DEA-C01 Mock Exam 85 — 25 Questions

Question 1

A company is using AWS Lake Formation to manage permissions on data in Amazon S3. They need to ingest data from an external source into a new database 'sales_db' and a table 'transactions' using AWS Glue. The IAM role used by Glue must have the minimal permissions to create the database and table in the Data Catalog and write data to the S3 location. Which combination of permissions should be granted?

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

Accepted Answer

Lake Formation permissions: `CREATE_DATABASE` on the catalog, `CREATE_TABLE` on `sales_db`, and S3 location permission. AWS Lake Formation manages permissions on the Data Catalog. To create a database and table, the Glue IAM role must have Lake Formation `CREATE_DATABASE` on the catalog and `CREATE_TABLE` on the `sales_db` database. Additionally, to write data to the S3 location, the role needs S3 write permissions (either via an IAM policy or Lake Formation data location permissions). Option A lacks Lake Formation permissions. Option B grants overly broad `lakeformation:GrantPermissions` which is not needed for creation. Option C provides only S3 permissions, missing Data Catalog permissions. Thus, D is the minimal combination.

Answer

IAM policy with `glue:CreateDatabase`, `glue:CreateTable`, and `s3:PutObject`

Answer

IAM policy with `lakeformation:GrantPermissions` on the database and table

Answer

IAM policy with `s3:GetObject` and `s3:PutObject` on the target location

Question 2

A data engineer needs to schedule a nightly ETL job that reads from an Amazon RDS database and writes to Amazon S3 in Parquet format. The solution must be serverless and minimize cost. Which TWO AWS services should be used? (Choose TWO.)

Accepted Answer

Amazon S3. AWS Glue can run serverless ETL jobs. Amazon S3 is the destination. Lambda could trigger but not run the ETL itself; Data Pipeline is not serverless; Athena is query-only.

Answer

AWS Data Pipeline

Answer

AWS Lambda

Answer

Amazon Athena

Question 3

A company uses AWS DMS to migrate a 2 TB Oracle database to Amazon RDS for PostgreSQL. The migration completes successfully, but data validation shows some tables have missing rows. The task is configured for ongoing replication using change data capture (CDC). What is the MOST likely cause of the missing rows?

Accepted Answer

Source tables missing primary keys. If a table lacks a primary key, DMS cannot uniquely identify rows for CDC, leading to missed changes. Option A is wrong because the endpoint connection is valid (migration completed). Option B is wrong because CDC captures changes from redo logs, not the source database directly. Option D is wrong because DMS supports large objects with proper configuration.

Answer

Source database archive log retention period too short

Answer

Large objects (LOBs) not supported by the target

Answer

Insufficient storage on the DMS replication instance

Question 4

A company uses AWS Glue to process large datasets. The Glue job occasionally fails with 'DiskFull' errors. Which TWO actions should the engineer take to resolve this issue? (Choose two.)

Accepted Answer

Increase the number of workers for the Glue job.

Answer

Store intermediate results in Amazon S3 instead of local disk.

Answer

Enable job bookmark to skip already processed data.

Answer

Use a Python shell job instead of Spark.

Question 5

A company ingests millions of small files (1-10 KB) into Amazon S3 every hour. These files are then processed by AWS Glue ETL jobs. The Glue jobs are slow because of the overhead of reading many small files. Which strategy will most effectively improve Glue job performance?

Accepted Answer

Use a Lambda function to merge small files into larger ones before Glue processes them.. Grouping small files into larger ones (e.g., by merging in a preprocessing step) reduces the number of file read operations and improves Glue's efficiency. Using S3 Select or increasing DPUs helps but doesn't address the root cause.

Answer

Enable Glue job bookmark.

Answer

Increase the number of DPUs for the Glue job.

Answer

Use S3 Select to filter data before Glue reads it.

Question 6

A company uses AWS Glue to transform data in an S3 data lake. The transformation logic requires joining two large datasets that are each hundreds of gigabytes. The Glue job runs out of memory. Which configuration change will most likely resolve this issue?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs provides more memory for the join operation. Glue automatically distributes data across workers, so more workers mean more total memory.

Answer

Repartition the data before the join.

Answer

Use a different file format like Parquet with compression.

Answer

Use the 'spark.sql.autoBroadcastJoinThreshold' setting to broadcast the smaller table.

Question 7

A data engineer needs to ingest streaming data from thousands of IoT devices and immediately process each record with minimal latency. Which AWS service should be used as the ingestion point?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed for real-time streaming data ingestion with low latency. AWS Glue is for batch ETL, S3 is object storage, and Lambda is compute but not an ingestion endpoint itself.

Answer

AWS Lambda

Answer

Amazon S3

Answer

AWS Glue

Question 8

A data engineer runs the command shown. The consumer application is unable to read data older than 24 hours. What is the most likely cause?

