DEA-C01 Practice Test 21 — 15 Questions

Question 1

A data engineer needs to store semi-structured JSON logs from multiple microservices in a cost-effective manner for ad-hoc querying using SQL. Which AWS service should be used?

Accepted Answer

Amazon Athena with data in S3. Amazon Athena is the correct choice because it allows you to query semi-structured JSON logs stored in S3 directly using standard SQL, without needing to load or transform the data. Athena's schema-on-read approach and pay-per-query pricing make it highly cost-effective for ad-hoc analysis of large volumes of log data, as you only pay for the data scanned during queries.

Answer

Amazon DynamoDB

Answer

Amazon RDS for MySQL

Answer

Amazon Kinesis Data Analytics

Question 2

A company is using Amazon DynamoDB with on-demand capacity for a gaming application. During a new game launch, write traffic spikes to 50,000 writes per second, but the application experiences throttling. The DynamoDB table has a partition key of 'game_id' and a sort key of 'timestamp'. What is the MOST likely cause of throttling?

Accepted Answer

Hot partitions due to a skewed access pattern on the partition key 'game_id'.. DynamoDB on-demand capacity automatically scales to handle traffic spikes, but it still has per-partition throughput limits. With 'game_id' as the partition key, a single popular game can create a hot partition where all writes target the same partition, exceeding the partition's maximum write capacity (1,000 write capacity units per partition) and causing throttling, even though the overall table capacity is sufficient.

Answer

The table has not enabled auto-scaling for writes.

Answer

The table's on-demand capacity is insufficient for the write spike.

Answer

The sort key is not optimal for write-heavy workloads.

Question 3

A data engineer needs to grant a data scientist access to query a Glue Data Catalog database but must prevent the data scientist from seeing the underlying S3 data locations. Which approach should be used?

Accepted Answer

Use AWS Lake Formation to grant SELECT permission on the database and tables without granting S3 access. AWS Lake Formation allows fine-grained access control at the database, table, and column level without requiring direct S3 permissions. By granting SELECT on the Glue Data Catalog database and tables through Lake Formation, the data scientist can query the data via Athena or Redshift Spectrum while Lake Formation handles credential vending to access S3 on their behalf. This meets the requirement of preventing the data scientist from seeing the underlying S3 locations.

Answer

Use a Glue resource policy to restrict access to the database

Answer

Grant the data scientist IAM permissions to access the Glue Data Catalog and the underlying S3 data

Answer

Create a VPC endpoint for Glue and S3 to restrict network access

Question 4

A data engineer needs to store time-series data from IoT devices. The data is write-heavy and requires low-latency queries by device ID and timestamp. The data volume is expected to grow to terabytes. Which AWS database service is most suitable?

Accepted Answer

Amazon Timestream. Amazon Timestream is purpose-built for time-series data, offering automatic tiered storage (in-memory for recent data and magnetic for historical) to handle write-heavy IoT workloads at scale. It supports low-latency queries by device ID and timestamp via its SQL-compatible query engine, making it the most suitable choice for terabytes of time-series data.

Answer

Amazon RDS for MySQL

Answer

Amazon ElastiCache for Redis

Answer

Amazon DynamoDB

Question 5

A data engineer needs to store encryption keys used for protecting data in Amazon S3 and automatically rotate them every year. Which service should be used?

Accepted Answer

AWS KMS. AWS KMS is the managed service for creating, storing, and rotating encryption keys, and it supports automatic annual rotation for customer managed keys. It integrates natively with S3 SSE-KMS, making it the correct choice for key storage and yearly rotation.

Answer

AWS CloudHSM

Answer

AWS Certificate Manager

Answer

AWS Secrets Manager

Question 6

A data engineer is running an Amazon Athena query that scans a large amount of data in Amazon S3, resulting in high costs. The data is stored in Parquet format in a partitioned table. Which strategy would be MOST effective in reducing the amount of data scanned?

Accepted Answer

Ensure the query includes a WHERE clause that filters on partition columns.. In Athena, partition pruning is the single most effective way to reduce data scanned because Athena only reads the S3 prefixes that match the partition filter. Adding a WHERE clause on partition columns (A) lets the query engine skip entire partitions, dramatically lowering both cost and runtime. This is especially impactful with Parquet, which already supports columnar projection and predicate pushdown.

