DEA-C01 Practice Test 29 — 15 Questions

Question 1

A data engineer is monitoring an Amazon EMR cluster and notices that the cluster is running out of disk space on the core nodes. Which action can be taken to resolve this issue?

Accepted Answer

Increase the EBS volume size attached to core nodes. Increasing the EBS volume size attached to core nodes directly adds storage capacity, resolving the disk space issue. Option A is wrong because reducing HDFS data retention may free space but does not increase available disk space; it could result in data loss. Option B is wrong because changing to a compute-optimized instance type affects CPU and memory, not storage. Option D is wrong because Spot Instances are a pricing model and do not add disk space.

Answer

Reduce the retention period of data stored on HDFS

Answer

Change the core node instance type to a compute-optimized type

Answer

Use Spot Instances for core nodes

Question 2

A company runs a nightly Amazon EMR job that processes data from S3 and writes results back to S3. The job fails with 'OutOfMemoryError' in the reduce phase. The cluster currently uses 5 m5.xlarge instances. Which cost-effective change should the data engineer make?

Accepted Answer

Increase the number of reducers (mapreduce.reduce.tasks) and keep the same instance type.. Increasing the number of reducers (mapreduce.reduce.tasks) distributes the memory load across the existing 5 m5.xlarge instances, reducing the per-reducer memory pressure and preventing OutOfMemoryError without adding cost. Option A (adding more core nodes) increases cost without directly addressing reducer memory. Option C (reducing input data) may affect completeness and is not a proper fix. Option D (switching to r5.xlarge) is more expensive per instance, making it less cost-effective.

Answer

Increase the number of core nodes to 10.

Answer

Reduce the input data size by filtering early in the job.

Answer

Switch to r5.xlarge instances for more memory per instance.

Question 3

Which TWO actions should a data engineer take to optimize Amazon S3 query performance for Amazon Athena when dealing with large Parquet files? (Choose 2.)

Accepted Answer

Optimize file sizes to be around 64 MB to 256 MB. The correct answers are D and E. Optimizing file sizes to be around 64 MB to 256 MB (D) reduces the overhead of Athena reading many small files or processing overly large files, balancing parallelism and efficiency. Partitioning data by frequently filtered columns (E) allows Athena to prune partitions and scan less data, improving query performance. Option A (single large file) hinders parallelism and query performance. Option B (GZIP compression) is already supported by Parquet and does not inherently optimize query performance beyond the file size considerations. Option C (many small files) increases metadata overhead and degrades performance.

Answer

Store data in a single large file without partitioning

Answer

Use GZIP compression on the Parquet files

Answer

Split large files into many small files

Question 4

A data engineer is troubleshooting an Amazon EMR cluster that has been running for several days. The cluster uses Amazon S3 as the data source and HDFS for intermediate storage. The engineer notices that some tasks fail with 'Java heap space' errors. Which TWO actions should the engineer take to resolve this issue?

Accepted Answer

Increase the maximum Java heap size for the task nodes (mapreduce.map.java.opts).. Options C and D are correct. Increasing the maximum Java heap size (mapreduce.map.java.opts) gives each task more memory to avoid heap space errors. Increasing YARN memory overhead (yarn.nodemanager.resource.memory-mb) allows containers to request more memory from YARN. Option A is incorrect because EMRFS consistent view addresses S3 consistency, not memory. Option B is incorrect because increasing the number of containers per node without increasing total memory reduces memory per container, worsening heap issues. Option E is incorrect because decreasing YARN container size reduces available memory, counterproductive for out-of-memory errors.

Answer

Enable EMRFS consistent view for S3.

Answer

Increase the number of containers per node.

Answer

Decrease the YARN container size.

Question 5

A data pipeline uses AWS Glue to process data from Amazon S3. The job fails with an 'OutOfMemoryError' during the transformation phase. Which action should the data engineer take to resolve this issue?