Accepted Answer

The retention period is set to 24 hours, so data older than 24 hours is deleted.. The stream's retention period is 24 hours, meaning data is automatically deleted after 24 hours. The consumer tries to read data older than 24 hours, which is no longer available.

Answer

The shard has reached its maximum sequence number.

Answer

The stream is encrypted with KMS, preventing access.

Answer

The stream is in ACTIVE status but not processing data.

Question 9

A company uses AWS DMS to migrate an on-premises Oracle database to Amazon RDS for PostgreSQL. The migration completes, but the target table has more rows than the source. Which is the MOST likely cause?

Accepted Answer

Target tables lack unique constraints, causing DMS to insert duplicate rows.. When target tables lack unique constraints (like primary keys or unique indexes), AWS DMS cannot identify duplicate records during change data capture (CDC). If the same change is applied multiple times (e.g., due to recovery after a failure), DMS may insert duplicate rows, causing the target to have more rows than the source. Option A is incorrect because binary replication is not used; DMS uses logical replication. Option B is incorrect because case sensitivity differences could lead to missing rows, not extra rows. Option C is incorrect because a full load alone would not cause duplicates; the issue arises during ongoing replication without unique constraints.

Answer

DMS used binary replication which included extra metadata rows.

Answer

Oracle and PostgreSQL handle case sensitivity differently.

Answer

DMS performed a full load instead of ongoing replication.

Question 10

A company is ingesting streaming data into Kinesis Data Streams. The consumer application experiences high latency due to a single shard bottleneck. What is the most effective way to reduce latency?

Accepted Answer

Increase the number of shards in the data stream.. Increasing the number of shards increases throughput and reduces latency. Waiting for autoscaling is passive, using KCL is for processing, and switching to Firehose changes the architecture.

Answer

Wait for automatic scaling to add shards.

Answer

Use the Kinesis Client Library (KCL) to process records.

Answer

Switch to Amazon Kinesis Data Firehose.

Question 11

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.. Increasing executor memory reduces GC frequency. Option B is wrong because it reduces parallelism. Option C is wrong because it reduces memory per task. Option D is wrong because it reduces memory and may increase GC.

Answer

Increase the spark.sql.shuffle.partitions.

Answer

Decrease the number of executor cores.

Answer

Decrease the spark.executor.memoryOverhead.

Question 12

A company is running an Amazon EMR cluster with Spark for data processing. The data engineer wants to automatically scale the core and task nodes based on the YARN memory and CPU utilization. Which scaling metric should the engineer use for the EMR managed scaling policy?

Accepted Answer

YARNMemoryAvailablePercentage. EMR managed scaling uses YARNMemoryAvailablePercentage and YARNContainersPending as the default metrics for scaling. Option B is incorrect because CPUUtilization is not a default metric for EMR managed scaling. Option C is incorrect because HDFSUtilization is for HDFS, not YARN. Option D is incorrect because IOPS is not a metric for EMR managed scaling.

Answer

CPUUtilization

Answer

DiskIOPS

Answer

HDFSUtilization

Question 13

A data engineer needs to back up an Amazon DynamoDB table daily. The backup must be restorable to a specific point in time within the last 24 hours. Which solution meets these requirements with the LEAST operational overhead?

Accepted Answer

Enable point-in-time recovery (PITR) on the table.. DynamoDB's point-in-time recovery (PITR) provides continuous backups that allow restoration to any point within the last 35 days with no manual scheduling. Option A is incorrect because on-demand backups are manual and not continuous. Option B is incorrect because DynamoDB Streams captures changes in near-real time but does not provide a backup mechanism; it is used for event-driven processing and replication, not for point-in-time restoration. Option D is incorrect because exporting to S3 requires manual scheduling and is not a backup feature.

Answer

Create an on-demand backup of the table every 24 hours.

Answer

Use DynamoDB Streams to replicate data to another table.

Answer

Export the table data to Amazon S3 every 6 hours using a Lambda function.

Question 14

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 MySQL JDBC driver must be included in the Glue job's dependent JARs or as a Python module. Option B is incorrect because the driver class name is correct; the driver JAR is missing. Option C is incorrect because the error is about driver, not connection. Option D is incorrect because subnet routing does not affect driver loading.

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 15

A data engineer needs to store a large number of small files (each a few KB) from IoT sensors. The data is written once and never modified. The primary requirement is high write throughput and low latency for writes. Which storage solution is most suitable?

Accepted Answer

Amazon DynamoDB with on-demand capacity. Amazon DynamoDB with on-demand capacity is the most suitable because it is a NoSQL key-value and document database designed for single-digit millisecond latency at any scale. It supports high write throughput by automatically distributing data across multiple partitions, and on-demand capacity eliminates the need for provisioning, allowing it to absorb unpredictable write spikes from many IoT sensors without throttling.

