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CCNA Data Ingestion and Transformation Questions

75 of 610 questions · Page 8/9 · Data Ingestion and Transformation · Answers revealed

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526

Multi-Selectmedium

A data engineer is designing a data ingestion pipeline for real-time clickstream data. The data must be available for both real-time analytics and batch processing. The engineer wants to use Amazon Kinesis Data Streams. Which THREE components should be included in the architecture?

Select 3 answers

A.Amazon Kinesis Data Analytics

B.Amazon Kinesis Data Streams

C.Amazon Kinesis Client Library (KCL) application

D.Amazon Kinesis Data Firehose to deliver data to Amazon S3

E.Amazon SQS as a buffer

AnswersB, C, D

Primary ingestion service.

Why this answer

Kinesis Data Streams ingests data, Kinesis Client Library (KCL) consumes for real-time analytics, and Kinesis Data Firehose delivers to S3 for batch. Kinesis Data Analytics is for real-time SQL, not batch; SQS is not used; Glue is batch but not directly from stream.

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527

Multi-Selecteasy

A data engineer needs to transform CSV files in S3 to Parquet format using a serverless solution. The files are large (up to 5 GB each) and arrive irregularly. Which TWO services can accomplish this with minimal operational overhead? (Choose TWO.)

Select 2 answers

A.AWS Glue ETL job

B.AWS Step Functions with Athena CTAS queries

C.Amazon EC2 with a script

D.Amazon EMR cluster

E.Amazon Redshift Spectrum

AnswersA, B

Glue is serverless and can convert large CSV to Parquet efficiently.

Why this answer

Option A (Glue ETL) is serverless and can handle large files. Option C (Step Functions with Athena) can orchestrate Parquet conversion via CTAS. Option B (EMR) is not serverless.

Option D (Redshift Spectrum) is for querying. Option E (EC2) requires management.

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528

MCQmedium

A media company ingests large video files from partners via AWS Transfer Family (SFTP) into an S3 bucket. Each file is typically 2-5 GB. Once uploaded, an AWS Lambda function is triggered to transcode the video using Amazon Elastic Transcoder. The Lambda function reads the file from S3, submits a transcoding job to Elastic Transcoder, and writes the output back to a different S3 bucket. Recently, the Lambda function has been failing intermittently with timeouts, and the company reports that some files are not being transcoded. The CloudWatch logs show that the Lambda function is timing out after 15 minutes. The average transcoding job takes about 10 minutes to complete. The data engineer needs to fix the issue without changing the architecture drastically. What should the data engineer do?

A.Increase the Lambda function's reserved concurrency to allow multiple invocations in parallel.

B.Increase the Lambda function timeout to 20 minutes to accommodate longer transcoding jobs.

C.Modify the Lambda function to submit the transcoding job asynchronously and exit, using an SNS topic to trigger a second Lambda function when the job completes.

D.Replace AWS Transfer Family with AWS Database Migration Service to handle file transfers more efficiently.

AnswerC

Decoupling submission from completion avoids timeout.

Why this answer

Option B is correct because the Lambda function should not wait for the transcoding job to complete; it should submit the job and exit. The current design synchronously waits, causing timeouts. Option A is wrong because increasing the timeout to 20 minutes may still fail if jobs take longer.

Option C is wrong because the issue is not about concurrency. Option D is wrong because DMS is for database migration, not file processing.

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529

MCQmedium

A company is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data must be transformed in real-time using custom Python code before being stored in Amazon S3. Which AWS service should be used to perform this transformation?

A.Amazon EMR with Spark Streaming

B.AWS Lambda function triggered by Kinesis Data Streams

C.Kinesis Data Analytics for Apache Flink

D.Kinesis Data Firehose with custom data transformation

AnswerC

Supports custom Flink applications for complex real-time transformations.

Why this answer

Option C is correct because Kinesis Data Analytics for Apache Flink allows running custom Apache Flink applications for real-time data processing. Option A (Kinesis Data Firehose) does not support custom Python code; it uses built-in transformations. Option B (AWS Lambda) can be used but requires custom code and is typically used for simple transformations.

Option D (Amazon EMR) is for large-scale batch processing, not real-time.

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530

Multi-Selecthard

A company needs to ingest data from multiple SaaS applications (Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate (~100 GB per day). The pipeline must handle schema changes, deduplicate records, and provide low latency (under 1 hour). Which THREE services should be used? (Choose THREE.)

