DEA-C01 Practice Exam 63 — 20 Questions

Question 1

A data engineer is troubleshooting a failed AWS Glue job that reads from an S3 bucket and writes to an Amazon Redshift table. The error message indicates 'Access Denied'. Which TWO permissions are likely missing? (Choose TWO.)

Accepted Answer

s3:GetObject on the source S3 bucket. Options A and B are required for the Glue job to read from S3 and write to Redshift. Option C (Lambda) is not used. Option D (EC2) is not needed if the job uses a VPC endpoint. Option E (KMS) is unnecessary unless encryption is used.

Answer

kms:Decrypt on a KMS key

Answer

ec2:DescribeInstances on the Redshift cluster

Answer

lambda:InvokeFunction on a Lambda function

Question 2

A data engineer is designing a disaster recovery strategy for an Amazon Redshift data warehouse. The RPO (Recovery Point Objective) is 1 hour, and the RTO (Recovery Time Objective) is 2 hours. Which approach meets these requirements with the least operational overhead?

Accepted Answer

Configure automated cross-region snapshot copy to another region.. Option B is correct because automated snapshots to S3 with cross-region copy provide up-to-the-hour recovery and fast restore. Option A is incorrect because automated snapshots are default but not cross-region. Option C and D have higher overhead and may not meet RTO.

Answer

Set up a secondary Redshift cluster in another region and use AWS DMS for continuous replication.

Answer

Export the data to S3 daily using UNLOAD and copy to another region.

Answer

Enable automated snapshots with a retention period of 1 day.

Question 3

Refer to the exhibit. A data engineer runs the command on an object in S3. The engineer expected the object to have a tag 'type=raw' but sees no metadata. What is the likely cause?

Accepted Answer

Object tags are not returned by head-object; use get-object-tagging instead. Option B is correct because object tags are not returned by head-object; they require a separate get-object-tagging call. Option A is wrong because the command works. Option C is wrong because bucket policies affect access, not tag visibility. Option D is wrong because lifecycle rules do not remove tags.

Answer

The S3 bucket is in a different AWS Region

Answer

The bucket policy blocks reading tags

Answer

The object was created without tags because of lifecycle rules

Question 4

A company stores sensitive data in Amazon S3. To meet compliance requirements, they need to ensure that any data older than 1 year is automatically moved to a lower-cost storage class. Which S3 feature should they use?

Accepted Answer

S3 Lifecycle policies. Option B is correct because S3 Lifecycle policies automate transitioning objects between storage classes. Option A is wrong because S3 Glacier is a storage class, not a feature to automate transitions. Option C is wrong because S3 Intelligent-Tiering automatically optimizes costs but does not enforce a specific age-based transition. Option D is wrong because S3 Replication is for copying objects across buckets.

Answer

S3 Replication

Answer

S3 Glacier

Answer

S3 Intelligent-Tiering

Question 5

A data engineer is setting up a Redshift cluster and needs to ensure high availability. Which TWO actions should be taken?

Accepted Answer

Enable automatic replication across Availability Zones.. Options B and D are correct. Multi-node clusters with automatic replication provide high availability. Option A is wrong because single-node clusters are not HA. Option C is wrong because concurrency scaling improves performance, not HA. Option E is wrong because cross-region snapshot is disaster recovery, not HA.

Answer

Enable concurrency scaling.

Answer

Configure cross-region snapshot copy.

Answer

Use a single-node cluster to reduce complexity.

Question 6

A data engineer runs an AWS Glue Crawler that updates a table in the AWS Glue Data Catalog. The table is used by Amazon Athena queries. After the crawler runs, some queries start failing with the error 'HIVE_CANNOT_OPEN_SPLIT'. What is the most likely cause?

Accepted Answer

The crawler updated the schema and the partition metadata is inconsistent with the actual data.. Option C is correct because a schema change (e.g., new column or changed data type) can cause partition metadata to be inconsistent with actual data, leading to split errors. Option A is wrong because S3 permissions would cause access denied. Option B is wrong because the crawler can handle partitions. Option D is wrong because concurrent queries do not cause split errors.

Answer

The crawler does not have IAM permissions to read the S3 location.

Answer

The crawler created too many partitions, exceeding the Athena limit.

Answer

There are concurrent queries accessing the same table.

Question 7

A data engineering team notices that an AWS Glue ETL job fails intermittently with a 'ThrottlingException' error. The job reads from an Amazon S3 bucket and writes to an Amazon Redshift table. What is the MOST likely cause of this error?

Accepted Answer

The Redshift cluster's write throughput is exceeding its provisioned capacity.. Option A is correct because ThrottlingException when writing to Redshift typically indicates that the write throughput exceeds the cluster's capacity, causing API throttling. Option B is incorrect because S3 throttling would result in a different error (e.g., SlowDown). Option C is incorrect because Glue job throttling would be at the API level for job operations, not data operations. Option D is incorrect because insufficient memory would cause OutOfMemoryError, not ThrottlingException.

Answer

The S3 bucket's request rate is exceeding the bucket's performance limits.

