DEA-C01 Mock Exam 87 — 25 Questions

Question 1

A data engineering team uses Amazon S3 to store raw data files. They have an AWS Glue ETL job that reads from an S3 bucket, transforms the data, and writes to a Redshift cluster. The job runs daily and has been failing intermittently with the error: 'An error occurred while calling o143.pyWriteDynamicFrame. S3 Access Denied'. The team has confirmed that the IAM role used by the Glue job has s3:GetObject and s3:PutObject permissions on the bucket and all objects. The Redshift cluster is in the same VPC and the Glue connection is configured correctly. What is the most likely cause of the failure?

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

Accepted Answer

The Glue job's IAM role lacks permission to write to the Glue temporary file bucket (aws-glue-*).. Glue jobs use a special S3 bucket for bookkeeping and temporary data. The job's IAM role must have s3:PutObject permission on the bucket used for temporary files, which is often 'aws-glue-*' for the same region. If this permission is missing, the job fails with access denied. Option A is wrong because the error is an S3 access issue, not a network timeout. Option B is wrong because the error is not related to schema mismatch. Option C is wrong because the error is an S3 access issue, not a Glue job timeout.

Answer

The Redshift cluster is not publicly accessible and the Glue job does not have a VPC endpoint to Redshift.

Answer

The Glue job has exceeded the maximum execution time and is being killed by AWS.

Answer

The Glue job is using the wrong JDBC driver version for Redshift.

Question 2

A data engineer needs to transform JSON data into Parquet format using AWS Glue. The input data has nested fields. Which Glue feature should be used to flatten the nested structure?

Accepted Answer

Relationalize transform. The Relationalize transform is the correct choice because it is specifically designed to flatten nested JSON structures (such as arrays and structs) into a set of related tables (DataFrames) that can be written as Parquet. This transform recursively extracts nested fields, creating separate DataFrames for each level of nesting, which is essential for converting complex JSON into a flat, columnar Parquet format suitable for analytics.

Answer

DropNullFields transform

Answer

FindMatches transform

Answer

Map transform

Question 3

A company wants to schedule a nightly batch job to copy data from an on-premises PostgreSQL database to Amazon S3. The solution must minimize operational overhead. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it provides a fully managed ETL service that can connect to on-premises PostgreSQL via JDBC, extract data, and write it to Amazon S3 with minimal configuration. Glue's built-in scheduler can run the job nightly, eliminating the need to manage servers or orchestration infrastructure, which directly meets the requirement to minimize operational overhead.

Answer

AWS Data Pipeline

Answer

Amazon EMR

Answer

AWS Database Migration Service (AWS DMS) with ongoing replication

Question 4

A company runs a data pipeline that ingests clickstream data from a web application into Amazon Kinesis Data Streams. A Lambda function processes records from the stream and writes them to an Amazon S3 bucket in JSON format. The pipeline has been running smoothly, but for the past hour, the Lambda function has been failing with 'Rate exceeded' errors, and the Kinesis stream shows elevated 'IteratorAgeMilliseconds' metrics. The Lambda function has a reserved concurrency of 100, and the Kinesis stream has 10 shards. The average record size is 5 KB, and the data rate is approximately 15 MB per second. Which combination of actions should a data engineer take to resolve the issue and prevent recurrence? (Choose TWO.)

Accepted Answer

Increase the Lambda function's reserved concurrency to 200.. The 'Rate exceeded' errors indicate that the Lambda function's concurrency is insufficient to keep up with the incoming data rate from Kinesis. With 10 shards and a 15 MB/s data rate, each shard processes ~1.5 MB/s, and with 5 KB records, that's ~300 records per second per shard. Increasing reserved concurrency to 200 allows more parallel invocations to handle the load, reducing the iterator age.

Answer

Decrease the Lambda function's batch size from 100 to 50.

Answer

Enable S3 multipart upload for the Lambda function.

Answer

Replace the Lambda function with Amazon Kinesis Data Firehose to write directly to S3.

