DEA-C01 Data Operations and Support Practice Test 20 — 15 Questions

Question 1

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and must be queryable using Amazon Athena. The engineer needs to optimize query performance and reduce costs. Which THREE actions would achieve this?

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

Accepted Answer

Partition the data by a commonly used filter column.. Option B is correct because partitioning the S3 data by a commonly used filter column (e.g., date or region) lets Athena prune irrelevant partitions via partition projection or the AWS Glue Data Catalog, drastically reducing the bytes scanned and thus query latency and cost. Option D is correct because splittable compression formats like Snappy (and also LZO/BZip2) allow Athena to split large objects across multiple readers, preserving parallelism while lowering storage and scan costs; non-splittable formats like Gzip force a single reader per file. Option E is correct because columnar formats such as Apache Parquet or ORC enable column pruning and predicate pushdown, so Athena reads only the needed columns and row groups, cutting both I/O and cost compared to row-based CSV/JSON. Option A is incorrect because many small files create excessive per-file overhead in S3 listing, Glue catalog, and Athena planning, hurting performance and raising costs rather than increasing useful parallelism. Option C is incorrect because S3 Select is a lightweight object-level filtering feature, not a replacement for Athena's distributed SQL engine over a data lake, and it cannot perform joins, aggregations, or catalog-based queries.

Answer

Store data in many small files to increase parallelism.

Answer

Use S3 Select instead of Athena for queries.

Question 2

A data engineer is troubleshooting a nightly ETL job that reads data from an RDS MySQL instance and writes to an S3 bucket in Parquet format. The job runs on an EMR cluster and uses PySpark. Recently, the job started failing with 'OutOfMemoryError' in the executor logs. The data volume has grown 30% in the last month. Which is the MOST efficient solution to resolve this issue without changing the code?

Accepted Answer

Increase the executor memory and memoryOverhead in the Spark configuration.. The OutOfMemoryError in executors indicates insufficient memory per executor to handle the increased data volume. Increasing 'spark.executor.memory' and 'spark.executor.memoryOverhead' directly addresses this by providing more heap and off-heap memory without any code changes. Option A is wrong because the RDS instance size does not affect executor memory; the bottleneck is in Spark processing. Option B is wrong because switching to AWS Glue would require code changes and may still need memory tuning, making it less efficient. Option D is wrong because adding core nodes increases parallelism but does not increase memory per executor, so the OOM could still occur.

Answer

Change the RDS instance to a larger size to reduce load.

Answer

Switch the ETL job to use AWS Glue with a larger WorkerType.

Answer

Increase the number of core nodes in the EMR cluster.

Question 3

A data engineer receives an alert that a Kinesis Data Stream has a 'WriteProvisionedThroughputExceeded' error. The stream has 5 shards with 1 MB/s write capacity per shard. The producer application is sending data at 8 MB/s sustained. What should the engineer do to resolve the issue?

Accepted Answer

Increase the number of shards from 5 to 10.. The 'WriteProvisionedThroughputExceeded' error indicates that the total write throughput to the Kinesis Data Stream exceeds the provisioned capacity. With 5 shards, each offering 1 MB/s write capacity, the total write capacity is 5 MB/s. The producer is sending 8 MB/s, which is above this limit. Increasing the number of shards to 10 raises the total write capacity to 10 MB/s, accommodating the sustained 8 MB/s throughput and resolving the throttling.

Answer

Reduce the record size to below 1 MB per record.

Answer

Enable enhanced fan-out on the stream.

Answer

Use Kinesis Firehose as an intermediary to buffer data.

Question 4

A data engineer is tasked with setting up a data pipeline that moves data from an on-premises Oracle database to Amazon S3 every hour. The network bandwidth is limited, and the engineer needs to ensure data consistency. Which AWS service should the engineer use?

Accepted Answer

AWS Database Migration Service (DMS) with change data capture (CDC).. AWS DMS with CDC is the correct choice because it can continuously replicate ongoing changes from an on-premises Oracle database to Amazon S3 while ensuring data consistency. CDC captures only the incremental changes (inserts, updates, deletes) after an initial full load, minimizing the data transferred over limited bandwidth and maintaining transactional integrity.

Answer

AWS DataSync.

Answer

Amazon Kinesis Data Firehose.

Answer

S3 Transfer Acceleration.

Question 5

A data engineer is monitoring an AWS Glue ETL job that processes data from an S3 bucket and writes to a Redshift table. The job completes successfully but takes longer than expected. The engineer notices that the job uses 10 DPUs and the data size is 500 GB. The job runs in standard mode. Which change would MOST reduce job duration?

