DEA-C01 Practice Exam 62 — 20 Questions

Question 1

A company is using Amazon Athena to query data stored in S3. Queries are failing with 'HIVE_INVALID_PARTITION' errors. What is the most likely cause?

Accepted Answer

A partition folder in S3 has been deleted or moved, but the table metadata still references it.. The 'HIVE_INVALID_PARTITION' error in Amazon Athena occurs when the table's partition metadata in the AWS Glue Data Catalog (or Hive metastore) references a partition folder that no longer exists in the S3 bucket. Athena relies on the metadata to locate data files; if a partition folder is deleted or moved without updating the metadata, queries fail because Athena cannot find the expected data location.

Answer

The S3 bucket is configured with a bucket policy that denies access to the Athena service.

Answer

The data is compressed with gzip, but the table definition expects uncompressed data.

Answer

The data files are in CSV format but the table definition expects Parquet.

Question 2

A company uses Amazon Redshift for data warehousing. The data engineering team notices that queries are slow due to high disk I/O. The team wants to improve query performance without changing the cluster configuration. Which action should the team take?

Accepted Answer

Redesign tables with appropriate sort keys and distribution styles.. Redesigning tables with appropriate sort keys and distribution styles directly addresses high disk I/O by minimizing data scanning and reducing data movement across nodes. Sort keys enable Redshift to skip irrelevant blocks via zone maps, while distribution styles (KEY, ALL, EVEN) optimize data locality for joins and aggregations, reducing I/O without changing cluster configuration.

Answer

Increase the number of nodes in the cluster.

Answer

Run the ANALYZE command to update table statistics.

Answer

Run the VACUUM command to reclaim disk space.

Question 3

A data engineer needs to schedule an AWS Glue ETL job to run every hour and process new data that arrives in an S3 bucket. The job should only process files that have been added since the last run. Which approach should the engineer use to track which files have been processed?

Accepted Answer

Enable job bookmarks in the Glue ETL job.. AWS Glue job bookmarks automatically track the last processed data, allowing the job to incrementally process only new files since the last run. This is the native, built-in mechanism for stateful incremental processing in Glue ETL, eliminating the need for external tracking.

Answer

Configure S3 Event Notifications to trigger the Glue job on each new object creation.

Answer

Store the last processed timestamp in a DynamoDB table and query it at the start of the job.

Answer

Use S3 Inventory to list all objects and filter by last modified date in the job.

Question 4

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

Answer

Amazon Redshift

Answer

Amazon S3

Question 5

A data engineer needs to allow an IAM user to rotate the secret in AWS Secrets Manager for an RDS database. Which IAM action should be included in the policy?

Accepted Answer

secretsmanager:RotateSecret. The secretsmanager:RotateSecret action allows the user to initiate rotation of a secret. Option A is correct. secretsmanager:GetSecretValue only retrieves the secret value, not rotate it.

Answer

secretsmanager:PutSecretValue

Answer

secretsmanager:UpdateSecret

Answer

secretsmanager:GetSecretValue

Question 6

A data engineer is designing a data pipeline that ingests data from multiple sources into Amazon S3, then processes it with AWS Glue and loads it into Amazon Redshift. Which THREE practices should be implemented to ensure data quality?

Accepted Answer

Implement data validation checks at the ingestion stage. Option A is correct because validating data at ingestion (for example, checking types, nulls, ranges, and referential integrity before writing to Amazon S3) prevents corrupt or malformed records from propagating downstream into Glue ETL jobs and Redshift tables. Option B is correct because AWS Glue DataBrew provides visual data profiling, column statistics, and built-in transformations/rules that can enforce schema consistency and detect anomalies before loading into Redshift. Option E is correct because Amazon CloudWatch alarms on Glue job metrics, Redshift load events, and custom anomaly metrics enable proactive detection of pipeline failures and unexpected data patterns, which is essential for ongoing data quality assurance. Option C is not correct because compressing files reduces storage and I/O costs but does not validate or improve data accuracy, completeness, or consistency. Option D is not correct because manual sampling is ad hoc, non-scalable, and not a reliable or automated data quality control compared with systematic validation, profiling, and monitoring.