Question 5

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 6

A company is designing a multi-Region disaster recovery solution for Amazon DynamoDB. They need to ensure that data is replicated across Regions with minimal latency and that applications can read from any Region. Which THREE steps should be taken? (Choose THREE.)

Accepted Answer

Configure application to read from any Region using the DynamoDB endpoint.. DynamoDB Global Tables provide multi-Region, multi-master replication, allowing applications to read from any Region by simply pointing to the local DynamoDB endpoint. This ensures low-latency reads by serving data from the closest Region, and the replication is fully managed by DynamoDB.

Answer

Configure Time to Live (TTL) to automatically expire old data.

Answer

Deploy DynamoDB Accelerator (DAX) in each Region.

Question 7

A company uses Amazon RDS for PostgreSQL to store financial data. The security team requires that all database connections be encrypted in transit and that the database audit logs be stored in Amazon S3 for at least 7 years. Which steps should the data engineer take to meet these requirements?

Accepted Answer

Set the `rds.force_ssl` parameter to 1 in the DB parameter group, and export RDS audit logs to Amazon CloudWatch Logs with a subscription to Amazon S3. Setting `rds.force_ssl` to 1 in the DB parameter group forces all connections to use SSL/TLS, ensuring encryption in transit. To store audit logs in S3 for at least 7 years, you can export RDS audit logs to Amazon CloudWatch Logs and create a subscription filter to stream them to Amazon S3, where an S3 lifecycle policy can manage retention. Option A is incorrect because encryption at rest does not enforce TLS for connections, and RDS cannot publish logs directly to S3. Option B is incorrect because security groups control network access, not encryption protocol, and CloudTrail logs AWS API calls, not database queries. Option C is incorrect because ACM certificates are not used with RDS for SSL connections, and streaming to Kinesis Firehose is unnecessary and not directly supported for RDS audit logs.

Answer

Enable encryption at rest using AWS KMS, and configure the RDS instance to publish logs to an S3 bucket with a lifecycle policy

Answer

Configure the DB security group to allow only TLS connections, and set up AWS CloudTrail to log all database queries

Answer

Use an SSL certificate from AWS Certificate Manager (ACM) and attach it to the RDS instance, and stream logs to Amazon Kinesis Data Firehose with S3 destination

Question 8

A company uses Amazon DynamoDB for a gaming leaderboard. The table has a primary key of GameId (partition key) and Score (sort key). The application needs to retrieve the top 10 scores for a given game. Which strategies can improve query performance? (Choose TWO.)

Accepted Answer

Create a global secondary index with Score as sort key. Creating a global secondary index (GSI) with Score as the sort key allows efficient range queries on scores for a given GameId. DynamoDB can then use the GSI to retrieve the top 10 scores in sorted order without scanning the entire table, leveraging the index's sort key to fetch only the highest values.

Answer

Change the primary key to a single attribute

Answer

Use a Scan operation with a limit

Answer

Increase the write capacity units

Question 9

A company wants to store historical financial data for 7 years with immediate access for the first year and then infrequent access. After 7 years, the data must be automatically deleted. Which S3 lifecycle policy should be configured?

Accepted Answer

Transition to S3 Glacier Deep Archive after 365 days, expire after 2555 days. It meets all requirements: immediate access for the first year (no transition before 365 days), then transition to S3 Glacier Deep Archive for infrequent access and cost savings, with automatic deletion after 7 years (2555 days). S3 Glacier Deep Archive is the most cost-effective storage class for long-term archival data that is rarely accessed, and the 2555-day expiration ensures compliance with the 7-year retention policy.

Answer

Transition to S3 Standard-IA after 30 days, expire after 2555 days

Answer

Transition to S3 Glacier Flexible Retrieval after 365 days, expire after 2555 days

Answer

Transition to S3 One Zone-IA after 90 days, expire after 365 days

Question 10

A company is using Amazon S3 to store sensitive data. The security team requires that all data be encrypted at rest using a customer-managed AWS KMS key. The data engineer must ensure that only a specific IAM role can decrypt the data. Which policy should the data engineer attach to the KMS key?

