DEA-C01 Practice Exam 54 — 20 Questions

Question 1

Which THREE factors should a data engineer consider when choosing between AWS Glue and Amazon EMR for a data transformation job? (Choose three.)

Accepted Answer

The level of control over the execution environment and dependencies. When choosing between AWS Glue and Amazon EMR for a data transformation job, key considerations include: the level of control over the execution environment (EMR offers more customization, while Glue is managed), the deployment model (Glue is serverless, EMR is cluster-based), and the cost structure (Glue charges per DPU, EMR charges per EC2 instance). Options A and B are not differentiating factors because both services support Apache Spark and can output to S3.

Answer

The ability to output results to Amazon S3

Answer

The support for Apache Spark

Question 2

A company uses Amazon Kinesis Data Streams to ingest IoT sensor data. The data is processed by an AWS Lambda function that transforms the records and writes to an Amazon S3 bucket. Recently, the Lambda function has been failing with 'Rate exceeded' errors for the S3 PUT API calls. The data volume is 10 MB/s with average record size 2 KB. What should be done to resolve this issue?

Accepted Answer

Add a random prefix to the S3 object key to distribute writes across multiple prefixes. The 'Rate exceeded' error for S3 PUT API calls indicates that the Lambda function is hitting S3 request rate limits. S3 buckets have a default limit of 3,500 PUT requests per second per prefix. With a data volume of 10 MB/s and an average record size of 2 KB, the Lambda function is generating approximately 5,000 PUT requests per second (10 MB/s ÷ 2 KB), which exceeds the per-prefix limit. Adding a random prefix to the S3 object key distributes writes across multiple prefixes, effectively increasing the aggregate request rate limit to 3,500 PUT requests per second per prefix, thereby resolving the throttling issue.

Answer

Switch to Amazon Kinesis Data Firehose to write to S3

Answer

Increase the Lambda function's reserved concurrency

Answer

Increase the number of Kinesis shards

Question 3

A data engineer notices that an Amazon Kinesis Data Firehose delivery stream is failing to deliver data to an Amazon S3 bucket. The CloudWatch metrics show 'DeliveryToS3.Success' is 0 and 'S3.BucketExists' is 1. What is the MOST likely cause?

Accepted Answer

The IAM role for Firehose lacks s3:PutObject permission.. The metric 'S3.BucketExists' is 1, confirming the S3 bucket exists, so the issue is not bucket existence. With 'DeliveryToS3.Success' at 0, the failure is in the write operation. The IAM role assumed by Firehose must have the s3:PutObject permission to deliver data; lacking it would cause all delivery attempts to fail silently, matching the observed metrics.

Answer

The S3 bucket has an ACL that denies access to Firehose.

Answer

The Firehose delivery stream Lambda transformation function is failing.

Answer

The S3 bucket does not exist.

Question 4

A company wants to use AWS Glue to transform data stored in Amazon S3. The data is partitioned by date and includes both CSV and Parquet files. The transformation should be optimized for cost and performance. Which THREE actions should the data engineer take? (Choose THREE.)

Accepted Answer

Use partition pruning by filtering on the date column in the ETL script.. Option B is correct because partition pruning by filtering on the date column lets AWS Glue read only the relevant S3 partitions instead of scanning the entire dataset, which directly reduces I/O, runtime, and cost. Option C is correct because Glue job bookmarks persist state between runs so the job processes only new or changed data since the last run, avoiding reprocessing of already-transformed partitions and lowering both execution time and DPU consumption. Option E is correct because converting CSV files to Parquet enables columnar storage and compression, so Glue reads less data and benefits from predicate pushdown, improving performance and reducing cost compared with row-based CSV. Option A is not appropriate because running a crawler before every job run adds unnecessary cost and time, and the schema can be supplied directly or updated only when it changes. Option D is not appropriate because maximizing DPUs increases cost rather than optimizing it, and the required performance can be achieved through pruning, bookmarks, and columnar formats.

Answer

Run a crawler to update the schema before each job run.

Answer

Increase the number of DPUs to the maximum allowed.

Question 5

A data engineer is configuring a data lake on Amazon S3 that contains sensitive customer information. The company requires that all access to this data be logged and monitored, and that any data shared with external partners must be anonymized before leaving the S3 bucket. Which combination of AWS services should the engineer use to meet these requirements? (Choose THREE.)

