DEA-C01 Practice Test 34 — 15 Questions

Question 1

A company uses AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by date. The company wants to improve query performance in Amazon Athena and reduce costs. Which THREE actions should the company take? (Choose THREE.)

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

Accepted Answer

Partition the data by date so Athena can use partition pruning.. Partitioning the data by date allows Athena to use partition pruning, which limits the amount of data scanned by only reading the partitions that match the query's WHERE clause. This directly reduces both query cost (since Athena charges per byte scanned) and query latency, especially for date-range queries on large datasets.

Answer

Convert the data to JSON format for better schema evolution.

Answer

Use Glue DataBrew to clean the data before querying.

Question 2

A data engineer is designing a data lake on AWS using Amazon S3. The data consists of CSV files generated by IoT devices. The data is accessed by multiple analytics jobs, and the engineer needs to ensure that new files are immediately visible to all consumers after writing. What S3 consistency model applies?

Accepted Answer

Strong consistency for all operations.. Amazon S3 now provides strong consistency for all operations. After a successful write of a new object (or overwrite of an existing object), any subsequent read request immediately receives the latest version of the object, and list operations are also strongly consistent. Therefore, for new CSV files written to S3, the applicable model is strong consistency for all operations.

Answer

Consistent reads require S3 Object Lock.

Answer

Eventual consistency for all operations.

Answer

Read-after-write consistency for new object PUTS.

Question 3

A company wants to monitor and alert on unauthorized API calls in their AWS account. Which AWS service should be used to detect and notify on such events?

Accepted Answer

AWS CloudTrail and Amazon CloudWatch Events. D is correct because AWS CloudTrail records all API calls in the AWS account, and Amazon CloudWatch Events (or EventBridge) can be configured with rules to detect specific API calls (e.g., unauthorized actions) and trigger notifications. Option A is incorrect because Amazon GuardDuty and AWS Security Hub are threat detection and security management services, not primarily for monitoring all API calls. Option B is incorrect because Amazon VPC Flow Logs capture network traffic metadata, not API calls. Option C is incorrect because AWS Config monitors resource configuration changes, not API calls.

Answer

Amazon GuardDuty and AWS Security Hub

Answer

Amazon VPC Flow Logs and Amazon CloudWatch Logs

Answer

AWS Config and AWS Systems Manager

Question 4

A company is streaming IoT sensor data to Amazon Kinesis Data Streams. The data is JSON with a schema that changes occasionally. They want to load the data into Amazon S3 in Parquet format partitioned by date and sensor_id. Which approach is MOST cost-effective and operationally efficient?

Accepted Answer

Use Amazon Kinesis Data Firehose with a schema from AWS Glue Data Catalog to convert to Parquet and enable dynamic partitioning by date and sensor_id.. Amazon Kinesis Data Firehose is the most cost-effective and operationally efficient solution because it natively supports converting JSON to Parquet using a schema from the AWS Glue Data Catalog and can dynamically partition data by date and sensor_id. Firehose handles buffering, compression, and delivery to S3 without requiring custom code or infrastructure management.

Answer

Use Amazon EMR to read from Kinesis Data Streams and write to S3 in Parquet format.

Answer

Use a Lambda function to transform records to Parquet and write to S3.

Answer

Use a custom Kinesis Client Library application on EC2 to buffer and write Parquet files to S3.

Question 5

A data engineer is designing a data lake on Amazon S3. The data includes sensitive personally identifiable information (PII). Which combination of services would provide the most comprehensive data protection?

Accepted Answer

Enable S3 Block Public Access and use Amazon Macie to discover and classify PII. S3 Block Public Access prevents accidental public exposure of buckets and objects, which is the leading cause of S3 data leaks, while Amazon Macie uses machine learning and pattern matching to discover, classify, and alert on PII stored in S3. Together they address both the access-control and data-discovery dimensions of protecting sensitive PII, making this the most comprehensive combination.

Answer

Use S3 Transfer Acceleration and enable versioning

Answer

Enable S3 server-side encryption with AWS KMS

Answer

Use Amazon CloudWatch Logs to monitor access and enable MFA Delete

Question 6

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and must be queryable using Amazon Athena. The engineer needs to optimize query performance and reduce costs. Which THREE actions would achieve this?