Accepted Answer

A KMS key policy that allows the IAM role to perform kms:Decrypt. KMS key policies are the primary mechanism for controlling access to a customer-managed KMS key. By specifying the IAM role as a principal in the key policy and granting kms:Decrypt, you ensure that only that role can decrypt data encrypted with this key, regardless of any IAM policies that might otherwise allow broader access.

Answer

An IAM user policy that allows kms:Decrypt for the specific key

Answer

An IAM policy attached to the role that allows kms:Decrypt

Answer

An S3 bucket policy that denies access unless encryption is used

Question 11

A data engineer is troubleshooting an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The job runs successfully but 5% of records are missing after the load. The engineer suspects data consistency issues. Which THREE actions could help diagnose and resolve the problem? (Choose THREE.)

Accepted Answer

Use the Redshift COPY command with a manifest file to load data.. Using the Redshift COPY command with a manifest file ensures that only the exact files listed in the manifest are loaded, eliminating the risk of partial or duplicate reads from S3. This is a common pattern to guarantee data consistency when the Glue job may not reliably track which files have been processed, especially in scenarios with concurrent writes or retries.

Answer

Increase the number of DPUs for the Glue job.

Answer

Use a staging table in Redshift with a transaction to commit.

Question 12

Refer to the exhibit. A CloudFormation template is used to create a DynamoDB table. After creation, a data engineer wants to restore the table to a point in time from 3 hours ago. Which action is required?

Accepted Answer

Use the AWS CLI or Console to initiate a point-in-time restore specifying the desired timestamp.. To restore a DynamoDB table to a point in time, the table must have point-in-time recovery (PITR) enabled. If it is enabled, you can use the AWS CLI or Console to initiate a point-in-time restore by specifying the desired timestamp. Option A is incorrect because a manual backup is not required; PITR uses continuous backups. Option B is incorrect because AWS Backup is not necessary; DynamoDB PITR is sufficient. Option C is incorrect because PITR does not require any on-demand backups; it relies on continuous backups.

Answer

Create a manual backup of the table first.

Answer

Enable AWS Backup to schedule automatic backups.

Answer

Ensure the table has at least one on-demand backup.

Question 13

A company needs to transfer 20 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The network bandwidth is limited and the transfer must complete within one week. Which service should the company use?

Accepted Answer

AWS Snowball Edge. AWS Snowball Edge is designed for large-scale data transfers (up to 80 TB per device) over limited bandwidth. It physically ships the data, bypassing network constraints entirely. The requirement of 20 TB within one week with limited bandwidth makes Snowball the ideal choice. Option A is wrong because S3 Transfer Acceleration still uses the internet and cannot guarantee completion within a week with limited bandwidth. Option C is wrong because Direct Connect provides a dedicated network connection but requires sufficient bandwidth; if bandwidth is limited, it cannot complete the transfer in time. Option D is wrong because DataSync over VPN still relies on the internet bandwidth and would not meet the deadline.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Direct Connect with DataSync

Answer

AWS DataSync over a VPN connection

Question 14

A data engineer is designing a data ingestion pipeline for streaming social media data. The data must be ingested with low latency (seconds) and stored in Amazon S3 for long-term analytics. The engineer also needs to perform real-time aggregations. Which TWO services should the engineer use? (Choose two.)

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is correct because it enables real-time processing and aggregation of streaming data using SQL or Apache Flink, allowing the engineer to compute metrics like counts or averages on the fly. Combined with Kinesis Data Streams as the ingestion layer, it provides sub-second latency for both ingestion and analytics, meeting the low-latency requirement.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue ETL

Answer

Amazon S3

Question 15

Which THREE factors should be considered when choosing between Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for real-time data ingestion? (Choose three.)

Accepted Answer

The need for a fully managed delivery destination. Amazon Kinesis Data Firehose is a fully managed service that automatically delivers streaming data to destinations like Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, making it ideal when you need a fully managed delivery destination without managing the ingestion pipeline. In contrast, Amazon Kinesis Data Streams requires you to build and manage consumers to process and deliver data, so if you need a fully managed destination, Firehose is the correct choice.

