DEA-C01 Practice Test 27 — 15 Questions

Question 1

The Glue job attempts to read data from 's3://my-data-bucket/input/' and write to 's3://my-data-bucket/output/'. It also tries to update a table in the Glue Data Catalog. The job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The IAM role is missing the 's3:ListBucket' permission on the bucket.. The policy allows GetObject and PutObject on the bucket, but the Glue job also needs 's3:ListBucket' permission to list objects in the bucket. Without ListBucket, the job cannot enumerate files. Option A is wrong because the resource is correct ('/*' includes all objects). Option C is wrong because the action 'glue:UpdateTable' is allowed. Option D is wrong because there is no condition key issue.

Answer

The 'glue:UpdateTable' action is not allowed on the specific table.

Answer

The policy is missing a condition key for the S3 bucket.

Answer

The resource ARN does not include the bucket itself; it only covers objects.

Question 2

A company uses AWS Glue to process data from multiple S3 buckets. The Glue job runs daily and reads data from a bucket that contains millions of small files (each < 1 MB). The job has been running for hours and is often close to the 8-hour timeout limit. Which optimization would MOST reduce the job's runtime?

Accepted Answer

Pre-process the data to consolidate small files into larger files before the Glue job.. Small files cause overhead in reading and processing. Grouping them into larger files (e.g., by using S3 batch operations or a compaction step) reduces the number of files and improves performance. Option A is wrong because increasing DPUs may help but not as much as file consolidation. Option B is wrong because Spark is already used by Glue. Option D is wrong because Parquet helps but if files are small, the benefit is limited.

Answer

Convert the source data from CSV to Parquet format.

Answer

Increase the number of DPUs allocated to the Glue job.

Answer

Use a larger Spark shuffle partition size.

Question 3

The command returns an empty result, but you know there are objects in the 'logs/' prefix larger than 1000 bytes. What is the MOST likely reason?

Accepted Answer

The comparison 'Size > '1000'' uses a string instead of a number, so it never matches.. The 'Size' field in S3 is an integer, but the query compares it to a string '1000'. This causes a type mismatch, and the filter evaluates to false. Option A is wrong because the prefix is correct. Option B is wrong because the output format does not affect the query. Option D is wrong because there is no need for pagination (the command returns up to 1000 objects).

Answer

The prefix 'logs/' is incorrect; the objects are in a different prefix.

Answer

The command does not paginate, so it only checks the first 1000 objects.

Answer

The output format is set to text, but the query requires JSON.

Question 4

A company is using Amazon Kinesis Data Streams to ingest clickstream data from a website. The data is consumed by an AWS Lambda function that enriches records and writes to Amazon S3. The Lambda function is experiencing high error rates due to records exceeding the 256 KB payload limit. Which TWO actions should the team take to resolve this issue?

Accepted Answer

Enable compression on the producer side before sending records to Kinesis.. The correct answers are A and C. Enabling compression reduces record size. Using Kinesis Aggregation Library (KPL) aggregates multiple records into a single larger record while staying within limits. Option B (increasing shards) does not reduce record size. Option D (using Kinesis Firehose) changes architecture but does not solve the payload limit. Option E (increasing Lambda timeout) does not address size.

Answer

Increase the Lambda function timeout.

Answer

Switch from Kinesis Data Streams to Kinesis Data Firehose.

Answer

Increase the number of shards in the Kinesis stream.

Question 5

A company is ingesting streaming data from a fleet of weather sensors. Each sensor sends a JSON payload every second. The data is used for real-time dashboarding and also archived to S3. The pipeline should handle sudden bursts of data without data loss. Which architecture meets these requirements?

Accepted Answer

Amazon Kinesis Data Streams -> AWS Lambda for dashboard -> Amazon Kinesis Data Firehose -> S3. Kinesis Data Streams provides durable storage and can handle bursts; Firehose reads from the stream and writes to S3; Lambda can update dashboards. Direct Firehose ingestion may throttle; SQS is not ideal for streaming; EC2 is not serverless.