Accepted Answer

AWS Lake Formation. AWS Lake Formation (B) is correct because it provides fine-grained access control and data anonymization capabilities for data lakes on Amazon S3. It allows you to define column-level and row-level security policies, and can automatically anonymize sensitive data (e.g., via masking or tokenization) before it is shared with external partners, ensuring compliance with data governance requirements.

Answer

AWS WAF

Answer

AWS Direct Connect

Question 6

A data engineer is troubleshooting an AWS Glue job that reads from an Apache Kafka topic using a Glue connector. The job fails with 'TimeoutException'. The Kafka cluster is in a VPC. Which step should the engineer take FIRST?

Accepted Answer

Check the security group and network ACLs associated with the Glue job's VPC.. A TimeoutException from a Glue job reading Kafka in a VPC almost always indicates a network connectivity problem between the Glue job's ENIs and the Kafka brokers. The first step is to verify that the security groups and network ACLs allow traffic on the Kafka ports (typically 9092/9093) between the Glue job's subnet and the Kafka cluster's subnet. This is the most common root cause and the fastest to check.

Answer

Increase the Kafka consumer session timeout.

Answer

Update the Glue connector to the latest version.

Answer

Change the Glue job type from Spark to Python Shell.

Question 7

A company is using AWS Glue to process data stored in Amazon S3. The Glue job runs successfully but takes longer than expected. Which TWO actions can reduce the job runtime?

Accepted Answer

Increase the number of DPUs allocated to the job. Option B is correct because increasing the number of DPUs (Data Processing Units) allocated to a Glue job adds more compute capacity and parallelism, allowing Spark executors to process partitions concurrently and thereby reducing overall runtime. Option E is correct because partitioning the input data in S3 (for example, by date or category) lets Glue's Spark engine prune irrelevant partitions and read only the data it needs, cutting I/O and shuffle overhead. Option A is wrong because disabling job bookmarks causes Glue to reprocess already-processed data, which typically increases runtime rather than reducing it. Option C is wrong because reducing the number of workers lowers parallelism and generally makes the job slower. Option D is wrong because Python shell jobs are single-node, non-distributed, and intended for lightweight scripts, so they cannot efficiently process large datasets that a distributed Spark job handles.

Answer

Disable job bookmarking

Answer

Reduce the number of workers

Answer

Change the job type from Spark to Python shell

Question 8

A data engineer is setting up an Amazon Kinesis Data Firehose delivery stream to load data into Amazon Redshift. The data is coming from an application that produces JSON records. The engineer needs to transform the data to match the Redshift table schema. Which approach is the MOST cost-effective and requires the least operational overhead?

Accepted Answer

Configure a Lambda function in the Firehose delivery stream to transform records before delivery.. Kinesis Data Firehose natively supports invoking a Lambda function as a transformation step within the delivery stream. This allows the engineer to write a simple Lambda function that parses the incoming JSON records and transforms them to match the Redshift table schema, all without provisioning or managing any additional infrastructure. This approach is the most cost-effective (pay per invocation) and requires the least operational overhead since Firehose handles the orchestration, retries, and delivery to Redshift automatically.

Answer

Use AWS Glue as a transformation step between Firehose and Redshift, with a trigger on S3.

Answer

Use Kinesis Data Firehose with direct PUT to Redshift and rely on Redshift's COPY command to transform.

Answer

Use the Kinesis Client Library (KCL) to consume the stream, transform in an EC2 instance, and then load to Redshift.

Question 9

Which TWO actions can help improve the read performance of an Amazon DynamoDB table that is experiencing throttling? (Choose two.)

Accepted Answer

Increase the provisioned read capacity units (RCUs) for the table.. Option B is correct because DynamoDB throttling on reads occurs when request rate exceeds the table's provisioned read capacity units (RCUs); increasing the provisioned RCUs directly raises the available read throughput and relieves the throttling. Option C is correct because eventually consistent reads consume only half the RCUs of strongly consistent reads (1 RCU per 4 KB vs. 1 RCU per 2 KB), so switching to eventually consistent reads effectively doubles the read throughput available for the same provisioned capacity. Option A is wrong because DAX is designed to accelerate read-heavy workloads by caching, not write-heavy ones, and it does not address the stated read throttling. Option D is wrong because adding a GSI does not increase the base table's read capacity and actually adds its own separate capacity costs and potential throttling. Option E is wrong because auto-scaling with a lower minimum capacity would reduce available RCUs and worsen, not improve, read throttling.