Select 3 answers

A.Amazon AppFlow

B.Amazon EventBridge

C.Amazon Kinesis Data Streams

D.AWS Glue DataBrew

E.AWS Database Migration Service (DMS)

AnswersA, B, D

AppFlow can ingest data from SaaS applications like Salesforce and Marketo.

Why this answer

Option A (AppFlow) connects to SaaS sources. Option C (Glue DataBrew) can clean and deduplicate data. Option E (EventBridge) can trigger Glue jobs on schedule or events.

Option B (Kinesis) is for streaming, not SaaS. Option D (DMS) is for databases.

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531

Multi-Selecthard

A company is migrating a legacy on-premises ETL pipeline to AWS. The pipeline processes daily batch files from an FTP server. The data must be transformed using complex business logic before being loaded into Amazon Redshift. Which THREE AWS services should be used for this migration?

Select 3 answers

A.Amazon Athena

B.Amazon Redshift

C.AWS Glue

D.Amazon Kinesis Data Streams

E.AWS Transfer Family

AnswersB, C, E

Redshift is the target data warehouse.

Why this answer

Option A, Option C, and Option D are correct because AWS Transfer Family can ingest files from FTP, AWS Glue can perform complex transformations, and Amazon Redshift is the target. Option B is wrong because Kinesis Data Streams is for streaming data. Option E is wrong because Athena is for querying, not ETL.

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532

MCQeasy

A data engineer needs to ingest data from multiple SaaS applications (Salesforce, Marketo) into Amazon S3 for a data lake. The data volumes are moderate and the sync needs to be scheduled daily. Which AWS service is most appropriate for this task?

A.AWS Glue

B.Amazon AppFlow

C.AWS Database Migration Service (DMS)

D.Amazon Kinesis Data Firehose

AnswerB

Designed for SaaS data ingestion.

Why this answer

Amazon AppFlow is purpose-built for securely transferring data between SaaS applications (like Salesforce and Marketo) and AWS services (like S3). It supports scheduled, incremental data syncs with built-in connectors, making it the most appropriate choice for moderate-volume daily ingestion into a data lake.

Exam trap

The trap here is that candidates often confuse AWS Glue's ETL capabilities with direct SaaS ingestion, overlooking that Glue requires a custom connector or script to pull from APIs, whereas AppFlow provides native, managed connectors.

How to eliminate wrong answers

Option A is wrong because AWS Glue is an ETL service designed for batch data transformation and cataloging, not for direct ingestion from SaaS applications; it lacks native connectors for Salesforce or Marketo. Option C is wrong because AWS DMS is intended for migrating databases (e.g., Oracle, MySQL) to AWS, not for pulling data from SaaS APIs. Option D is wrong because Amazon Kinesis Data Firehose is optimized for streaming data ingestion (e.g., from IoT or logs) and does not provide native SaaS connectors or scheduled sync capabilities.

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533

Matchingmedium

Match each AWS data migration tool to its primary function.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Migrate databases with minimal downtime

Physical device for large data transfer

Online data transfer between on-prem and AWS

Fast uploads over long distances

Combine data across sources into views

Why these pairings

Different tools suit different migration scenarios.

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534

MCQmedium

A company uses Amazon Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data must be transformed from JSON to Parquet format before delivery. Which approach should be used?

A.Configure Kinesis Data Firehose to invoke a Lambda function for data transformation.

B.Use an AWS Glue ETL job to read from S3 and write Parquet back to S3.

C.Use Amazon EMR to process the data and output Parquet.

D.Use Kinesis Data Analytics to convert the data to Parquet.

AnswerA

Firehose can call a Lambda function to transform records, including converting JSON to Parquet.

Why this answer

Kinesis Data Firehose can invoke a Lambda function as a transformation step before data is delivered to S3. This allows you to convert JSON records to Parquet format inline, without needing an intermediate storage or separate processing pipeline. The Lambda function receives batches of records, transforms them (e.g., using PyArrow or similar libraries), and returns them to Firehose for delivery.

Exam trap

The trap here is that candidates may think they need a separate ETL service like Glue or EMR for format conversion, but Firehose's built-in Lambda integration is the simplest and most cost-effective way to transform data in-flight before delivery.

How to eliminate wrong answers

Option B is wrong because using an AWS Glue ETL job to read from S3 and write Parquet back to S3 introduces unnecessary latency and cost, and does not meet the requirement for transformation before delivery — it processes data after it is already stored. Option C is wrong because Amazon EMR is a heavy, cluster-based solution designed for large-scale batch or stream processing, not for lightweight, real-time transformation within a Firehose delivery stream. Option D is wrong because Kinesis Data Analytics is used for real-time analytics and SQL-based processing, not for converting data formats like JSON to Parquet; it cannot output Parquet directly to S3.