Answer

Compress data files to reduce storage costs

Answer

Use manual sampling to check data quality periodically

Question 7

A data engineer is monitoring Amazon CloudWatch metrics for an Amazon Redshift cluster and notices high CPU utilization. The engineer wants to reduce CPU usage. Which TWO actions should the engineer take?

Accepted Answer

Enable concurrency scaling to offload read queries to additional clusters.. Option A is correct because concurrency scaling automatically adds transient cluster capacity to handle bursts of read (SELECT) queries, offloading that work from the main cluster and thereby reducing its CPU utilization. Option C is correct because choosing appropriate sort keys and compression encodings reduces the amount of data scanned and I/O performed, which directly lowers CPU work during query execution. Option B is not the best fit because adding nodes increases capacity but does not address inefficient queries or design, and it is a costlier, less targeted remedy for CPU pressure. Option D is not correct because VACUUM reclaims space and re-sorts rows after deletes/updates; it is maintenance rather than a primary CPU-reduction action and can itself consume significant resources. Option E is not correct because audit logging only records connection and user activity for monitoring/compliance and does nothing to reduce CPU usage.

Answer

Increase the number of nodes in the cluster.

Answer

Run the VACUUM command on all tables.

Answer

Enable audit logging to monitor queries.

Question 8

A company needs to store files that are accessed by multiple EC2 instances in a VPC. The files must be concurrently accessible and durable. Which storage solution should the data engineer choose?

Accepted Answer

Amazon Elastic File System (Amazon EFS). Amazon EFS provides a fully managed, scalable, and elastic NFS file system that can be concurrently accessed by multiple EC2 instances across multiple Availability Zones. It is designed for high durability (11 nines of durability) and automatically replicates data across multiple AZs within a region, meeting the requirements for concurrent access and durability.

Answer

Amazon EC2 instance store

Answer

Amazon Simple Storage Service (Amazon S3)

Answer

Amazon Elastic Block Store (Amazon EBS)

Question 9

A data engineer is troubleshooting a step function that orchestrates ETL jobs. The state machine fails with 'State Machine Execution Throttled' error. What should the engineer do to resolve this?

Accepted Answer

Request a service quota increase for concurrent executions.. The 'State Machine Execution Throttled' error in AWS Step Functions occurs when the account hits the service quota for concurrent state machine executions (default 1,000 per region, or lower for certain execution types). The correct remediation is to request a service quota increase via the AWS Service Quotas console for the 'Concurrent executions' quota of Step Functions. This directly addresses the throttling cause rather than masking it.

Answer

Reduce the number of steps in the state machine.

Answer

Set up a CloudWatch alarm to detect throttling and retry.

Answer

Adjust the API rate limits in the state machine definition.

Question 10

A company needs to ingest data from multiple SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data sources have different schemas and update frequencies. Which AWS service should be used to build this ingestion pipeline with minimal code?

Accepted Answer

Amazon AppFlow. Amazon AppFlow (Option D) is the correct answer because it is purpose-built for ingesting data from SaaS applications like Salesforce and Marketo into Amazon S3 with minimal code. It supports various source connectors, handles schema variations, and allows scheduling based on update frequencies. AWS Data Pipeline (A) requires more manual configuration and code for connectors. AWS Glue (B) has connectors but is more complex for this use case, often requiring additional ETL scripting. Amazon Kinesis Data Firehose (C) is designed for streaming data, not batch extraction from SaaS APIs.

Answer

AWS Data Pipeline

Answer

AWS Glue

Answer

Amazon Kinesis Data Firehose

Question 11

A healthcare company is ingesting patient data from a legacy system into an Amazon S3 data lake using AWS Glue. The legacy system produces CSV files with inconsistent schemas (columns may appear or disappear in different files). The data engineer needs to create a Glue ETL job that can handle schema evolution and transform the data into a standardized parquet format. The job should also be able to process new files as they arrive. Which approach should the data engineer use?