Accepted Answer

Partition the data by a commonly used filter column.. Option B is correct because partitioning the S3 data by a commonly used filter column (e.g., date or region) lets Athena prune irrelevant partitions via partition projection or the AWS Glue Data Catalog, drastically reducing the bytes scanned and thus query latency and cost. Option D is correct because splittable compression formats like Snappy (and also LZO/BZip2) allow Athena to split large objects across multiple readers, preserving parallelism while lowering storage and scan costs; non-splittable formats like Gzip force a single reader per file. Option E is correct because columnar formats such as Apache Parquet or ORC enable column pruning and predicate pushdown, so Athena reads only the needed columns and row groups, cutting both I/O and cost compared to row-based CSV/JSON. Option A is incorrect because many small files create excessive per-file overhead in S3 listing, Glue catalog, and Athena planning, hurting performance and raising costs rather than increasing useful parallelism. Option C is incorrect because S3 Select is a lightweight object-level filtering feature, not a replacement for Athena's distributed SQL engine over a data lake, and it cannot perform joins, aggregations, or catalog-based queries.

Answer

Store data in many small files to increase parallelism.

Answer

Use S3 Select instead of Athena for queries.

Question 7

A data engineer is troubleshooting a failed AWS Glue job that reads from an Amazon RDS for MySQL table. The error message indicates 'java.sql.SQLException: No suitable driver'. What is the most likely cause?

Accepted Answer

The MySQL JDBC driver JAR is not included in the Glue job's dependencies.. The error 'java.sql.SQLException: No suitable driver' indicates that the Java application (the Glue job) cannot find a suitable JDBC driver for the database URL. In AWS Glue, you must include the JDBC driver JAR as a dependency for the job. If the MySQL JDBC driver JAR is not included, the job cannot load the driver class, resulting in this error.

Answer

The Glue job is using the wrong JDBC driver class name.

Answer

The Glue job's VPC subnet does not have a route to the RDS instance.

Answer

The RDS instance is not publicly accessible.

Question 8

A data engineer uses AWS CloudTrail to investigate a security incident. The engineer runs the command shown in the exhibit. What does the output indicate?

Accepted Answer

A file was uploaded to the S3 bucket.. The CloudTrail event shown is an S3 PutObject API call, which is the operation used to upload an object to an S3 bucket. The event name 'PutObject' and the presence of request parameters like 'bucketName' and 'key' confirm that a file was successfully written to the bucket. Therefore, the output indicates that a file was uploaded to the S3 bucket.

Answer

A file was downloaded from the S3 bucket.

Answer

A file was deleted from the S3 bucket.

Answer

A batch of files was listed from the S3 bucket.

Question 9

A company runs a SQL Server transactional database on Amazon RDS. They need to capture change data (inserts, updates, deletes) in near real-time and replicate them to an Amazon S3 data lake. Which AWS service is most suitable?

Accepted Answer

AWS Database Migration Service (DMS) with change data capture. AWS DMS with change data capture (CDC) is the most suitable service because it can continuously capture and replicate incremental changes (inserts, updates, deletes) from a SQL Server transactional database on Amazon RDS to an S3 data lake in near real-time. DMS uses native SQL Server transaction logs (e.g., MS-CDC or log-based replication) to read changes without impacting source performance, and it supports target S3 in formats like Parquet or CSV. This directly meets the requirement for near-real-time CDC replication to a data lake.

Answer

AWS Glue DataBrew

Answer

Amazon Kinesis Data Streams with Kinesis Client Library

Answer

Amazon Redshift Spectrum

Question 10

Which TWO practices improve the performance of AWS Glue ETL jobs? (Choose two.)

Accepted Answer

Use pushdown predicates to filter data at the source. Pushdown predicates (Option A) improve AWS Glue ETL performance by filtering data at the source before it is read into the job. This reduces the volume of data transferred and processed, which is especially effective when using formats like Parquet or ORC that support predicate pushdown natively. By applying filters early, Glue avoids scanning unnecessary partitions or rows, leading to faster execution and lower costs.

Answer

Increase the number of DPUs to the maximum allowed

Answer

Use the smallest possible file size for input data

Answer

Enable AWS Glue job metrics and debug logging

Question 11

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is JSON and each record is about 2 KB. The delivery stream is configured to buffer incoming data to 5 MB or 60 seconds, whichever comes first. The data engineering team notices that the S3 bucket contains many small files (average 2 MB), which makes subsequent processing inefficient. They need to reduce the number of small files without increasing the latency beyond 5 minutes. Which solution should they implement?

