DEA-C01 Data Operations and Support Practice Test 5 — 15 Questions

Question 1

A company is using Amazon DynamoDB as a data store for a real-time application. The application reads a single item by primary key and occasionally updates it. The data engineer notices high read latency during peak hours. Which TWO actions would most effectively reduce read latency?

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

Accepted Answer

Increase the read capacity units for the table.. Increasing read capacity units (RCUs) for the DynamoDB table directly allocates more read throughput, reducing throttling and latency during peak hours when read demand exceeds provisioned capacity. This ensures the application can handle the higher read load without experiencing request throttling, which is a primary cause of increased latency.

Answer

Enable DynamoDB global tables.

Answer

Add a local secondary index on the table.

Answer

Disable auto-scaling and set a fixed read capacity.

Question 2

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?

Accepted Answer

Partition the data by a commonly used filter column.. Partitioning data by a commonly used filter column (e.g., date, region) allows Athena to prune partitions during query execution, scanning only the relevant S3 prefixes. This reduces the amount of data read per query, directly lowering both query latency and cost, as Athena charges based on the volume of data scanned.

Answer

Store data in many small files to increase parallelism.

Answer

Use S3 Select instead of Athena for queries.

Question 3

A company is using AWS Glue ETL jobs to process data from Amazon S3 and write results back to S3. The jobs are failing intermittently with 'ThrottlingException' errors. Which TWO configurations would help reduce these errors?

Accepted Answer

Add retry logic with exponential backoff in the job script.. Adding retry logic with exponential backoff in the job script directly addresses transient 'ThrottlingException' errors by automatically retrying failed API calls after increasing delays. This is a standard best practice for handling service throttling in AWS Glue, as it reduces the request rate to stay within service limits without requiring infrastructure changes.

Answer

Decrease the number of DPUs for the job.

Answer

Enable GZIP compression on the output data.

Answer

Change the job type from Spark to Python shell.

Question 4

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 5

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 6

A company ingests IoT sensor data into an S3 bucket. Daily, a Lambda function reads new objects, processes them, and writes results to a DynamoDB table. Recently, the Lambda function started timing out after 15 minutes. The data volume has increased, and the function processes records one by one. Which solution would improve performance without significant cost increase?

Accepted Answer

Use S3 Batch Operations to invoke the Lambda function in parallel for each object.. S3 Batch Operations invokes the Lambda function for each object in parallel, efficiently handling increased volume without significant cost increase. Option A is incorrect because AWS Glue ETL jobs have startup overhead and may cost more. Option B is incorrect because increasing the timeout does not address the root cause of sequential processing; the function would still process records one by one and may still timeout. Option D is incorrect because increasing DynamoDB write capacity does not speed up the Lambda processing; the bottleneck is the sequential processing within the function.

Answer

Replace Lambda with an AWS Glue ETL job.

Answer

Increase the Lambda function timeout to 30 minutes.

Answer

Increase the DynamoDB write capacity units.

Question 7

A data engineer is monitoring a Redshift cluster that is experiencing slow query performance. The cluster has 4 dc2.large nodes. The engineer notices that disk space usage is at 85% across all nodes. Which action would MOST likely improve query performance?

Accepted Answer

Increase the number of nodes to 8.. At 85% disk usage on dc2.large nodes, the cluster is approaching the threshold where Redshift begins to automatically offload data to Amazon S3, causing significant performance degradation due to increased I/O and network latency. Adding more nodes (option C) increases both compute capacity and total disk space, reducing per-node disk pressure and allowing the cluster to keep more data local for faster query execution.

Answer

Change the table design to use DISTKEY and SORTKEY.

Answer

Enable compression on all columns.

Answer

Run the VACUUM command to reclaim space.

Question 8

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 9

A company uses a DynamoDB table with on-demand capacity for a gaming application. During a new game launch, the table experienced throttling errors. The engineer checks CloudWatch metrics and sees that the 'ConsumedWriteCapacityUnits' exceeded the 'ProvisionedWriteCapacityUnits' (on-demand uses the table's previous peak). The application is writing at 50,000 WCU but the table's peak was 30,000 WCU. What should the engineer do to resolve throttling?