Answer

The Glue job is exceeding the maximum number of concurrent runs allowed.

Answer

The Glue job's allocated memory is insufficient for the data volume.

Question 8

A data pipeline using AWS Glue jobs is failing with 'Insufficient capacity' errors for Spark executors. Which action should the data engineer take to resolve this?

Accepted Answer

Increase the number of workers (DPUs) in the Glue job configuration.. Option B is correct because the error indicates resource limits; increasing the number of workers (DPUs) can resolve capacity issues. Option A (reduce workers) would worsen the problem. Option C (increase timeout) does not address capacity. Option D (disable logging) does not help.

Answer

Reduce the number of workers in the Glue job configuration.

Answer

Increase the job timeout value.

Answer

Disable Spark UI logging.

Question 9

A company uses Amazon Kinesis Data Streams to ingest real-time clickstream data. A Lambda function processes each record. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' when writing results to a DynamoDB table. The data engineer has already increased the DynamoDB write capacity. What else can the engineer do to resolve the issue?

Accepted Answer

Increase the number of shards in the Kinesis stream.. Option B is correct because increasing the number of shards increases the number of Lambda concurrent invocations, reducing the batch size per invocation and easing pressure on DynamoDB. Option A is incorrect because increasing Lambda memory does not directly help with throttling. Option C is incorrect because increasing DynamoDB read capacity does not affect writes. Option D is incorrect because reducing batch size may increase throttling frequency.

Answer

Increase the Lambda function memory.

Answer

Increase the DynamoDB read capacity units.

Answer

Decrease the Lambda batch size to 1.

Question 10

A data engineer notices that an Amazon Athena query on a partitioned table in S3 scans more data than expected. The table is partitioned by year, month, day. The query includes a WHERE clause on a non-partition column but also filters on day='2023-01-01'. What is the most likely cause of the excessive data scan?

Accepted Answer

The partition column data type in the table definition does not match the actual partition folder names. Option D is correct because mismatched data types cause partition pruning to fail. Option A is wrong because partition pruning works with proper types. Option B is wrong because it would not affect scan size. Option C is wrong because sorting is irrelevant.

Answer

The data is stored in JSON format instead of Parquet

Answer

The table is not partitioned by the column used in the WHERE clause

Answer

The data is not sorted within partitions

Question 11

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon Aurora MySQL. After the migration, the data in Aurora is inconsistent with the source. The engineer needs to ensure ongoing replication with minimal downtime. Which solution should the engineer implement?

Accepted Answer

Configure a DMS task with change data capture (CDC). Option B is correct because using DMS with change data capture (CDC) captures ongoing changes and replicates them with minimal downtime. Option A is wrong because full load only captures a snapshot. Option C is wrong because AWS Schema Conversion Tool does not handle data replication. Option D is wrong because exporting and importing does not provide ongoing replication.

Answer

Use AWS Schema Conversion Tool (SCT) to convert the schema

Answer

Export the data from Oracle and import into Aurora using mysqldump

Answer

Perform a full load migration again

Question 12

Refer to the exhibit. A data engineer has attached this IAM policy to a user. The user reports being unable to upload files to my-bucket from an on-premises network with a public IP of 203.0.113.5. What is the issue?

Accepted Answer

The user's IP address is not within the allowed IP range. Option A is correct because the policy restricts access to the IP range 10.0.0.0/16, which is a private range, not the user's public IP. Option B is wrong because the actions are allowed. Option C is wrong because if encryption were required, there would be a condition. Option D is wrong because the resource includes the bucket.

Answer

The resource ARN does not include the bucket itself

Answer

The user does not have s3:PutObject permission

Answer

The bucket requires server-side encryption

Question 13

A data engineer uses Amazon EMR to run a Spark job that reads from S3 and writes to HDFS on the cluster. The job fails with an 'OutOfMemoryError: Java heap space' error in the executors. Which parameter adjustment should be made to resolve this?

Accepted Answer

Increase spark.executor.memory. Option A is correct because increasing spark.executor.memory allocates more heap space to executors. Option B is wrong because spark.driver.memory affects the driver, not executors. Option C is wrong because spark.sql.shuffle.partitions affects shuffle behavior, not memory. Option D is wrong because spark.default.parallelism controls task parallelism, not memory.

Answer

Increase spark.default.parallelism

Answer

Increase spark.sql.shuffle.partitions

Answer

Increase spark.driver.memory

Question 14

A data engineer is troubleshooting a DMS task that is replicating data from an on-premises Oracle database to an RDS for MySQL instance. The task is failing with 'ORA-1555: snapshot too old' error. What is the best course of action?

Accepted Answer

Increase the UNDO tablespace size and set UNDO_RETENTION to a higher value.. Option A is correct because the snapshot too old error occurs when UNDO segments are too small or retention is too short for the long-running transaction needed by CDC. Increasing undo tablespace size and retention time allows CDC to read consistent old data. Option B is wrong because increasing redo logs does not prevent snapshot too old errors. Option C is wrong because it would lose CDC capability. Option D is wrong because batch optimized mode may not work with CDC and does not address the root cause.