Answer

Amazon EC2 with Apache Kafka -> S3

Answer

Amazon Kinesis Data Firehose directly with no buffer

Answer

Amazon SQS -> AWS Lambda -> S3

Question 6

A company is streaming clickstream data from a website into Amazon Kinesis Data Streams. The data is then consumed by a Lambda function that transforms the records and writes them to an S3 bucket in Parquet format. Recently, the Lambda function has been timing out and the S3 bucket is not receiving all expected records. The Kinesis stream has a shard count of 10 and the Lambda function's reserved concurrency is set to the default. Which change would MOST likely resolve the issue?

Accepted Answer

Increase the batch size from the default 100 to 500 records per invocation.. The Lambda function is timing out because it cannot keep up with the incoming data rate. Increasing the batch size allows each invocation to process more records, reducing the number of invocations and the overhead. Option A is wrong because increasing concurrency would help but may exceed account limits. Option B is wrong because decreasing batch window would increase invocation frequency. Option D is wrong because S3 destination is not an output target for Kinesis streams directly.

Answer

Decrease the batch window from the default 300 seconds to 60 seconds.

Answer

Configure the Kinesis stream to directly write to S3 using a delivery stream.

Answer

Increase the Lambda function's reserved concurrency.

Question 7

A data engineer needs to ingest data from an Amazon RDS MySQL database into a data lake on Amazon S3. The engineer wants to perform an initial full load and then capture incremental changes. Which TWO AWS services can be combined to achieve this?

Accepted Answer

Amazon S3. AWS DMS can do full load and CDC. S3 can be the target. Option B is wrong because Glue does not support CDC from MySQL directly. Option C is wrong because Firehose doesn't connect to MySQL. Option D is wrong because Data Pipeline does not support CDC.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue

Answer

AWS Data Pipeline

Question 8

A company uses AWS Glue ETL jobs to transform data stored in Amazon S3. The job reads data in Parquet format, applies transformations, and writes the output back to S3 in Parquet format. The team wants to improve the job's performance and reduce costs. Which action is MOST effective?

Accepted Answer

Use column pruning and predicate pushdown to read only necessary columns and filter data early.. The correct answer is to use column pruning and predicate pushdown. Reading only necessary columns and filtering early reduces data scanned and processing time. Option B (increasing worker count) increases cost and may not be needed. Option C (switching to CSV) would increase data size and slow performance. Option D (using a single large file) reduces parallelism and may harm performance.

Answer

Change the input format from Parquet to CSV to simplify parsing.

Answer

Coalesce the input data into a single large file before processing.

Answer

Increase the number of workers to maximum allowed.

Question 9

A company uses AWS Database Migration Service (DMS) to continuously replicate data from an on-premises Oracle database to Amazon S3. The data is stored as CSV files. The downstream team requires the data to be in Apache Parquet format. Which change should the data engineer make to the DMS task?

Accepted Answer

Modify the DMS task to use Apache Parquet as the target table preparation mode.. Option C is correct because DMS supports target table preparation mode to convert data to Parquet format. Option A is wrong because S3 lifecycle policies do not convert formats. Option B is wrong because DMS does not support Lambda transformations natively. Option D is wrong because switching to full load only would stop continuous replication.

Answer

Add an S3 lifecycle rule to convert CSV to Parquet.

Answer

Change the DMS task to use full load instead of continuous replication.

Answer

Configure a Lambda function to transform data after DMS writes to S3.

Question 10

Refer to the exhibit. An AWS Glue ETL job is failing with an OutOfMemoryError. The job reads from Amazon S3 and performs a GROUP BY on a large dataset. Which change should the data engineer make to resolve this error?

Accepted Answer

Increase the number of DPUs allocated to the Glue job.. Option C is correct. Increasing the number of DPUs provides more memory per executor. Option A is wrong because increasing parallelism may help but requires more cores, not just partitions. Option B is wrong because using coalesce reduces partitions but may cause data skew. Option D is wrong because increasing partition count may worsen memory issues.

Answer

Use coalesce to reduce the number of partitions.

Answer

Increase the number of partitions in the DataFrame.

Answer

Use repartition to increase the number of partitions.

Question 11

A data engineer is using AWS Glue to perform ETL on data stored in an S3 bucket. The source data is in CSV format with a header row, and the target is a set of Parquet files partitioned by date. The engineer notices that the Glue job is reading all files in the source prefix, including temporary files that should be ignored. What is the MOST efficient way to exclude these temporary files?