Accepted Answer

Contact AWS Support to pre-warm the table for higher throughput.. DynamoDB on-demand capacity automatically scales based on traffic but has a maximum throughput limit determined by the table's previous peak usage. When a new peak exceeds this limit, throttling occurs until the table adapts. Contacting AWS Support to pre-warm the table raises the initial throughput limit, allowing higher bursts immediately. Option B is incorrect because increasing partition count does not directly increase the overall throughput limit; it affects distribution of throughput. Option A is incorrect because DAX is a caching layer that improves read performance, not write throughput. Option D is incorrect because switching to provisioned capacity would require setting WCU to 50,000, but this changes the billing model and may still require a limit increase; pre-warming is the direct solution for on-demand throttling.

Answer

Add a DynamoDB Accelerator (DAX) cluster in front of the table.

Answer

Increase the number of partitions by splitting the partition key.

Answer

Switch the table to provisioned capacity and set WCU to 50,000.

Question 10

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 11

A data engineer needs to ensure that a Redshift cluster can recover from a failure with minimal data loss. The cluster is used for reporting and can tolerate a few minutes of downtime. Which feature should the engineer enable?

Accepted Answer

Enable automated snapshots with a retention period of 1 day.. Automated snapshots in Amazon Redshift are taken at regular intervals (default every 8 hours or 5 GB of data changes) and retained for a specified period. Enabling automated snapshots with a retention period of 1 day ensures that, in the event of a failure, the cluster can be restored to the most recent snapshot, minimizing data loss to at most the snapshot interval. This aligns with the requirement for minimal data loss and tolerance for a few minutes of downtime, as restoring from a snapshot takes time but preserves recent data.

Answer

Configure cross-region snapshot copy.

Answer

Take manual snapshots every hour.

Answer

Enable Multi-AZ deployment.

Question 12

A data engineer is troubleshooting a Kinesis Data Analytics application that processes streaming data. The application is falling behind and has a high 'MillisBehindLatest' metric. The application uses a parallelism of 2. The source stream has 4 shards. What is the MOST likely cause and solution?

Accepted Answer

The application parallelism is too low; increase it to 4.. The 'MillisBehindLatest' metric indicates the application is not keeping up with the incoming data. With a source stream of 4 shards and a parallelism of only 2, the application cannot process data from all shards concurrently, leading to backpressure. Increasing parallelism to match the shard count (4) allows each shard to be processed by a separate task, reducing lag.

Answer

The application is using a JSON format; switch to Avro.

Answer

The source stream has too many shards; decrease to 2.

Answer

The output destination is slow; change to a faster sink.

Question 13

A data engineer is designing a data lake on Amazon S3 with sensitive data. The engineer needs to ensure that data at rest is encrypted and that access is logged for compliance. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Enable CloudTrail data events for S3 object-level operations.. Enabling CloudTrail data events for S3 object-level operations captures detailed logs of all read, write, and delete actions on objects, which is essential for compliance auditing. Option D is correct because enabling default encryption on the S3 bucket using SSE-KMS ensures that all objects stored in the bucket are encrypted at rest with AWS Key Management Service (KMS) keys, providing centralized control and auditability of encryption keys.

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 14

A data engineer is migrating a legacy data warehouse to Amazon Redshift. The engineer needs to load data from multiple sources efficiently. Which THREE services can be used to load data into Redshift? (Choose THREE.)

Accepted Answer

Use the COPY command to load from Amazon DynamoDB.. The COPY command can load data from Amazon DynamoDB (A) and Amazon S3 (E) directly into Amazon Redshift. AWS DMS (C) can continuously replicate data from multiple sources into Redshift, making it a valid service for loading data. Option B (Kinesis Data Firehose) does not deliver data directly to Redshift; it writes to S3 first, then Redshift uses COPY. Option D (S3 Transfer Acceleration) accelerates uploads to S3, not loading into Redshift.

Answer

Use Kinesis Data Firehose to deliver data directly to Redshift.

Answer

Use S3 Transfer Acceleration.

Question 15

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.. Using Spot instances for task nodes in Amazon EMR can significantly reduce costs, as Spot instances are spare EC2 capacity offered at up to 90% discount compared to On-Demand instances. Since task nodes are stateless and can be added or removed without affecting cluster stability, they are ideal candidates for Spot instances, allowing the engineer to lower expenses while maintaining processing capacity.

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.