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

Question 1

Refer to the exhibit. A data engineer runs the command on an Amazon S3 bucket used for data lake storage. The engineer is concerned about accidental overwrites of objects. What does the output indicate?

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

Accepted Answer

Versioning is enabled, so previous versions of objects are preserved.. The output shows that versioning is enabled on the S3 bucket, which means that when an object is overwritten or deleted, the previous version is retained as a noncurrent version. This protects against accidental overwrites because the original data remains recoverable. The exhibit likely displays a versioning status of 'Enabled' or shows version IDs.

Answer

Old versions will be automatically deleted after a retention period.

Answer

Objects are encrypted at rest by default.

Answer

MFA Delete is disabled, meaning anyone can delete objects permanently.

Question 2

A data engineering team uses AWS Glue ETL jobs to process data from Amazon S3. The jobs recently started failing with 'Access Denied' errors when writing to the output S3 bucket. What is the most likely cause?

Accepted Answer

The S3 bucket policy was modified to deny write access to the Glue job's IAM role.. The most likely cause is that the S3 bucket policy was modified to deny write access to the Glue job's IAM role. AWS Glue jobs assume an IAM role to access S3; if the bucket policy explicitly denies that role, the job will receive Access Denied errors when writing. Other options are less likely because KMS key access issues would typically produce different errors, default encryption is not required for writes, and out-of-memory errors would not manifest as Access Denied.

Answer

The KMS key used for server-side encryption is not accessible to the Glue job.

Answer

The S3 bucket does not have default encryption enabled.

Answer

The job ran out of memory due to large data volume.

Question 3

Refer to the exhibit. A data engineer sees this output from the AWS CLI for a failed Glue job. The job uses 10 workers of Standard type. What is the MOST appropriate action to resolve the OutOfMemoryError?

Accepted Answer

Change WorkerType to G.1X. The OutOfMemoryError occurs because each Standard worker has a memory limit of 4 GB. Increasing MaxCapacity to 20 does not increase memory per worker when NumberOfWorkers is explicitly set; it only increases the total DPU allocation but the job still uses 10 workers each with 1 DPU. The correct solution is to change the worker type to G.1X, which provides 2 DPUs (8 GB memory) per worker, effectively doubling the memory per worker and resolving the error. Option A (increase workers) adds parallelism but does not increase per-worker memory. Option B (reduce workers) decreases parallelism and may aggravate memory pressure. Option D (increase MaxCapacity) is ineffective when NumberOfWorkers is fixed.

Answer

Increase NumberOfWorkers to 20

Answer

Reduce NumberOfWorkers to 5

Answer

Increase MaxCapacity to 20

Question 4

Refer to the exhibit. This IAM policy is attached to a user who is trying to read the object s3://data-bucket/confidential/report.csv. The user's principal tag 'role' is set to 'analyst'. What will happen when the user attempts to read the object?

Accepted Answer

Denied because the condition in the Deny statement evaluates to true. The Deny statement applies when the role tag is not 'admin'. The user's tag is 'analyst', so the condition matches and access is denied. Option A is wrong because the Allow statement is overridden by the explicit Deny. Option B is wrong because the Deny applies to all actions in the confidential prefix. Option D is wrong because Deny overrides Allow.

Answer

Denied because the Deny statement covers all actions under confidential

Answer

Allowed because there is an explicit Allow and no explicit Deny that matches

Answer

Allowed because of the Allow statement for s3:GetObject

Question 5

A data engineer is designing a data pipeline that ingests streaming data from an IoT fleet using Kinesis Data Streams and processes it with a Lambda function. The Lambda function often times out when the data volume spikes. What is the most scalable solution?

Accepted Answer

Increase the number of shards and use a Kinesis Data Analytics application for windowed aggregation before Lambda.. Increasing the number of shards increases the parallelism of the stream, allowing higher throughput. Using Kinesis Data Analytics for windowed aggregation reduces the volume of data sent to Lambda, preventing timeouts during spikes. Option A is wrong because reducing batch size decreases the number of records per invocation, which can increase the number of Lambda invocations and overhead, but does not address the root cause of timeouts due to volume. Option B is wrong because increasing the Lambda timeout to 15 minutes allows the function to run longer, but it does not increase throughput or handle spikes efficiently; it only delays failures and is not scalable. Option C is wrong because increasing memory can improve performance, but setting reserved concurrency limits the maximum number of concurrent executions, which can throttle processing during spikes and reduce scalability.