Accepted Answer

Use an S3 prefix exclusion pattern in the Glue job's source path.. Using a S3 prefix exclusion pattern in the Glue job's S3 path is the most efficient way to exclude files. Option A is wrong because changing to Parquet does not exclude files. Option B is wrong because a custom classifier is for schema inference. Option D is wrong because Lambda would add complexity.

Answer

Change the source format from CSV to Parquet.

Answer

Set up an S3 event notification to trigger a Lambda function that moves temporary files.

Answer

Create a custom classifier in the Glue Data Catalog.

Question 12

A company is using Amazon Kinesis Data Streams to process real-time stock trade data. The data is consumed by a Lambda function that calculates moving averages and stores results in Amazon DynamoDB. The Lambda function is failing with 'ProvisionedThroughputExceededException' on the DynamoDB table. The table has on-demand capacity. Which TWO actions should the engineer take to resolve this issue?

Accepted Answer

Add a dead-letter queue and configure the Lambda function to retry on failure with exponential backoff.. On-demand DynamoDB can handle bursts but may throttle if the traffic is too high. Increasing batch size reduces write frequency. Adding a retry mechanism with exponential backoff handles throttling gracefully. Option B is wrong because increasing Lambda concurrency increases write pressure. Option C is wrong because reducing batch size increases frequency. Option D is wrong because Firehose is not needed.

Answer

Decrease the batch window to 0 seconds to process records immediately.

Answer

Increase the Lambda function's reserved concurrency to process more shards.

Question 13

A data engineer is ingesting data from an Amazon RDS for PostgreSQL database into Amazon S3 using AWS Glue. The Glue job reads the entire table each time it runs, which takes several hours. The team wants to reduce the job duration by reading only new or updated records. Which approach should the engineer adopt?

Accepted Answer

Enable job bookmarks in AWS Glue and use a column with timestamps as the bookmark key to read only incremental data.. The correct answer is to enable Change Data Capture (CDC) using AWS DMS or enable PostgreSQL logical replication and use Glue with bookmark keys. Glue job bookmarks can track processed data if a column (like last_updated) is used. Option B (increasing workers) does not solve reading all data. Option C (partitioning) reduces data per run but still reads entire table. Option D (using Kinesis) is real-time but overkill for periodic ingestion.

Answer

Partition the table in the source database by date and read only the latest partition.

Answer

Increase the number of Glue workers to improve parallel reads.

Answer

Use Amazon Kinesis Data Streams to capture changes from PostgreSQL.

Question 14

A data engineer is building a data ingestion pipeline that reads JSON files from Amazon S3 and loads them into an Amazon Redshift table using COPY commands. The files are gzip compressed and contain nested JSON. The engineer wants to minimize transformation steps. Which approach should the engineer use?

Accepted Answer

Use the COPY command with the 'auto' option to ingest JSON directly.. Option B is correct because Redshift COPY with the 'auto' option can automatically flatten nested JSON. Option A is wrong because Glue ETL is an unnecessary intermediate step. Option C is wrong because Kinesis Data Firehose is for streaming, not batch. Option D is wrong because Athena requires schema definition and is not a direct load to Redshift.

Answer

Use Amazon Athena to query the JSON and INSERT INTO Redshift.

Answer

Use Kinesis Data Firehose to transform and load into Redshift.

Answer

Use AWS Glue ETL to flatten the JSON and write to S3 as CSV, then COPY from CSV.

Question 15

A data engineer needs to ingest data from a SaaS application (Salesforce) into Amazon S3 on a daily basis. Which TWO AWS services can be used for this purpose? (Choose TWO.)

Accepted Answer

AWS Glue. Amazon AppFlow natively integrates with Salesforce and can write to S3. AWS Glue can also connect via JDBC. Option A is correct. Option B is wrong because Kinesis Data Streams is for real-time streaming, not batch from SaaS. Option C is correct. Option D is wrong because Transfer Family is for FTP. Option E is wrong because DataSync is for file/object storage, not SaaS.

Answer

AWS DataSync

Answer

Amazon Kinesis Data Streams

Answer

AWS Transfer Family

DEA-C01 Practice Test 27 — 15 Questions

Exhibit

DEA-C01 Practice Test 27 — 15 Questions

Exhibit