Accepted Answer

Increase the number of DPUs (Data Processing Units) for the Glue job.. The OutOfMemoryError occurs because the Glue job does not have enough memory allocated. Increasing the number of DPUs (Data Processing Units) increases both memory and processing capacity, directly resolving the issue. Option A (S3 server-side encryption) affects data security, not memory. Option B (increasing data partitions) may help parallelism but does not directly increase memory per executor. Option C (changing to Parquet) can reduce data volume but does not guarantee sufficient memory for transformation.

Answer

Enable S3 server-side encryption.

Answer

Increase the number of partitions in the input data.

Answer

Change the data format from CSV to Parquet.

Question 6

A company runs an Amazon Redshift cluster for analytics. During peak hours, query performance degrades significantly. The data engineer notices that disk space usage is above 80% on many nodes. Which of the following is the MOST effective long-term solution to improve query performance?

Accepted Answer

Resize the cluster to include additional nodes.. Resizing the cluster to include additional nodes increases both storage and compute capacity, directly addressing the high disk usage and improving query performance. Increasing WLM queue slots (Option A) only manages concurrency but does not add capacity. Compression encoding (Option C) reduces storage but may not alleviate immediate performance degradation, and is not a long-term solution for capacity. Running VACUUM (Option D) reclaims space from deleted rows but does not add new capacity.

Answer

Increase the workload management (WLM) queue slots.

Answer

Apply compression encoding to all columns.

Answer

Run the VACUUM command to reclaim space.

Question 7

A company stores sensitive customer data in an S3 bucket. The data engineer needs to ensure that all data is encrypted at rest. Which S3 feature should be enabled?

Accepted Answer

Default encryption. Default encryption ensures that all new objects written to the bucket are encrypted at rest using SSE-S3, SSE-KMS, or SSE-C. Option A is incorrect because S3 Versioning tracks object versions but does not encrypt data. Option B is incorrect because S3 Block Public Access controls public access, not encryption. Option C is incorrect because the aws:SecureTransport condition enforces encryption in transit, not at rest.

Answer

S3 Versioning

Answer

S3 Block Public Access

Answer

Bucket policy requiring aws:SecureTransport

Question 8

A company is using Amazon Athena to query data in an S3 bucket. Queries are failing with the error 'HIVE_PATH_ALREADY_EXISTS'. The data is partitioned by year, month, day. What is the MOST likely cause?

Accepted Answer

A partition was manually added to the Glue Data Catalog that already exists. The error 'HIVE_PATH_ALREADY_EXISTS' occurs in Athena when attempting to add a partition (via ALTER TABLE ADD PARTITION or MSCK REPAIR TABLE) that already exists in the Glue Data Catalog. Option B (inconsistent data format) would cause schema mismatch errors like 'HIVE_PARTITION_SCHEMA_MISMATCH', not this error. Option C (empty S3 location) would not cause this error; queries might succeed but return no results. Option D (lack of s3:ListBucket permission) would cause permission errors like 'Access Denied'.

Answer

The data format in the partition is inconsistent with the table schema

Answer

The S3 location for the partition is empty

Answer

The IAM role used by Athena lacks s3:ListBucket permission on the bucket

Question 9

A data engineer needs to automate the backup of an Amazon RDS for PostgreSQL database. Which AWS service can be used to schedule and manage the backups?

Accepted Answer

AWS Backup. AWS Backup is a fully managed backup service that can automate backups of RDS databases. Option A (Amazon S3) is incorrect because S3 is an object storage service, not a backup scheduling service. Option B (AWS Lambda) is incorrect because while Lambda can be used for custom automation, it is not the primary managed service for backup scheduling. Option D (Amazon CloudWatch) is incorrect because CloudWatch is for monitoring and observability, not backup management.

Answer

Amazon S3

Answer

AWS Lambda

Answer

Amazon CloudWatch

Question 10

A data engineer is running a Glue ETL job that reads from a JDBC source and writes to S3 in Parquet format. The job is slow and the engineer notices that the number of DPUs used is low. What can be done to improve performance?