Answer

Reduce the batch size in the event source mapping.

Answer

Increase the Lambda function timeout to 15 minutes.

Answer

Increase the Lambda function memory and set reserved concurrency.

Question 6

A data engineer needs to export data from an Amazon DynamoDB table to Amazon S3 for archival purposes. The export should be a one-time operation and must not impact the read capacity of the table. Which approach meets these requirements?

Accepted Answer

Use DynamoDB on-demand backup feature to export to S3. DynamoDB's on-demand backup feature can export table data directly to S3 without consuming any read capacity units, making it ideal for a one-time archival export. Option A (Scan operation) consumes read capacity and impacts table performance. Option B (AWS Glue ETL with DynamoDB connector) also uses Scan operations that consume read capacity. Option C (DynamoDB Streams to Lambda) is designed for continuous change capture, not one-time bulk export.

Answer

Use a Scan operation in a script to read all items and write to S3

Answer

Use AWS Glue ETL with a DynamoDB connector

Answer

Set up a DynamoDB Stream to Lambda that writes to S3

Question 7

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 8

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

Accepted Answer

Configure cross-region snapshot copy.. Option B (Configure cross-region snapshot copy) is correct because it replicates cluster snapshots to a second AWS Region, providing disaster recovery if the primary Region becomes unavailable, which is a key part of a high-availability strategy for Redshift. Option E (Deploy a multi-node cluster with at least two compute nodes) is correct because a multi-node cluster distributes data and query processing across multiple compute nodes, so the failure of a single node does not take down the entire cluster and Redshift can continue serving queries. Option A (Enable concurrency scaling) is incorrect because concurrency scaling adds transient compute capacity to handle bursts of concurrent queries; it improves performance and scalability, not availability. Option C (Enable automatic replication across Availability Zones) is incorrect because Redshift does not offer a user-enabled AZ replication feature; a Redshift cluster resides in a single AZ, and cross-AZ resilience is achieved through features like Multi-AZ deployments (where supported) or snapshot-based recovery, not a toggle called automatic AZ replication. Option D (Use a single-node cluster to reduce complexity) is incorrect because a single-node cluster has no compute-node redundancy, so it is the opposite of a high-availability design.

Answer

Enable concurrency scaling.

Answer

Enable automatic replication across Availability Zones.

Answer

Use a single-node cluster to reduce complexity.

Question 9

A company is using Amazon EMR to run Spark jobs. The jobs are failing due to memory issues. Which THREE configurations can help mitigate out-of-memory errors?

Accepted Answer

Tune Spark memory configurations like spark.executor.memory and spark.memory.fraction.. The correct options are C, D, and E. Tuning Spark memory configurations (C) such as spark.executor.memory and spark.memory.fraction directly controls memory allocation within executors. Increasing the instance type to one with more memory per node (D) provides additional physical memory for Spark workloads. Enabling Spark dynamic allocation (E) allows the cluster to automatically adjust the number of executors based on workload, which helps prevent memory pressure from over-allocation. Option A is incorrect because instance store volumes are used for temporary data storage (e.g., shuffle spills) but do not address memory constraints; they may help with disk I/O but not OOM errors. Option B is incorrect because increasing vCPUs increases parallelism, which can actually worsen memory contention if each task consumes significant memory, potentially leading to more OOM errors.

Answer

Configure instance store volumes for intermediate shuffle data.

Answer

Use instances with more vCPUs to process more tasks in parallel.

Question 10

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. Amazon Kinesis Data Firehose can buffer the incoming small JSON files from S3 (via S3 event notifications) and write larger aggregated files to S3. A scheduled COPY command then efficiently loads these larger files into Amazon Redshift, reducing the number of COPY operations and avoiding Lambda throttling. This approach is cost-effective as Firehose charges only for data volume processed, and it eliminates the need for custom batching logic. Other options either process files individually (A, C) or incur higher costs with AWS Glue (D).

