DEA-C01 Mock Exam 75 — 25 Questions

Question 1

A data engineer is analyzing a DynamoDB table for a session management application. The table currently has 10,000 items and is 1 MB in size. The application expects 1,000 writes per second during peak hours. What should the data engineer do to accommodate the write workload?

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

Accepted Answer

Increase the write capacity units (WCU) for the table.. DynamoDB write capacity is provisioned in Write Capacity Units (WCUs), and the table's current WCU setting is insufficient for 1,000 writes per second. Increasing the WCU ensures the table can handle the peak write throughput without throttling, as each WCU supports one write per second for items up to 1 KB. The table size (1 MB) and item count (10,000) are irrelevant to throughput capacity.

Answer

Reduce the item size to improve write performance.

Answer

Use global tables to distribute writes across regions.

Answer

Enable DynamoDB Accelerator (DAX) to offload writes.

Question 2

Refer to the exhibit. An IAM policy is attached to an AWS Glue ETL job. The job reads from the Kinesis stream 'input-stream' and writes to S3 bucket 'data-lake-bucket'. The job fails with an access denied error. Which missing permission is most likely the cause?

Accepted Answer

s3:ListBucket permission on the bucket. The AWS Glue ETL job fails with an access denied error because it lacks the s3:ListBucket permission on the 'data-lake-bucket'. When writing to S3, the job must first list the bucket to verify the target prefix exists and to handle multipart uploads; without this permission, the write operation fails even if s3:PutObject is granted.

Answer

kinesis:PutRecord permission on a wildcard stream ARN

Answer

kinesis:DescribeStream permission

Answer

s3:PutObject permission on a specific prefix

Question 3

A data engineer is responsible for a data pipeline that uses Amazon S3 as a data lake, AWS Glue for ETL, and Amazon Athena for ad-hoc queries. The pipeline ingests CSV files from an external partner via SFTP into an S3 bucket. The files are then processed by a Glue job that converts them to Parquet and writes to a separate S3 bucket partitioned by date. The Glue job runs daily and is triggered by a scheduled CloudWatch Events rule. Recently, the data engineer noticed that some days the Glue job fails because of memory errors, and on those days the Athena queries that rely on the data return incomplete results. The engineer needs to ensure that the pipeline is resilient and that Athena queries always see a complete view of the data, even if the Glue job fails mid-run. The engineer also needs to minimize re-processing of data. Which course of action should the engineer take?

Accepted Answer

Modify the Glue job to use job bookmarks for incremental processing and write the Parquet output to a temporary location, then use an S3 copy operation to move the data into the final partitioned location only after the job completes successfully.. Using Glue job bookmarks enables incremental processing and the ability to resume from the last successful checkpoint. Staging the data in a temporary location and moving it atomically ensures that Athena sees only complete data, even if the job fails mid-run. Option A is wrong because increasing worker capacity does not prevent partial writes during failures. Option B is wrong because using Lambda for conversion is less scalable and error-prone, and it still doesn't solve the atomicity issue. Option D is wrong because partition projection does not address the atomicity of writes after job failures.

Answer

Increase the number of workers and the worker type to G.2X to handle the memory errors, and enable job retries.

Answer

Replace the Glue job with an AWS Lambda function that processes the CSV files and writes Parquet to S3, and use S3 Event Notifications to trigger the function.

Answer

Use Athena partition projection to automatically discover partitions and set up a retry mechanism using AWS Step Functions.

Question 4

A company wants to ingest streaming data from thousands of IoT devices into AWS for real-time processing. Each device sends JSON payloads of about 2 KB at a rate of 1 message per second. The data must be processed with a durable, ordered stream per device. Which service should the company use as the ingestion layer?

Accepted Answer

Amazon Kinesis Data Streams.. Amazon Kinesis Data Streams is the correct choice because it provides durable, ordered stream processing per shard, which can be partitioned by device ID to maintain message order for each device. It supports real-time ingestion from thousands of IoT devices at 2 KB per message per second, with a retention period of up to 365 days and the ability to reprocess data via multiple consumers.

Answer

Amazon Simple Queue Service (Amazon SQS) with a FIFO queue.

Answer

Amazon Simple Notification Service (Amazon SNS) with a Lambda subscriber.

Answer

Amazon Kinesis Data Firehose with Direct Put.