Answer

Amazon RDS for MySQL with InnoDB

Answer

Amazon S3 with standard storage class

Answer

Amazon Elastic Block Store (EBS) volumes

Question 16

A data engineer uses AWS Glue to catalog data from an S3 bucket. The data is partitioned by year, month, day. After adding new partitions, the Glue Crawler does not detect them. What is the MOST likely reason?

Accepted Answer

The crawler is configured to only add new partitions to existing tables, but the table schema has changed.. The Glue Crawler, by default, is configured to add new partitions only if the table schema remains unchanged. When new partitions are added to an S3 bucket, if the underlying data schema has changed (e.g., new columns, different data types), the crawler will not add those partitions to the existing table. This is a common safeguard to prevent schema drift from corrupting the cataloged table structure.

Answer

The crawler runs only once and does not schedule subsequent runs.

Answer

The IAM role lacks permission to write to the Glue Data Catalog.

Answer

The partition depth exceeds the crawler's default limit.

Question 17

Refer to the exhibit. A data engineer has an IAM policy attached to an IAM role used by an AWS Glue job. The Glue job reads from S3 bucket 'example-bucket' and writes to an S3 bucket 'output-bucket'. The job fails with an 'Access Denied' error when writing to 'output-bucket'. What is the MOST likely cause?

Accepted Answer

The policy does not allow s3:PutObject on 'output-bucket'.. The policy only allows s3:PutObject on 'example-bucket/*', not on 'output-bucket/*'. The job needs permission on the output bucket. Option A is incorrect because s3:PutObject is allowed on example-bucket, but not on output-bucket. Option C is incorrect because there is no condition that restricts PutObject to example-bucket. Option D is incorrect because the policy allows s3:GetObject on example-bucket, which is for reading.

Answer

The policy does not allow s3:PutObject on any bucket.

Answer

The policy does not allow s3:GetObject on 'output-bucket'.

Answer

The policy has a condition that restricts s3:PutObject to 'example-bucket'.

Question 18

A data engineer is troubleshooting an AWS Glue job that reads from an Apache Kafka topic using a Glue connector. The job fails with 'TimeoutException'. The Kafka cluster is in a VPC. Which step should the engineer take FIRST?

Accepted Answer

Check the security group and network ACLs associated with the Glue job's VPC.. A 'TimeoutException' when reading from Kafka in a VPC typically indicates a network connectivity issue. The first step should be to verify that the security group and network ACLs allow traffic between the Glue job's VPC and the Kafka cluster. Option B (increasing consumer timeout) may be considered after confirming network connectivity, but it is not the first step. Option C (updating the connector) is unrelated to network timeouts. Option D (changing job type) does not address the root cause.

Answer

Increase the Kafka consumer session timeout.

Answer

Update the Glue connector to the latest version.

Answer

Change the Glue job type from Spark to Python Shell.

Question 19

A data engineer notices that an AWS Glue ETL job that processes streaming data from Amazon Kinesis Data Streams is failing intermittently with a 'ResourceNotFoundException' error for the Kinesis stream. The job has been running successfully for weeks. Which action should the engineer take to resolve the issue?

Accepted Answer

Add the 'kinesis:DescribeStream' permission to the IAM role used by the Glue job.. The most common cause of intermittent 'ResourceNotFoundException' for a Kinesis stream is that the IAM role used by the Glue job does not have the kinesis:DescribeStream permission, which is required for the job to check stream details. Option A is incorrect because increasing the Kinesis shard count would not resolve a permissions issue. Option B is incorrect because the Kinesis stream name must match exactly; case sensitivity would cause a consistent error, not intermittent. Option D is incorrect because the timeout setting on the Glue job would not cause a resource not found error.

Answer

Increase the number of shards in the Kinesis data stream to handle higher throughput.

Answer

Rename the Kinesis data stream to match the stream name used in the Glue job exactly, including case.

Answer

Increase the timeout for the Glue job in the job configuration.

Question 20

Refer to the exhibit. A data engineer has attached this KMS key policy to a customer-managed key. The policy is intended to allow the DataEngineer role to decrypt objects in S3 only when the request comes through S3. However, the role is unable to decrypt objects stored in an S3 bucket in the us-west-2 region. What is the most likely cause?

Accepted Answer

The condition restricts the permission to the us-east-1 region only. The condition in the policy restricts the permission to requests coming through S3 in us-east-1 only (s3.us-east-1.amazonaws.com). For buckets in us-west-2, the viaService would be s3.us-west-2.amazonaws.com, so the condition fails. The key policy does not allow decrypt from other services, but the issue is region mismatch.

Answer

The key policy does not allow the role to use GenerateDataKey

Answer

The role does not have an IAM policy that allows kms:Decrypt

Answer

The role does not have permission to decrypt from S3