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 11

A data engineer needs to monitor the performance of an RDS for PostgreSQL database. Which THREE CloudWatch metrics are most useful for this purpose?

Accepted Answer

CPUUtilization. CPUUtilization is a critical metric for monitoring RDS for PostgreSQL because high CPU usage can indicate inefficient queries, insufficient instance size, or contention. Sustained high CPU can lead to performance degradation and increased query latency, making it essential for capacity planning and troubleshooting.

Answer

FreeStorageSpace

Answer

NetworkThroughput

Question 12

A company runs an Amazon EMR cluster that processes data from S3 and writes results back to S3. The cluster uses Spot Instances for task nodes. Some tasks are failing due to Spot Instance interruptions. What is the BEST way to handle this without manual intervention?

Accepted Answer

Enable automatic node replacement in the EMR cluster. EMR's automatic node replacement feature detects when Spot Instance task nodes are interrupted and provisions replacement nodes from the same instance group, allowing the cluster to self-heal without operator action. Because EMR resubmits the failed task attempts on the new nodes, the job continues and the cluster stays at its target capacity. This directly addresses the requirement of handling Spot interruptions without manual intervention.

Answer

Manually relaunch the cluster after failures

Answer

Configure the application to checkpoint to S3 every few minutes

Answer

Use only On-Demand instances for task nodes

Question 13

A company uses AWS Glue DataBrew to clean and transform data. A data engineer notices that a DataBrew recipe step that should remove duplicates is not working as expected. The dataset has millions of rows. What is the MOST likely reason?

Accepted Answer

The dataset is not sorted by the columns used for deduplication. AWS Glue DataBrew's 'Remove duplicate rows' recipe step operates by comparing each row to the immediately preceding row in the dataset. This means the data must be sorted by the columns that define a duplicate before the step is applied. If the dataset is not sorted on those key columns, identical rows that are not adjacent will not be detected as duplicates, and the step will appear to fail. Sorting the data first ensures that all duplicate rows become contiguous, allowing the step to remove them correctly.

Answer

The data source is an S3 bucket with a large number of files

Answer

The dataset contains null values in the key columns

Answer

The DataBrew project is using a sampling of the data

Question 14

Refer to the exhibit. A data engineer sees this error in CloudWatch Logs from an AWS Glue ETL job. The job reads from an S3 location that contains both .parquet and .csv files. What is the most likely cause?

Accepted Answer

The job is reading a CSV file that was incorrectly placed in the directory with .parquet extension.. The error indicates that the job encountered an object that is not a valid Parquet file. Since the S3 location contains both .parquet and .csv files, the Glue job likely attempted to read a CSV file as if it were Parquet, causing the invalid Parquet error. Option C is correct because the CSV file was incorrectly placed in the directory with a .parquet extension (or the job's schema inference expects all files to be Parquet). Option A is incorrect because the error is about format, not a missing file. Option B is incorrect because the error is about the object's format, not a permissions issue. Option D is incorrect because insufficient memory would typically cause out-of-memory or capacity errors, not an invalid Parquet error.

Answer

The S3 object was deleted during the job execution.

Answer

The IAM role does not have permission to read the S3 object.

Answer

The Glue job does not have enough memory to parse the Parquet file.

Question 15

A data engineer needs to monitor the number of records processed by an Amazon Kinesis Data Analytics application and trigger an alarm if the count drops below a threshold over 5 minutes. Which CloudWatch metric should be used?

Accepted Answer

millisBehindLatest (from KinesisDataAnalytics). 'millisBehindLatest' is a metric published by KinesisDataAnalytics that indicates how far behind the application is in processing records. Monitoring this metric allows the engineer to set an alarm when the number of records processed drops, as a high millisBehindLatest value indicates the application is falling behind. Option B (IncomingRecords) is from Kinesis Streams, not KinesisDataAnalytics. Option C (DPUCount) is a Glue metric. Option D (IncomingBytes) is a Kinesis Firehose metric.

Answer

IncomingRecords (from Kinesis Streams)

Answer

DPUCount (from Glue)

Answer

IncomingBytes (from Kinesis Firehose)