Accepted Answer

Increase the number of workers (MaxCapacity) in the job configuration.. Increasing the number of workers (MaxCapacity) directly increases the parallelism of the Glue ETL job. Since the DPU usage is low, the job is not fully utilizing available resources; adding workers allows more concurrent processing, thus improving performance. Option A: Disabling job bookmarks avoids reading metadata but does not address low DPU usage and may cause reprocessing. Option B: Push-down predicates filter data at the source, reducing data volume, but the primary bottleneck here is parallelism, not data volume. Option D: Changing output to CSV would increase overhead and worsen performance because Parquet is columnar and compressed, while CSV is not.

Answer

Disable job bookmarks to avoid reading metadata.

Answer

Use push-down predicates to filter data at the source.

Answer

Change the output format to CSV to reduce CPU overhead.

Question 11

A company uses Amazon DynamoDB as the primary data store for a web application. The application experiences high read latency. Which TWO actions can improve read performance?

Accepted Answer

Enable DynamoDB Global Tables. (Enable DynamoDB Global Tables) is correct because Global Tables provide local read replicas in multiple AWS Regions, reducing read latency for users accessing from different geographic locations. Option C (Enable DynamoDB Accelerator (DAX)) is correct because DAX is an in-memory cache that significantly reduces read latency for frequently accessed items. Option A is incorrect because adding a Global Secondary Index (GSI) improves query flexibility but does not directly reduce read latency for primary key lookups. Option D is incorrect because DynamoDB Streams capture changes to the table for event-driven processing, not for reducing read latency. Option E is incorrect because increasing write capacity units only affects write throughput, not read performance.

Answer

Add a Global Secondary Index (GSI)

Answer

Enable DynamoDB Streams

Answer

Increase the write capacity units

Question 12

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). Configuring a DMS task with Change Data Capture (CDC) enables ongoing replication of changes from the source Oracle database to the target Aurora MySQL database with minimal downtime, ensuring consistency. Option A is incorrect because AWS Schema Conversion Tool (SCT) only converts schema and does not handle data replication. Option B is incorrect because mysqldump provides a one-time export/import, not ongoing replication. Option D is incorrect because performing a full load migration again would disrupt operations and would not capture ongoing changes.

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 13

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.. The 'Insufficient capacity' error for Spark executors indicates that the Glue job is running out of resources (DPUs). Increasing the number of workers (DPUs) provides more compute capacity, allowing the job to allocate sufficient executors. Option A (reducing workers) would worsen the issue. Option B (increasing timeout) does not add resources. Option C (disabling Spark UI) does not affect capacity. Therefore, increasing the number of workers (DPUs) is the correct resolution.

Answer

Reduce the number of workers in the Glue job configuration.

Answer

Increase the job timeout value.

Answer

Disable Spark UI logging.

Question 14

A data engineer is configuring AWS Glue jobs to access data stored in Amazon S3. The data is encrypted using server-side encryption with AWS KMS (SSE-KMS). The Glue job needs to read and write data to the S3 bucket. Which IAM policy statement should be added to the Glue job's IAM role to allow it to use the KMS key?

Accepted Answer

{"Effect":"Allow","Action":["kms:Decrypt","kms:GenerateDataKey"],"Resource":"*"}. To read and write data encrypted with SSE-KMS, AWS Glue needs both `kms:Decrypt` (to read existing encrypted data) and `kms:GenerateDataKey` (to create a new data key for writing encrypted data). `kms:GenerateDataKey` is required because S3 uses a data key to encrypt objects, and the caller must generate that key via KMS. Option B correctly includes both actions, allowing the Glue job to perform read and write operations on the SSE-KMS encrypted bucket.

Answer

{"Effect":"Allow","Action":["kms:Decrypt"],"Resource":"*"}

Answer

{"Effect":"Allow","Action":["kms:Decrypt","kms:ReEncrypt"],"Resource":"*"}

Answer

{"Effect":"Allow","Action":["kms:Decrypt","kms:Encrypt"],"Resource":"*"}

Question 15

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.. If partitions are not defined in the table, Athena cannot perform partition pruning. Option A is wrong because too many partitions improve pruning, not hinder scanning. Option C is wrong because S3 storage class does not affect scanning. 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.