Question 5

A company uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. The Flink application reads from a Kinesis Data Streams source, performs aggregations, and writes results to Amazon S3. The application is experiencing high checkpoint failures, and the processing lag is increasing. The data volume is 50 MB/s with an average record size of 1 KB. Which TWO actions would improve checkpoint reliability and reduce lag? (Choose TWO.)

Accepted Answer

Increase the checkpoint interval in the Flink configuration.. Increasing the checkpoint interval (Option D) reduces the frequency of checkpoint operations, which decreases the overhead on the Flink application and allows it to dedicate more resources to processing data, thereby reducing lag. This is especially effective when checkpoint failures are caused by the system being unable to complete checkpoints within the current interval due to high throughput (50 MB/s).

Answer

Decrease the checkpoint interval to complete checkpoints faster.

Answer

Replace the S3 sink with Kinesis Data Firehose.

Answer

Decrease the parallelism of the Flink application.

Question 6

A company uses Amazon DynamoDB to store session data. The security team requires that all data be encrypted at rest using a customer-managed KMS key. The data engineer has enabled DynamoDB encryption with a customer-managed key. However, the security team notices that the key is not being used for all tables; some tables still use the default AWS-managed key. The engineer needs to ensure that all new tables are automatically encrypted with the customer-managed key. The company has hundreds of developers who create tables using various methods (console, CLI, SDK, CloudFormation). What is the most efficient way to enforce this policy?

Accepted Answer

Use an AWS Config rule to check for tables not using the customer-managed key and trigger auto-remediation.. AWS Config rules can evaluate whether DynamoDB tables use customer-managed KMS keys and take remediation actions. Option A is wrong because it requires updating all existing code. Option B is wrong because CloudFormation templates can be bypassed. Option D is wrong because SCPs cannot enforce encryption configuration for DynamoDB.

Answer

Create a CloudFormation template that all developers must use to create tables.

Answer

Attach an SCP to deny creating DynamoDB tables without the customer-managed key.

Answer

Update the company's internal documentation and require all developers to specify the KMS key in their code.

Question 7

A data engineering team uses AWS Glue ETL jobs to process data daily. They notice that job run times are increasing as data volume grows. Which action will most effectively improve performance without changing the code?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs (Data Processing Units) allocated to the job provides more parallelism and memory, speeding up processing without code changes.

Answer

Use a smaller instance type for the Glue job.

Answer

Enable job bookmark to skip previously processed data.

Answer

Split the data into more files in S3.

Question 8

Refer to the exhibit. A data engineer is configuring an AWS Lambda function to process records from a Kinesis stream. The function is set up with an event source mapping, but no records are being processed. The Lambda function's IAM role has the policy shown. What is the most likely reason for the issue?

Accepted Answer

The IAM policy does not include all the necessary Kinesis actions for the event source mapping to work.. The IAM policy attached to the Lambda function's execution role is missing key Kinesis actions required for the event source mapping to operate. Specifically, while the policy includes kinesis:DescribeStream and kinesis:GetRecords, it lacks kinesis:GetShardIterator and kinesis:ListShards. These actions are necessary for the Lambda service to poll the stream, retrieve shard iterators, and process records. Without them, the event source mapping cannot read from the Kinesis stream, resulting in no records being processed. Therefore, option B is correct: the policy does not include all necessary Kinesis actions.

Answer

The policy does not grant permission to describe the Kinesis stream.

Answer

The policy includes too many actions, which causes a conflict.

Answer

The resource ARN for the Lambda function in the policy is incorrect.

Question 9

A company uses AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by date. The company wants to improve query performance in Amazon Athena and reduce costs. Which THREE actions should the company take? (Choose THREE.)

Accepted Answer

Partition the data by date so Athena can use partition pruning.. Partitioning the data by date allows Athena to use partition pruning, which limits the amount of data scanned by only reading the partitions that match the query's WHERE clause. This directly reduces both query cost (since Athena charges per byte scanned) and query latency, especially for date-range queries on large datasets.

Answer

Convert the data to JSON format for better schema evolution.

Answer

Use Glue DataBrew to clean the data before querying.

Question 10

A company uses Amazon S3 to store raw data and runs AWS Glue ETL jobs to transform it into Parquet. The data is then queried using Amazon Athena. Queries are slow and expensive due to high scan volumes. Which THREE design changes can improve query performance and reduce costs? (Select THREE.)