Answer

The ability to compress data before storage

Answer

The maximum throughput supported per shard

Question 16

A company uses AWS Glue to run ETL jobs that process data from Amazon S3 and load into Amazon Redshift. The jobs have recently started failing with 'Out of Memory' errors. The data volume has increased 3x in the past month. Which is the MOST effective solution to resolve this issue without redesigning the job?

Accepted Answer

Increase the number of Glue workers (DPUs) for the job.. To increase the number of Glue workers (DPUs). This provides more memory and processing capacity to handle the increased data volume, directly resolving the 'Out of Memory' errors. Increasing S3 partitions (option D) may improve parallelism but does not directly increase memory for the Glue job. Using Spark SQL (option C) instead of PySpark does not necessarily address memory issues. Switching to Athena (option A) would change the architecture and is not a fix for the existing Glue job.

Answer

Use Amazon Athena instead of Glue for the transformation.

Answer

Rewrite the job to use Spark SQL instead of PySpark.

Answer

Increase the number of partitions in the input S3 data.

Question 17

A company is migrating its on-premises data warehouse to Amazon Redshift. The daily batch load from the source database takes 6 hours using a single-node Redshift cluster. The engineer needs to reduce load time to under 2 hours without increasing cost significantly. Which strategy should the engineer adopt?

Accepted Answer

Increase the number of nodes in the Redshift cluster and use parallel COPY from multiple files.. Increasing the number of nodes in the Redshift cluster provides more compute and I/O capacity, and using parallel COPY from multiple files allows Redshift to automatically split the load across the node slices, dramatically reducing load time. This approach scales performance linearly with the number of nodes, enabling the engineer to meet the sub-2-hour target without significantly increasing cost if the cluster is sized appropriately.

Answer

Use COPY with compression (gzip) to reduce data volume.

Answer

Use a VPC endpoint to improve network throughput to S3.

Answer

Change the table distribution style to EVEN to distribute data evenly.

Question 18

A data engineer runs the above DDL statement in Amazon Athena. The query returns an error. What is the most likely cause?

Accepted Answer

The INPUTFORMAT is incorrect for Parquet files.. The DDL statement uses the default INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat', which is designed for text-based files like CSV or JSON, not for binary columnar formats like Parquet. Parquet requires 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' to correctly read the file's metadata and compressed column chunks. Using the wrong INPUTFORMAT causes Athena to fail when attempting to parse the Parquet file, resulting in an error.

Answer

The SerDe is not compatible with Parquet files.

Answer

The S3 bucket location does not exist.

Answer

The table name contains underscores.

Question 19

Which TWO features of Amazon DynamoDB help ensure high availability and durability? (Choose two.)

Accepted Answer

Data is automatically replicated across multiple Availability Zones within an AWS Region.. DynamoDB automatically replicates data synchronously across three Availability Zones (AZs) within an AWS Region. This built-in replication ensures that even if an entire AZ fails, the data remains available and durable, providing a 99.999999999% (11 nines) durability SLA.

Answer

Auto-scaling adjusts provisioned capacity based on traffic.

Answer

Global tables enable active-active replication across multiple AWS Regions.

Answer

Time to Live (TTL) automatically deletes expired items.

Question 20

A data engineer needs to load data from an on-premises Oracle database to Amazon S3 daily. The table is 500 GB and grows by 50 MB per day. The load must capture only new and changed rows since the last run. Which solution is MOST cost-effective and requires the least maintenance?

Accepted Answer

Use AWS Database Migration Service (DMS) with ongoing replication (CDC). AWS DMS with ongoing replication (CDC) is the most cost-effective and low-maintenance solution because it continuously captures only new and changed rows from the Oracle source using its built-in CDC mechanism (reading redo logs), without requiring custom scripting or full table exports. It automatically handles schema changes, resumability, and incremental loading to S3, minimizing operational overhead and data transfer costs.

Answer

Write a custom Python script on EC2 to query the Oracle redo logs and upload to S3

Answer

Export the entire table to CSV daily using a script and upload to S3

Answer

Use AWS Glue ETL job with a JDBC connection and a timestamp filter