Answer

Convert the Parquet files to CSV format and apply GZIP compression.

Answer

Use S3 Intelligent-Tiering storage class to reduce storage costs.

Answer

Increase the number of partitions by adding more partition columns.

Question 7

A company is ingesting streaming data from thousands of IoT devices into Amazon Kinesis Data Streams. The data is processed by a Kinesis Data Analytics application. Recently, the application started reporting high iterator age (millisBehindLatest). Which action would BEST reduce the iterator age?

Accepted Answer

Increase the number of shards in the Kinesis stream.. Increasing the number of shards increases the stream's throughput capacity, allowing the Kinesis Data Analytics application to consume data faster and reduce the iterator age (millisBehindLatest). Option A is incorrect: decreasing the data retention period does not improve processing speed; it only reduces the time data is stored. Option B is incorrect: increasing retention also does not affect processing speed. Option C is incorrect: the record size limit is fixed (1 MB) and cannot be increased; increasing shards is the appropriate scaling action.

Answer

Decrease the data retention period of the Kinesis stream.

Answer

Increase the data retention period of the Kinesis stream.

Answer

Increase the record size limit in the Kinesis stream.

Question 8

A data engineer is troubleshooting a slow Amazon Redshift query that joins several large tables. The query plan shows a large number of broadcasts. Which design change would most likely reduce the broadcast operations?

Accepted Answer

Change the DISTKEY on all tables to match the join column.. Setting the DISTKEY on all tables to the join column ensures that rows with the same join key value are co-located on the same compute node. This allows Redshift to perform a collocated join, eliminating the need to broadcast entire tables across the network, which is the primary cause of the slow query.

Answer

Change the SORT KEY on all tables to match the join column.

Answer

Change the DISTSTYLE to EVEN on all tables.

Answer

Change the DISTSTYLE to ALL on all large tables.

Question 9

A data engineer needs to share a dataset from an S3 bucket in Account A with users in Account B. The dataset must remain encrypted at rest with an S3-managed key. What is the MOST secure way to grant cross-account access?

Accepted Answer

Create a bucket policy that grants cross-account access to an IAM role in Account B.. A bucket policy granting access to the IAM role in Account B is the recommended secure method for cross-account access to S3 objects encrypted with S3-managed keys. Option A is insecure because it grants public access. Option C is incorrect because ACLs are legacy and less secure for cross-account scenarios. Option D is incorrect because while S3 VPC endpoints are a valid AWS feature that provides private connectivity to S3, they do not grant cross-account access; bucket policies are still required to authorize access.

Answer

Make the bucket public and use bucket policies to allow only Account B users.

Answer

Use S3 object ACLs to grant access to Account B's root user.

Answer

Use an S3 VPC endpoint to allow Account B users through private IPs.

Question 10

A data engineer reviewed the S3 lifecycle policy shown in the exhibit. The engineer notices that objects under the 'logs/' prefix are being deleted after 365 days. The business requirement is to retain logs for at least 5 years. What should the engineer change in the lifecycle policy?

Accepted Answer

Set the expiration days to 1825. The business requirement is to retain logs for at least 5 years, which is 1,825 days (5 × 365). The current lifecycle policy sets expiration to 365 days, causing premature deletion. By setting the expiration days to 1,825, the S3 lifecycle policy will delete objects under the 'logs/' prefix only after 5 years, meeting the retention requirement.

Answer

Change the prefix to 'logs/archive/'

Answer

Change the transition to GLACIER on day 365

Answer

Remove the expiration action

Question 11

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error 'java.lang.OutOfMemoryError: Java heap space'. The job processes a large number of small files in Amazon S3. Which action would MOST effectively resolve the issue?

Accepted Answer

Enable S3 groupFiles option in the Glue job. The 'java.lang.OutOfMemoryError: Java heap space' in AWS Glue when processing many small files is typically caused by the driver or executor accumulating too many file metadata objects. Enabling the S3 groupFiles option (with groupSize and groupFiles parameters) consolidates small files into larger groups, reducing the number of objects processed and alleviating heap pressure. This directly addresses the root cause of the memory issue.