Accepted Answer

Convert the data to a columnar format like Parquet or ORC if not already. Columnar formats like Parquet or ORC store data by column rather than by row, allowing Athena to read only the columns needed for a query. This drastically reduces the amount of data scanned per query, directly lowering both latency and cost since Athena charges based on the volume of data read.

Answer

Increase the number of files by reducing file size to 1 MB

Answer

Use bucketing on high-cardinality columns

Question 11

A company is migrating a large Oracle database to Amazon Aurora PostgreSQL. The migration must have minimal downtime and preserve data consistency. Which THREE AWS services or features should be used?

Accepted Answer

AWS Schema Conversion Tool (SCT) for schema conversion. The AWS Schema Conversion Tool (SCT) is required to convert the source Oracle database schema (including stored procedures, functions, and data types) to a format compatible with Amazon Aurora PostgreSQL. Without SCT, the heterogeneous migration would fail due to incompatible SQL dialects and database objects.

Answer

Amazon RDS for Oracle as the target

Answer

AWS DataSync for initial load

Question 12

A CloudFormation template defines an AWS Glue job. The job fails during execution with the error 'Unable to locate script: s3://scripts-bucket/etl-script.py'. The S3 bucket 'scripts-bucket' exists and the script file is present. What is the most likely cause?

Accepted Answer

The IAM role for the Glue job does not have s3:GetObject permission on the scripts bucket.. The Glue job fails to locate the script because the IAM role assigned to the job lacks the s3:GetObject permission on the scripts-bucket. Even though the bucket and object exist, AWS Glue requires the execution role to have explicit read access to the S3 object to download and execute the script. Without this permission, the job cannot retrieve the file, resulting in the 'Unable to locate script' error.

Answer

The script location path is incorrect; it should include the bucket's region.

Answer

The Glue job requires Python version 2, but the script uses Python 3 syntax.

Answer

The S3 bucket is in a different AWS region than the Glue job.

Question 13

Which THREE of the following are best practices for managing data storage costs in Amazon S3? (Choose 3.)

Accepted Answer

Use S3 Lifecycle policies to transition objects to S3 Glacier Deep Archive after a specified period.. S3 Lifecycle policies allow you to automatically transition objects to lower-cost storage classes like S3 Glacier Deep Archive after a specified period, reducing storage costs for infrequently accessed data. Option C is correct because combining S3 Object Lock with a lifecycle policy to expire objects after the retention period ensures that objects are automatically deleted when they are no longer needed, preventing unnecessary storage costs. Option E is correct because S3 Intelligent-Tiering automatically moves data between access tiers based on changing patterns, optimizing costs without manual intervention. Options A and D are not best practices for cost management: storing all data in S3 Standard is costly, and creating multiple replicas increases storage costs.

Answer

Store all data in S3 Standard for maximum durability.

Answer

Create multiple bucket replicas in different regions to ensure availability.

Question 14

A company is streaming IoT sensor data to Amazon Kinesis Data Streams. The data is JSON with a schema that changes occasionally. They want to load the data into Amazon S3 in Parquet format partitioned by date and sensor_id. Which approach is MOST cost-effective and operationally efficient?

Accepted Answer

Use Amazon Kinesis Data Firehose with a schema from AWS Glue Data Catalog to convert to Parquet and enable dynamic partitioning by date and sensor_id.. Amazon Kinesis Data Firehose can directly convert incoming JSON data to Parquet using a schema from AWS Glue Data Catalog, and it supports dynamic partitioning by date and sensor_id without requiring custom code. This is the most cost-effective and operationally efficient approach as it is a fully managed service that handles buffering, conversion, and partitioning automatically. Option A (EMR) is overkill for this use case and adds operational complexity. Option B (Lambda) would require additional transformation logic and is less efficient for high-throughput streaming. Option C (custom KCL application on EC2) requires ongoing management and is not as simple as using Firehose.

Answer

Use Amazon EMR to read from Kinesis Data Streams and write to S3 in Parquet format.

Answer

Use a Lambda function to transform records to Parquet and write to S3.

Answer

Use a custom Kinesis Client Library application on EC2 to buffer and write Parquet files to S3.

Question 15

A data engineer is running an AWS Glue ETL job that converts CSV files to Parquet. The job fails with the error shown in the exhibit. The input files are about 500 MB each. The job uses 5 workers of type G.1X (16 GB memory each). What is the MOST likely cause?