Accepted Answer

Increase the number of DPUs to 20.. Increasing the number of DPUs from 10 to 20 allows the job to process data in parallel, reducing execution time. AWS Glue standard mode scales linearly with DPUs for ETL jobs that are not I/O bound. In this case, with 500 GB of data and 10 DPUs, the job is likely CPU-bound and can benefit from additional parallelism. Option B is incorrect because using a smaller worker type (G.1X) reduces available memory and CPU, worsening performance. Option C is incorrect because changing the output from Parquet (columnar, compressed) to CSV (row-based, uncompressed) increases data size and I/O, slowing the job. Option D is incorrect because reducing partitions can cause data skew and reduce parallelism, increasing runtime.

Answer

Use a smaller worker type like G.1X.

Answer

Change the output format from Parquet to CSV.

Answer

Reduce the number of partitions in the data.

Question 6

A data engineer is designing a data lake on Amazon S3. The data is ingested from multiple sources and must be queryable using Amazon Athena. The engineer needs to optimize query performance and reduce costs. Which THREE actions would achieve this?

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

Accepted Answer

Enable CloudTrail data events for S3 object-level operations.. Option D is correct because enabling default bucket encryption with SSE-KMS ensures all objects written to the S3 bucket are encrypted at rest using AWS KMS-managed keys, satisfying the data-at-rest encryption requirement. Option C is correct because CloudTrail data events capture object-level S3 operations such as GetObject and PutObject, providing the access logging needed for compliance auditing. Option A is incorrect because S3 Select only filters data at read time and does not provide encryption or access logging. Option B is incorrect because S3 Transfer Acceleration only speeds up uploads and downloads over long distances, not encryption or logging. Option E is incorrect because S3 Block Public Access prevents public exposure but does not encrypt data at rest or log access.

Answer

Enable S3 Select to filter data at read time.

Answer

Enable S3 Transfer Acceleration.

Answer

Enable S3 Block Public Access on the account.

Question 7

A company runs a data processing pipeline on Amazon EMR. The pipeline reads data from S3, processes it with Spark, and writes results back to S3. The engineer notices that the cluster is underutilized and wants to reduce costs. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Use Spot instances for task nodes.. Option A is correct because using Spot instances for task nodes is a standard EMR cost-optimization technique: task nodes perform only HDFS-non-persistent work (Spark executors), so they can be interrupted without data loss, and Spot capacity typically costs significantly less than On-Demand. Option B is correct because configuring the cluster to terminate after the job completes (a transient cluster, e.g., via --auto-terminate or a termination-protected=false setting) stops paying for idle EC2 instances once the batch pipeline finishes, directly addressing the underutilization. Option C is wrong because enlarging the master node does not improve processing throughput and increases cost, since the master only manages the cluster. Option D is wrong because EMRFS consistent view is a data-consistency feature for S3 reads/writes, not a cost-reduction mechanism. Option E is wrong because adding core nodes increases cost and capacity rather than reducing spend, and the cluster is already underutilized.

Answer

Change the master node to a larger instance type.

Answer

Enable EMRFS consistent view.

Answer

Increase the number of core nodes to improve parallelism.

Question 8

A data engineer is monitoring an Amazon Redshift cluster and notices that queries are taking longer than expected. The engineer checks the system tables and sees that many queries are waiting for 'WLM' resources. What is the most likely cause and recommended fix?

Accepted Answer

The WLM queue concurrency is set too low; increase the concurrency level.. When queries wait on WLM resources, it means they are queued because the workload management (WLM) queue does not have enough concurrency slots to run them. Increasing the concurrency level of the WLM queue allows more queries to run simultaneously, reducing wait time. This is the most direct fix for WLM queue waits.

Answer

The table sort keys are poorly designed; recreate tables with better sort keys.

Answer

The distribution style is set to ALL; change to KEY distribution.

Answer

The cluster is running low on disk space; resize the cluster.

Question 9

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 10

A data engineer needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited to 100 Mbps, and the data transfer must be completed within 5 days. What is the most cost-effective solution?

Accepted Answer

Use AWS Snowball Edge to physically ship the data.. With 10 TB of data and a 100 Mbps link, the theoretical transfer time over the internet is approximately 10 days (10 TB * 8 / 100 Mbps = 800,000 seconds ≈ 9.26 days), which exceeds the 5-day requirement. AWS Snowball Edge is the most cost-effective solution because it bypasses the network bottleneck entirely by physically shipping the data, and it is designed for large-scale data transfers where network constraints make online transfer impractical.