Answer

Disable full supplemental logging on the source tables.

Answer

Increase the size of the redo logs on the source database.

Answer

Enable batch optimized apply on the DMS task.

Question 15

A data engineer is designing a data pipeline that ingests millions of small JSON files (1-10 KB each) from an S3 bucket into Amazon Redshift. The current approach uses a Lambda function triggered by S3 events to call the Redshift COPY command for each file. This is causing high latency and throttling. Which alternative is MOST cost-effective and efficient?

Accepted Answer

Use Amazon Kinesis Data Firehose to buffer and write larger files to S3, then use a scheduled COPY command. Option D is correct because Kinesis Firehose can buffer the small files and write larger batches to S3, then use a scheduled COPY or Redshift Spectrum. Option A still processes each file individually. Option B is for streaming, not batch. Option C adds complexity and cost.

Answer

Use Amazon Kinesis Data Streams and a consumer to batch files before COPY

Answer

Increase the Lambda concurrency limit and memory

Answer

Use AWS Glue to merge files into larger Parquet files before loading

Question 16

A company uses Amazon Athena to query data stored in an S3 bucket. The data is partitioned by year, month, day, and hour. The data engineer notices that queries are scanning a large amount of data even with a WHERE clause on the partition columns. What is the MOST likely cause?

Accepted Answer

The table does not have partitions defined in the AWS Glue Data Catalog.. Option B is correct because if partitions are not defined in the table, Athena cannot perform partition pruning. Option A is wrong because S3 storage class does not affect scanning. Option C is wrong because too many partitions improve pruning, not hinder. Option D is wrong because compressed files reduce scan size, not increase.

Answer

The data has too many partitions, causing overhead.

Answer

The S3 bucket uses the S3 Glacier storage class.

Answer

The data files are compressed with GZIP.

Question 17

A data engineer is setting up a new Amazon Redshift cluster for a data warehouse. The engineer wants to ensure data durability and high availability. Which THREE features should the engineer consider? (Choose three.)

Accepted Answer

Cross-Region snapshot copy.. Options A, C, and E are correct. A: Automated snapshots provide point-in-time recovery. C: Cross-Region snapshot copy protects against regional failures. E: Multi-node clusters provide data replication within the cluster. Option B is wrong because Multi-AZ is not a feature of Redshift; it uses automatic failover within a single AZ. Option D is wrong because S3 Cross-Region Replication is for S3, not Redshift.

Answer

S3 Cross-Region Replication for Redshift data.

Answer

Multi-AZ deployment for automatic failover.

Question 18

A data engineer is troubleshooting a failed AWS Glue ETL job that reads from an S3 bucket. The job logs show the following error: 'java.lang.RuntimeException: java.lang.ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found'. Which TWO actions will resolve this issue?

Accepted Answer

Include the hadoop-aws jar as an extra jar in the Glue job configuration.. Options A and D are correct. The error indicates the S3A filesystem class is missing, which is part of the Hadoop AWS library. Adding the jar to the job's extra jars (A) or using a Glue version that includes the library (D) fixes it. Option B is wrong because changing S3 access mode to EMRFS is for EMR, not Glue. Option C is wrong because the error is a classpath issue, not an IAM issue. Option E is wrong because enabling S3 endpoint is a networking issue.

Answer

Enable VPC S3 endpoint for the Glue job.

Answer

Update the IAM role to allow access to S3.

Answer

Change the S3 access mode from S3A to EMRFS.

Question 19

A data engineer is troubleshooting a slow Amazon Redshift query. The EXPLAIN plan shows a 'Seq Scan' on a large table. What is the most likely cause?

Accepted Answer

The table does not have a proper sort key defined.. Option D is correct because 'Seq Scan' indicates a full table scan, which typically occurs when there is no suitable index (or in Redshift, no sort key). Option A (too many nodes) would not cause a Seq Scan. Option B (concurrent queries) could cause slowdown but not a Seq Scan specifically. Option C (WLM queue) affects concurrency, not scan type.

Answer

The cluster has too many nodes.

Answer

There are too many concurrent queries.

Answer

The workload management (WLM) queue is misconfigured.

Question 20

A data engineer is optimizing an Amazon Redshift cluster that runs a nightly ETL workload. The engineer notices that the query performance degrades over the week and improves after a VACUUM operation. Which action should the engineer take to automate this maintenance and minimize impact on performance?

Accepted Answer

Schedule a VACUUM command using a query scheduler like Amazon EventBridge.. Option B is correct because automating VACUUM using a scheduled query (e.g., via Amazon EventBridge or Redshift's built-in scheduler) ensures regular maintenance without manual intervention. Option A is wrong because disabling auto vacuum does not automate it. Option C is wrong because dropping and recreating tables is disruptive. Option D is wrong because manual VACUUM is not automated.

Answer

Run VACUUM manually only when performance degrades significantly.

Answer

Disable auto vacuum and run a manual VACUUM every night after the ETL.

Answer

Drop and recreate the tables weekly to avoid unsorted data.