearch, or Splunk without requiring custom code. It can capture and transform streaming data from mobile applications in near-real-time and automatically deliver it to S3, making it ideal for near-real-time analytics pipelines.

Answer

Amazon DynamoDB Streams

Answer

AWS Glue

Answer

Amazon SQS

Question 17

A company is using Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) for streaming data processing. The application is experiencing high latency and the data engineer wants to improve performance. Which THREE actions should the engineer consider? (Choose three.)

Accepted Answer

Use a larger Kinesis data stream with more shards.. Options A, C, and D are correct. Increasing the number of shards in the Kinesis stream (A) increases the ingestion capacity, which can reduce bottlenecks and lower latency. Increasing Flink parallelism (C and D) allows more parallel processing of data, improving throughput and reducing latency. Option B is incorrect because decreasing the buffer time in Flink can lead to increased network overhead and may not improve overall performance; it can actually increase latency due to more frequent data transfers. Option E is incorrect because decreasing the checkpoint interval increases the frequency of state snapshots, adding overhead and potentially increasing latency rather than reducing it.

Answer

Decrease the buffer time in the Flink application to reduce latency.

Answer

Decrease the checkpoint interval to reduce state size.

Question 18

A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data must be stored in Amazon S3 in near real-time with minimal overhead. Which THREE steps should the data engineer take to achieve this? (Choose THREE.)

Accepted Answer

Configure a Lambda function to transform data before delivery to S3.. A Lambda function can be used as a data transformation step within a Kinesis Data Firehose delivery stream. This allows the clickstream data to be transformed (e.g., parsed, enriched, or reformatted) before being delivered to Amazon S3, enabling near real-time storage with minimal operational overhead.

Answer

Use Kinesis Data Analytics to process and store data in S3.

Answer

Enable S3 cross-region replication for the destination bucket.

Question 19

A data engineer runs an AWS Glue Crawler that updates a table in the AWS Glue Data Catalog. The table is used by Amazon Athena queries. After the crawler runs, some queries start failing with the error 'HIVE_CANNOT_OPEN_SPLIT'. What is the most likely cause?

Accepted Answer

The crawler updated the schema and the partition metadata is inconsistent with the actual data.. The error 'HIVE_CANNOT_OPEN_SPLIT' occurs when Athena attempts to read a partition but the metadata in the Glue Data Catalog does not match the actual data in S3. This is commonly caused by running an AWS Glue Crawler that updates the schema of a table (e.g., adding, removing, or changing column types) without updating the partition metadata accordingly. The crawler may update the table schema but leave existing partition metadata unchanged, leading to a mismatch. Option A correctly identifies this cause. Option B is incorrect because a permissions issue would result in an access denied error, not a split error. Option C is incorrect because while Athena has a limit on the number of partitions per table (currently 20,000), this limit would cause a different error or query failure, not 'HIVE_CANNOT_OPEN_SPLIT'. Option D is incorrect because concurrent queries can cause throttling or other issues but not this specific split error.

Answer

The crawler does not have IAM permissions to read the S3 location.

Answer

The crawler created too many partitions, exceeding the Athena limit.

Answer

There are concurrent queries accessing the same table.

Question 20

A data engineer is designing a data pipeline that uses AWS Glue to transform data stored in Amazon S3. The transformation logic must be written in Python and should handle schema evolution automatically. Which THREE features or configurations should the engineer use? (Select THREE.)

Accepted Answer

Use `applyMapping` transformations. Correct options: B, D, E. AWS Glue DynamicFrames (E) handle schema evolution automatically by allowing schema on read and accommodating changes in data structure. Schema detection in the Glue job (D) enables the job to infer the schema from the data, which is essential for handling evolving schemas. Using `applyMapping` (B) provides explicit control over schema transformations and can be combined with DynamicFrames to manage schema changes. Option A (scheduling a Glue crawler) is meant for updating the Data Catalog, not for within-job schema evolution. Option C (Spark SQL) does not inherently handle schema evolution; it relies on static schemas.

Answer

Schedule a Glue crawler to update the schema

Answer

Use Spark SQL for transformations