Accepted Answer

Use AWS Glue DynamicFrames to read the CSV files and apply transformations using resolveChoice and applyMapping.. AWS Glue DynamicFrames are designed to handle schema evolution and inconsistent data. Using resolveChoice to handle columns that appear/disappear and applyMapping to standardize the schema allows the job to process files with varying schemas and output consistent Parquet.

Answer

Use AWS Glue crawlers to create a schema in the Data Catalog and then use a standard Spark DataFrame for transformation.

Answer

Use a Python shell job in Glue to manually parse each file and write to parquet.

Answer

Use a Glue ETL job with a static schema defined in the script and ignore files that don't match.

Question 12

A company needs to store archival logs that must be retained for 10 years. The logs are accessed infrequently, but when accessed, retrieval must occur within 12 hours. Which storage class is MOST cost-effective?

Accepted Answer

Amazon S3 Glacier Deep Archive. Amazon S3 Glacier Deep Archive is the most cost-effective storage class for archival logs that must be retained for 10 years with infrequent access and a 12-hour retrieval window. It offers the lowest storage cost among S3 classes, with retrieval times typically within 12 hours for standard retrievals, making it ideal for long-term archival data that is rarely accessed.

Answer

Amazon S3 Intelligent-Tiering

Answer

Amazon S3 Standard

Answer

Amazon S3 One Zone-Infrequent Access

Question 13

A data engineer is running a Spark job on Amazon EMR. The job reads from S3, processes data, and writes to S3. The job is taking longer than expected. The engineer notices that the job is spending a lot of time in the 'GC' (garbage collection) phase. Which configuration change is most likely to improve performance?

Accepted Answer

Increase the spark.executor.memory setting.. Garbage collection overhead in Spark is directly tied to the size of the JVM heap on each executor. When executors have too little heap, the JVM runs GC far more frequently and for longer pauses, stalling task execution. Increasing spark.executor.memory enlarges the heap, reducing GC frequency and duration, which is the standard remedy when the Spark UI shows high GC time relative to task time.

Answer

Increase the spark.sql.shuffle.partitions.

Answer

Decrease the number of executor cores.

Answer

Decrease the spark.executor.memoryOverhead.

Question 14

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time and then stored in Amazon S3. Which AWS service should be used to perform the transformation?

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics (now part of Amazon Managed Service for Apache Flink) is the correct choice because it can consume streaming data from Kinesis Data Streams, apply real-time transformations using SQL or Apache Flink, and then output the transformed data to destinations like Amazon S3. This directly meets the requirement for real-time transformation of streaming IoT data before storage.

Answer

AWS Glue

Answer

Amazon EMR

Answer

Amazon Athena

Question 15

A company is streaming IoT data from thousands of devices into Amazon Kinesis Data Streams. The data must be transformed in real time before being stored in Amazon S3. Which service should be used to perform the transformation as the data streams through Kinesis?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it is purpose-built for running Apache Flink applications that can perform real-time transformations, filtering, and enrichment on data streaming through Kinesis Data Streams before outputting the results to destinations like Amazon S3. It integrates natively with Kinesis Data Streams as a source and can write transformed data directly to S3 using a Flink sink, making it ideal for this streaming ETL use case.

Answer

AWS Glue

Answer

Amazon EMR

Answer

AWS Lambda

Question 16

A data engineer is tasked with transforming JSON data from an S3 bucket into Parquet format for efficient querying. The transformation should run on a schedule every hour. Which AWS service is best suited for this task?

Accepted Answer

AWS Glue. AWS Glue is the best choice because it is a fully managed ETL service designed specifically for transforming and cataloging data at scale. It can natively read JSON from S3, convert it to Parquet, and run on a scheduled hourly basis using a Glue job with a trigger, without requiring server management or custom infrastructure.

Answer

AWS Lambda

Answer

Amazon Athena

Answer

Amazon EMR

Question 17

A data engineer is troubleshooting an AWS Glue job that fails with 'java.lang.OutOfMemoryError: Java heap space'. The job processes a large dataset. Which TWO configuration changes should the engineer consider to resolve this issue? (Choose TWO.)