Answer

Enable DynamoDB Accelerator (DAX) for write-heavy workloads.

Answer

Add a global secondary index (GSI) with a different partition key.

Answer

Enable auto-scaling with a lower minimum capacity.

Question 10

A data engineer is monitoring an Amazon Kinesis Data Stream used to ingest clickstream data. The engineer notices that the stream's 'WriteProvisionedThroughputExceeded' metric is frequently above zero. Which TWO actions could help mitigate this issue? (Choose TWO.)

Accepted Answer

Increase the number of shards in the stream.. Option A is correct because WriteProvisionedThroughputExceeded indicates that producers are exceeding the stream's ingest capacity, and each shard supports a fixed write throughput of 1 MB/s or 1,000 records/s, so adding shards (resizing/shard splitting) increases total write capacity. Option D is correct because a hot shard caused by an uneven partition key (for example, a constant or low-cardinality key) can throttle writes even when aggregate capacity is sufficient; adding a random prefix to the partition key spreads records across more shards, balancing the load. Option B is incorrect because the retention period only controls how long data is stored and has no effect on write throughput capacity. Option C is incorrect because decreasing shards reduces total write capacity and would worsen throttling. Option E is incorrect because enhanced fan-out increases read throughput for consumers (dedicated 2 MB/s per consumer per shard) and does not address write-side throttling.

Answer

Reduce the data retention period to free up capacity.

Answer

Decrease the number of shards to reduce overhead.

Answer

Enable enhanced fan-out on the stream.

Question 11

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 12

Match each AWS data migration tool to its primary function.

Accepted Answer

AWS Database Migration Service (DMS): Migrates databases to AWS with minimal downtime. AWS DMS migrates databases with minimal downtime; AWS Snowball transfers large datasets physically; AWS DataSync automates online data transfer. Common confusions include swapping tool functions or attributing schema conversion to DataSync instead of SCT.

Answer

AWS Database Migration Service (DMS): Transfers large datasets via physical devices

Answer

AWS Snowball: Migrates databases to AWS with minimal downtime

Answer

AWS DataSync: Converts database schema to target platform

Question 13

Match each AWS monitoring tool to its primary use.

Accepted Answer

CloudWatch: Monitor metrics, logs, and alarms. CloudWatch monitors metrics and logs, CloudTrail audits API calls, X-Ray traces requests, and Trusted Advisor provides optimization recommendations. Common confusions include swapping the roles of CloudWatch and CloudTrail.

Answer

CloudWatch: Auditing API activity

Answer

CloudTrail: Monitor metrics, logs, and alarms

Question 14

Arrange the steps to implement data encryption at rest for an Amazon Redshift cluster using AWS KMS.

Accepted Answer

Step 1: Create a KMS key. Step 2: Launch a new Amazon Redshift cluster. Step 3: Enable encryption and specify the KMS key during launch. Step 4: Verify that the cluster is encrypted at rest.. First, create the KMS key. Then launch a new encrypted cluster, specify the key, configure, and verify encryption.

Answer

Step 1: Launch a new Amazon Redshift cluster. Step 2: Create a KMS key. Step 3: Enable encryption and specify the KMS key. Step 4: Verify encryption.

Answer

Step 1: Create a KMS key. Step 2: Verify encryption. Step 3: Launch a new Amazon Redshift cluster. Step 4: Enable encryption and specify the KMS key.

Answer

Step 1: Specify the KMS key. Step 2: Create a KMS key. Step 3: Launch a new Amazon Redshift cluster. Step 4: Verify encryption.

Question 15

A company stores sensitive data in Amazon S3 and requires that all data be encrypted at rest. The data is accessed by multiple AWS services. Which solution meets the encryption requirement with the LEAST operational overhead?