Accepted Answer

Increase the buffer size to 50 MB and the buffer interval to 300 seconds.. Increasing the buffer size to 50 MB and the buffer interval to 300 seconds directly addresses the root cause: the current 5 MB buffer size triggers a flush too frequently, producing many 2 MB files. By raising the buffer size to 50 MB, each flush will contain more data, resulting in larger S3 objects (up to ~50 MB uncompressed), while the 300-second interval ensures latency stays within the 5-minute requirement. This reduces the number of small files without requiring additional services or post-processing.

Answer

Enable compression (GZIP) on the delivery stream.

Answer

Use a Lambda function to merge small files after delivery.

Answer

Decrease the buffer size to 1 MB and the buffer interval to 60 seconds.

Question 12

A company wants to audit all data access events in their S3 buckets, including who accessed objects and from which IP address. Which AWS service should be used to capture these events?

Accepted Answer

AWS CloudTrail with data events enabled. AWS CloudTrail with data events enabled captures object-level S3 operations (GetObject, PutObject, DeleteObject) along with the identity of the caller and the source IP address, which is exactly what is needed for a full data access audit. Management events alone do not capture object reads/writes, so data events must be explicitly turned on.

Answer

Amazon CloudWatch Logs

Answer

AWS Config

Answer

Amazon S3 Server Access Logs

Question 13

A data engineer is troubleshooting a Kinesis Data Streams application that is experiencing high latency. The stream has 2 shards. The application is using a single Kinesis Client Library (KCL) worker to process all shards. Which change will MOST likely reduce latency?

Accepted Answer

Deploy multiple KCL workers to process shards in parallel.. The application uses a single KCL worker to process all 2 shards, which processes records sequentially and causes high latency. Deploying multiple KCL workers (ideally one per shard) enables parallel processing of shards, significantly reducing latency. Option A is incorrect because increasing shard count to 4 adds more capacity but does not address the bottleneck of a single worker; the same worker would process all 4 shards sequentially, potentially worsening latency. Option C is incorrect because Kinesis Data Streams is a managed service; there is no instance type to change for the stream itself. The KCL worker runs on your compute resources, not on the stream. Option D is incorrect because decreasing shards to 1 reduces the level of parallelism, increasing the workload per shard and likely increasing latency further.

Answer

Increase the number of shards to 4.

Answer

Use a larger instance type for the Kinesis stream.

Answer

Decrease the number of shards to 1.

Question 14

A data pipeline uses AWS Glue ETL jobs to process data from Amazon RDS for MySQL to Amazon S3. Recently, the jobs have been failing with the error 'Communications link failure' during the connection phase. The RDS instance is in a private subnet, and the Glue job uses a VPC endpoint for S3. What is the most likely cause?

Accepted Answer

The Glue job does not have a network path to the RDS instance because it is not attached to the same VPC subnet.. The most likely cause is that the AWS Glue job does not have a network path to the RDS instance because it is not attached to the same VPC subnet. AWS Glue jobs run in a serverless environment that, by default, is outside the customer's VPC. To connect to a private RDS instance, the Glue job must be configured with a VPC connection that specifies the VPC, subnet, and security group, allowing it to reach the RDS instance. Without this, the JDBC connection fails with 'Communications link failure' during the connection phase.

Answer

The RDS database has reached the maximum number of connections.

Answer

The Glue job does not have IAM permissions to decrypt the RDS database using AWS KMS.

Answer

The JDBC driver used by Glue is incompatible with the MySQL version.

Question 15

An e-commerce company ingests clickstream data from their website into Amazon S3. The data is in JSON format, and each file is about 10 MB. They need to transform the data into a columnar format for analytics and load it into Amazon Redshift nightly. The transformation should be cost-effective and require minimal operational overhead. Which approach meets these requirements?

Accepted Answer

Use AWS Glue ETL job to convert to Parquet and load into Redshift.. AWS Glue ETL is the correct choice because it is a serverless, managed service that can efficiently convert JSON to Parquet (a columnar format optimized for Redshift) and load the data into Redshift with minimal operational overhead. The nightly batch processing of 10 MB files is well-suited for Glue's pay-per-use pricing, making it cost-effective without requiring infrastructure management.

Answer

Use Amazon Redshift COPY command to load JSON directly.

Answer

Use Amazon EMR with Spark to transform and load data.

Answer

Use AWS Lambda to transform each file and write to Redshift.