Accepted Answer

Increase the Spark shuffle partitions configuration (spark.sql.shuffle.partitions).. Option B is correct because increasing spark.sql.shuffle.partitions creates more, smaller partitions during shuffles, which reduces the amount of data each executor task must hold in memory and helps prevent Java heap space exhaustion on large datasets. Option D is correct because allocating more DPUs adds more executors and memory to the Glue job, giving Spark more heap capacity to process the large dataset without running out of memory. Option A is incorrect because switching from Parquet to CSV increases data size and I/O, worsening memory pressure rather than relieving it. Option C is incorrect because reducing source partitions concentrates more data per partition, increasing per-task memory usage and the risk of OOM. Option E is incorrect because disabling job bookmarks only affects incremental processing state and does not address heap memory consumption.

Answer

Change the output format from Parquet to CSV.

Answer

Reduce the number of partitions in the source data.

Answer

Disable job bookmarks to avoid incremental processing.

Question 18

A healthcare company processes patient records in near-real-time using Amazon Kinesis Data Streams. Each record contains sensitive personal health information (PHI). The data must be encrypted at rest and in transit. The company also needs to audit access to the data. The data engineer is designing the ingestion pipeline. Which combination of services and configurations meets these requirements?

Accepted Answer

Use Kinesis Data Streams with SSE-KMS and TLS, and enable CloudTrail for data events.. Kinesis Data Streams supports server-side encryption with AWS KMS (SSE-KMS) for encryption at rest, and TLS is used for encryption in transit by default. Enabling CloudTrail data events captures API-level access to Kinesis streams (e.g., PutRecord, GetRecords), satisfying the audit requirement. This combination meets all three requirements: encryption at rest, encryption in transit, and auditable access.

Answer

Use Kinesis Data Firehose to deliver data to S3 with SSE-S3, and enable CloudTrail for S3.

Answer

Use Kinesis Data Streams with TLS and enable CloudTrail for auditing. Do not enable SSE.

Answer

Use Kinesis Data Streams with SSE-KMS and TLS. Do not enable any auditing.

Question 19

A company stores sensitive data in an Amazon S3 bucket. A compliance requirement mandates that all data must be encrypted at rest with a key that is automatically rotated every year. The company also needs to maintain an audit trail of who used the key. Which solution meets these requirements?

Accepted Answer

Use AWS KMS customer managed keys (SSE-KMS) with automatic key rotation enabled.. AWS KMS customer managed keys (SSE-KMS) with automatic key rotation enabled satisfies both requirements: it encrypts data at rest in S3 and automatically rotates the KMS key every year. Additionally, KMS integrates with AWS CloudTrail to log every API call (e.g., Decrypt, GenerateDataKey) that uses the key, providing an audit trail of who used the key and when.

Answer

Use customer-provided encryption keys (SSE-C) and rotate keys manually.

Answer

Use S3 managed keys (SSE-S3) and enable S3 server access logs.

Answer

Configure a bucket policy to enforce encryption using the 'aws:SecureTransport' condition.

Question 20

A data engineer is troubleshooting a Kinesis Data Firehose delivery stream that ingests JSON log data from web servers. The stream is configured to transform records with an AWS Lambda function and deliver to an Amazon S3 bucket. Recently, the stream has been failing with 'InvalidData' errors. Which action should the engineer take to resolve the issue?

Accepted Answer

Check the CloudWatch Logs for the Lambda function to identify transformation errors.. The 'InvalidData' error in Kinesis Data Firehose typically indicates that the Lambda function used for data transformation is failing or returning malformed records. By checking the CloudWatch Logs for the Lambda function, the engineer can identify specific transformation errors, such as incorrect JSON parsing, missing fields, or exceptions, which cause Firehose to reject the records. This is the most direct troubleshooting step because Firehose relies on the Lambda function to return valid transformed data in the expected format.

Answer

Verify the S3 bucket policy allows Firehose to write.

Answer

Increase the buffer size and interval in the Firehose delivery stream.

Answer

Change the data format to CSV in the Firehose configuration.