Accepted Answer

The output Parquet file size is too large for the executor memory. The error indicates an out-of-memory (OOM) error during the write phase. When converting large CSV files to Parquet, Spark must buffer the output data in memory before writing. With 500 MB input files and 5 workers of G.1X (16 GB each), the executor memory is limited. If the output Parquet file or partition is too large, it may exceed the executor's memory, causing OOM. Option B (data skew) could also cause OOM, but typically during shuffle, not write. Options C and D are unrelated to write OOM issues.

Answer

The data is highly skewed causing a single partition to receive too much data

Answer

The Spark driver does not have enough memory to handle the schema inference

Answer

The input CSV files are corrupt or have inconsistent schema

Question 16

A company uses Amazon S3 to store customer documents. The data engineer needs to ensure that all objects uploaded to a specific S3 bucket are automatically encrypted with a customer-managed AWS KMS key. What should the data engineer do?

Accepted Answer

Set default encryption on the bucket to use SSE-KMS with the customer-managed key.. Setting default encryption on the S3 bucket to SSE-KMS with the customer-managed key ensures that all objects uploaded without explicit encryption headers are automatically encrypted using that KMS key. This satisfies the requirement without relying on client-side behavior, as S3 applies the encryption server-side at the time of write.

Answer

Use pre-signed URLs for all uploads that include encryption parameters.

Answer

Create a bucket policy that denies uploads without encryption.

Answer

Enable S3 Versioning on the bucket.

Question 17

Refer to the exhibit. An IAM policy includes the above statement to allow decryption of a KMS key under specific conditions. What does this policy allow?

Accepted Answer

Decrypt data that was encrypted with the encryption context {"aws:pi":"db-123"}. The policy allows the Decrypt action on the KMS key only when the encryption context condition is met, specifically the key 'aws:pi' with value 'db-123'. This means only data encrypted with that encryption context can be decrypted. Option A is incorrect because the condition restricts to a specific encryption context, not any KMS key. Option C is incorrect because the action is Decrypt, not Encrypt. Option D is incorrect because decryption without the required encryption context would not satisfy the condition.

Answer

Decrypt any data encrypted with any KMS key

Answer

Encrypt data with the KMS key using the specified encryption context

Answer

Decrypt data encrypted with the KMS key without any encryption context

Question 18

A data engineering team is managing an Amazon DynamoDB table that stores user session data. The table has a primary key of user_id (partition key) and session_id (sort key). The application performs strongly consistent reads on individual items. The team notices that read latency increases during peak hours. They suspect that the table is experiencing hot partitions. The team needs to improve read performance without changing the application code. Which solution should they implement?

Accepted Answer

Enable DynamoDB Accelerator (DAX) for the table.. DynamoDB Accelerator (DAX) is a fully managed, in-memory cache that delivers up to 10x read performance improvement by caching hot partition data. Since the application uses strongly consistent reads and cannot be changed, DAX provides a drop-in caching layer that reduces read latency on hot partitions without requiring any code modifications.

Answer

Enable DynamoDB global tables to distribute reads across regions.

Answer

Increase the read capacity units for the table.

Answer

Change the application to use eventually consistent reads.

Question 19

A data engineer is migrating a large Oracle data warehouse to Amazon Redshift. The engineer needs to ensure optimal performance. Which TWO practices should the engineer follow?

Accepted Answer

Choose appropriate sort keys based on common query patterns.. Amazon Redshift uses sort keys to physically order data on disk, which allows the query optimizer to skip large blocks of data during scans via zone maps. Choosing sort keys based on common query patterns (e.g., range filters or frequent GROUP BY columns) dramatically reduces I/O and improves query performance, especially for large tables.

Answer

Design the schema as a normalized star schema with row-based storage.

Answer

Manually define compression encodings for each column.

Answer

Stage data in Amazon S3 before loading into Redshift.

Question 20

A data engineer runs the above command and gets the output. What does the 'MFADelete' setting imply?

Accepted Answer

To permanently delete a version of an object, the user must provide MFA.. The 'MFADelete' setting on an S3 bucket versioning configuration requires multi-factor authentication to permanently delete an object version. This means that when a user issues a DELETE request with a version ID (a permanent delete), they must include a valid MFA token in the request headers. It does not apply to creating new versions, reading objects, or other operations.

Answer

Any modification to an object requires MFA.

Answer

MFA is required for all read operations as well.

Answer

All operations on the bucket require MFA authentication.