Answer

Use S3 Transfer Acceleration to speed up the transfer over the internet.

Answer

Use AWS DataSync over the internet to transfer the data.

Answer

Set up an AWS Direct Connect connection to increase bandwidth.

Question 11

An Amazon Kinesis Data Streams application is lagging behind. The data records are small (1 KB) and the shard count is 10. The consumer uses the KCL with default configuration. Which action will MOST effectively reduce the consumer lag?

Accepted Answer

Use Enhanced Fan-Out to provide dedicated throughput.. Enhanced Fan-Out provides each consumer with dedicated throughput (2 MB/s per shard) and eliminates the need for polling, which reduces latency and lag. Given that the consumer is lagging with default configuration and small records, Enhanced Fan-Out directly addresses the consumer-side bottleneck by providing dedicated read throughput, making it the most effective option. Option A is incorrect because the KCL does not support multiple workers per shard; each shard is processed by a single worker in a single-threaded manner. Option C may help if the shard is saturated with incoming data, but the problem is consumer lag, not data ingestion. Option D reduces data size but does not address the consumer processing speed.

Answer

Increase the number of KCL workers per shard (e.g., 2 workers per shard).

Answer

Increase the number of shards to 20.

Answer

Reduce the record size by compressing the data.

Question 12

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 13

A data engineer is using Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data is delivered in 5-minute intervals. However, the engineer notices that the data in S3 is often delayed by up to 30 minutes. Which configuration change would most likely reduce the delay?

Accepted Answer

Decrease the 'Buffer interval' from 300 seconds to 60 seconds.. The buffer interval determines the maximum time Firehose will wait before delivering data, regardless of buffer size. Decreasing it from 300 to 60 seconds forces more frequent deliveries, reducing the delay. Option B (compression) reduces data size, which could slow buffer filling and potentially increase delay if the buffer size trigger is not met. Option C (increasing buffer size) would cause Firehose to wait longer for the buffer to fill, increasing delay. Option D (dynamic partitioning) affects data organization, not delivery frequency, so it does not reduce delay.

Answer

Enable compression (GZIP) on the Firehose delivery stream.

Answer

Increase the 'Buffer size' from 5 MB to 50 MB.

Answer

Enable 'Dynamic partitioning' on the Firehose stream.

Question 14

A data engineer notices that an Amazon RDS for PostgreSQL instance's CPU utilization is consistently above 90% during business hours. The database is used for reporting queries. Which action should be taken FIRST to improve performance?

Accepted Answer

Enable Performance Insights and review slow queries.. The first step in diagnosing high CPU utilization on an RDS for PostgreSQL instance used for reporting queries is to identify the root cause. Enabling Performance Insights provides a detailed view of database load, wait events, and SQL query performance, allowing the data engineer to pinpoint slow or inefficient queries that are consuming CPU resources. Without this diagnostic data, any other action would be premature and could lead to unnecessary cost or complexity.

Answer

Enable Multi-AZ deployment for automatic failover.

Answer

Create a read replica to offload reporting queries.

Answer

Increase the instance size to a larger instance class.

Question 15

A data engineer is monitoring an Amazon RDS for PostgreSQL instance. The engineer wants to set up alerts for high CPU utilization and low free storage space. Which AWS services can be used together to achieve this? (Choose TWO.)

Accepted Answer

Amazon Simple Notification Service (SNS). Amazon CloudWatch [CORRECT] is the AWS monitoring service that collects RDS for PostgreSQL metrics such as CPUUtilization and FreeStorageSpace, and it lets you create alarms that trigger when those metrics cross defined thresholds. Amazon Simple Notification Service (SNS) [CORRECT] is the notification service that CloudWatch alarms publish to, delivering email, SMS, or other messages to the engineer when high CPU or low free storage is detected. Together, CloudWatch alarms plus an SNS topic form the standard alerting pipeline for RDS metric thresholds. AWS CloudTrail records API activity and audit events, not performance metrics, so it cannot detect high CPU or low storage. AWS Config evaluates resource configuration compliance and does not monitor runtime utilization metrics. Amazon Route 53 is a DNS and traffic-routing service and has no role in RDS metric alerting.

Answer

AWS CloudTrail

Answer

AWS Config

Answer

Amazon Route 53