Accepted Answer

Enable S3 default encryption with SSE-S3. Enabling S3 default encryption with SSE-S3 applies AES-256 encryption automatically to all objects at rest with zero key management overhead. AWS manages the keys, and no per-request KMS calls are needed, making it the lowest operational overhead solution that meets the encryption-at-rest requirement.

Answer

Use server-side encryption with AWS KMS (SSE-KMS)

Answer

Use client-side encryption with AWS KMS

Answer

Use server-side encryption with customer-provided keys (SSE-C)

Question 16

A company uses Amazon S3 to store large CSV files and runs Amazon Athena queries on them. The queries are becoming slower as data grows. A data engineer suggests converting the files to Apache Parquet format and partitioning the data. What is the primary benefit of converting to Parquet?

Accepted Answer

Parquet stores data in a columnar format, reducing the amount of data scanned per query.. Parquet is a columnar storage format that stores data by columns rather than rows. When Athena queries only a subset of columns, it can read just those columns from disk, drastically reducing the amount of data scanned per query. This directly addresses the performance slowdown because Athena charges by data scanned, and less scanning means faster queries and lower costs.

Answer

Parquet allows schema evolution without rewriting files.

Answer

Parquet supports nested data structures that CSV cannot.

Answer

Parquet is compressed by default, reducing storage costs.

Question 17

A company runs a Redshift cluster and notices that query performance has degraded over time. The data engineer suspects that table statistics are stale. What should the engineer do to improve query performance?

Accepted Answer

Run the ANALYZE command on the tables. Stale table statistics cause the Redshift query optimizer to generate suboptimal execution plans, leading to degraded query performance. Running the ANALYZE command updates these statistics, allowing the optimizer to make better decisions about join order, distribution, and data scan strategies. This directly addresses the root cause of performance degradation over time.

Answer

Rebuild the tables by using CREATE TABLE AS

Answer

Increase the number of slices in the cluster

Answer

Run the VACUUM command on the tables

Question 18

A company is designing a data lake on Amazon S3 for analytics. The data includes sensitive personally identifiable information (PII). Which TWO actions should the company take to protect the data? (Choose TWO.)

Accepted Answer

Enable S3 Block Public Access.. S3 Block Public Access (Option A) prevents any public access to S3 buckets and objects, which is critical for protecting PII from unintended exposure. Default encryption with SSE-KMS (Option E) ensures that all data written to S3 is encrypted at rest using AWS KMS-managed keys, providing both encryption and centralized key management for sensitive data.

Answer

Enable Requester Pays.

Answer

Enable S3 Transfer Acceleration.

Answer

Enable cross-region replication.

Question 19

A company uses Amazon S3 to store raw data and AWS Glue to run ETL jobs. The data is partitioned by date in the format 'year=YYYY/month=MM/day=DD'. A new data source started sending data with a different date format 'YYYY-MM-DD'. The Glue crawler is configured to create a single table for the entire bucket. The crawler runs daily, but it is not detecting the new partitions from the new data source. The existing partitions are in the format 'year=2024/month=05/day=10', while the new data is stored as '2024-05-10/' without the key-value structure. How should the engineer modify the data pipeline to include the new data?

Accepted Answer

Modify the new data source to store data in the same Hive-style partition format as the existing data.. The AWS Glue crawler relies on Hive-style partitioning (e.g., 'year=YYYY/month=MM/day=DD') to automatically detect and create partitions. The new data source uses a different format ('YYYY-MM-DD/') that does not follow the key-value structure, so the crawler cannot recognize it as partitions. The engineer must modify the new data source to store data in the same Hive-style partition format as the existing data.

Answer

Run the crawler with the 'Create partition indexes' option enabled.

Answer

Configure the crawler to add a custom classifier for date formats.

Answer

Convert the new data to Parquet format.

Question 20

Which THREE are best practices for managing data in Amazon S3 for a data lake? (Choose three.)

Accepted Answer

Enable S3 Versioning to protect against accidental deletions.. Enabling S3 Versioning is a best practice for data lakes because it protects against accidental deletions or overwrites by preserving all versions of an object, including deletions (which are recorded as delete markers). This allows you to recover previous object states and is essential for data governance and auditability in a data lake environment.

Answer

Enable S3 Snapshot for point-in-time recovery.

Answer

Disable S3 server access logging to reduce costs.