Answer

Change the worker type to G.1X

Answer

Increase the number of workers in the Glue job

Answer

Use a G.2X worker type with more memory

Question 12

A data engineer is migrating an on-premises Apache HBase workload to Amazon DynamoDB. The HBase table has a row key with composite structure: customer_id (10 chars) + timestamp (10 digits). The access pattern is to query by customer_id and retrieve the latest entries. How should the DynamoDB table be designed to optimize performance?

Accepted Answer

Create a table with partition key = customer_id and sort key = timestamp.. DynamoDB's partition key (customer_id) evenly distributes data across partitions, while the sort key (timestamp) enables efficient range queries using Query with ScanIndexForward=false to retrieve the latest entries. This design directly maps the HBase composite row key pattern to DynamoDB's primary key structure, optimizing for the described access pattern.

Answer

Use Amazon S3 with customer_id as prefix and timestamp as object name.

Answer

Create a table with partition key = concatenated customer_id and timestamp.

Answer

Create a table with partition key = timestamp and sort key = customer_id.

Question 13

Refer to the exhibit. An IAM policy is attached to an IAM role used by an application. The application needs to decrypt objects in an S3 bucket using a customer managed KMS key. What is the effect of this policy?

Accepted Answer

The application can decrypt objects only when accessing them through S3.. The IAM policy grants the `kms:Decrypt` permission with a `kms:ViaService` condition key set to `s3.amazonaws.com`. This condition restricts the decryption operation to only when the request is made through the S3 service. Therefore, the application can decrypt objects only when accessing them through S3, not via direct KMS API calls or other services.

Answer

The application cannot perform any KMS operations.

Answer

The application can decrypt objects from any service.

Answer

The application can encrypt but not decrypt objects.

Question 14

A company is ingesting streaming data from social media feeds using Amazon Kinesis Data Streams. The data volume peaks at 10,000 records per second, and each record is up to 1 KB. The company needs to archive the raw data in Amazon S3 in near real-time and also make it available for real-time analytics using Amazon Kinesis Data Analytics. What is the MOST efficient architecture to meet these requirements?

Accepted Answer

Use Kinesis Data Streams as the ingestion point. Use Kinesis Data Firehose to read from the stream and write to S3. Use Kinesis Data Analytics to read directly from the same stream.. Kinesis Data Streams can serve as a single ingestion point, with Kinesis Data Firehose reading from the stream to deliver data to S3 (with optional transformation) and Kinesis Data Analytics reading directly from the same stream for real-time analytics. This avoids unnecessary duplication of streams or Lambda-based processing, which would add latency and complexity. The architecture is the most efficient as it leverages native integrations without intermediate compute.

Answer

Use Kinesis Data Streams as the ingestion point. Use Kinesis Data Firehose to read from the stream, convert to Parquet, and write to S3. Use a Lambda function to send data to Kinesis Data Analytics.

Answer

Use Kinesis Data Streams as the ingestion point. Use a Lambda function to read from the stream, write to S3, and send data to Kinesis Data Analytics.

Answer

Use two Kinesis Data Streams: one for S3 delivery and one for Kinesis Data Analytics.

Question 15

A data engineer is designing a data lake on Amazon S3 that will be accessed by multiple AWS Glue ETL jobs. The engineer needs to ensure that the data is organized efficiently for querying and that sensitive columns are masked for certain users. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Use AWS Lake Formation to define column-level permissions for sensitive data.. AWS Lake Formation provides fine-grained access control at the column level, allowing you to mask or restrict sensitive columns (e.g., PII) for specific IAM roles or users without altering the underlying data in S3. This is achieved through Lake Formation’s column-level permissions and data filtering, which integrate directly with the AWS Glue Data Catalog and query engines like Athena and Redshift Spectrum.

Answer

Configure AWS Glue Data Catalog to automatically mask sensitive columns in table definitions.

Answer

Use S3 object tags to label sensitive data and apply bucket policies to restrict access.

Answer

Implement S3 lifecycle policies to transition sensitive data to S3 Glacier after 30 days.