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535

MCQmedium

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?

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

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

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

D.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.

AnswerD

Firehose natively supports Parquet conversion and dynamic partitioning, minimizing custom code and cost.

Why this answer

Option B is correct because Kinesis Data Firehose can directly convert JSON to Parquet using a Glue table schema and also supports custom partitioning (e.g., by date and sensor_id) without needing a separate transformation step. Option A (Lambda) adds cost and complexity. Option C (EMR) is overkill for this streaming case.

Option D (Kinesis Client Library + EC2) is custom and not managed. Option B is the most cost-effective and operationally efficient.

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536

MCQhard

A data engineer is designing a data ingestion pipeline for JSON files landing in an Amazon S3 bucket. The pipeline must transform the data (e.g., flatten nested structures) and load it into Amazon Redshift. The transformation logic is complex and may evolve frequently. Which approach provides the MOST flexibility and ease of maintenance?

A.Use AWS Lambda functions to transform each file and load into Redshift.

B.Use the Amazon Redshift COPY command to load raw JSON directly.

C.Use AWS Glue ETL jobs to transform the data and load into Redshift.

D.Use Amazon Athena to query the raw data and insert into Redshift.

AnswerC

Glue ETL supports complex transformations and is easy to maintain.

Why this answer

Option A is correct because Glue ETL (Apache Spark) provides a flexible, code-based environment for complex transformations. Option B is incorrect because Athena is for querying, not ETL. Option C is incorrect because Lambda is limited by execution time and memory for large files.

Option D is incorrect because Redshift COPY does not transform.

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537

MCQmedium

Refer to the exhibit. A data engineer is troubleshooting an AWS Lambda function that reads from an S3 bucket and writes to a Kinesis Data Stream. The Lambda function fails with an AccessDeniedException when calling the kinesis:PutRecords API. Which change is needed to the IAM policy?

A.Add s3:PutObject permission to the policy

B.Change the resource ARN for Kinesis to a wildcard

C.Change the resource ARN for Kinesis to include the correct stream name

D.Add kinesis:PutRecords permission to the policy

AnswerB

The error may occur if the Lambda is writing to a different stream; a wildcard would allow all streams, but the better fix is to ensure the resource matches the stream. However, given the options, D is the most plausible fix for a permission mismatch.

Why this answer

Option B is correct because the policy allows kinesis:PutRecord and kinesis:PutRecords, but the resource ARN is missing the stream name in the correct format. The ARN should be arn:aws:kinesis:us-east-1:123456789012:stream/input-stream, which is correct. However, the error might be due to missing permissions for the stream.

Actually, the policy looks correct. The most likely issue is that the Lambda execution role does not have the kinesis:PutRecords permission. The exhibit allows it, but maybe the role is different.

The correct answer is C: Add the kinesis:PutRecords permission, but it's already there. Wait, the error is AccessDeniedException, so the IAM policy is missing the action. The policy includes kinesis:PutRecords, so the error might be due to resource constraints.

The correct answer might be D: Change the resource to a wildcard. But the resource is specific. I think the issue is that the stream name is wrong.

But the question says "Refer to the exhibit." and the policy seems to allow PutRecords. Perhaps the Lambda function is trying to write to a different stream. The most straightforward answer is that the Lambda execution role does not have the kinesis:PutRecords permission.

But the exhibit shows it does. Hmm. Let me re-read.

The policy allows kinesis:PutRecord and kinesis:PutRecords. The resource is a specific stream ARN. If the Lambda is using a different stream, it would fail.

So the fix is to update the resource ARN to match the stream. Option B: Add the kinesis:PutRecords permission to the policy (but it's already there). Option D: Change the resource to a wildcard.

That would be a common fix. I'll go with D.

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538

MCQhard

A company ingests clickstream data into Amazon S3 via Kinesis Data Firehose. The data arrives in 20 MB files every 2 minutes. The data engineering team needs to transform nested JSON into a flat structure before loading into Amazon Redshift. Which approach is most cost-effective and scalable?

A.Create an AWS Glue ETL job that runs on a schedule, using dynamic frames to flatten the data and write to S3 in Parquet

B.Run an Amazon EMR cluster with Spark to flatten the data and write back to S3

C.Use AWS Lambda to transform each file as it arrives in S3

D.Use Amazon Redshift Spectrum to query the nested JSON directly and create a view

AnswerA

Glue's dynamic frames natively handle nested JSON and can run cost-effectively on a schedule.

Why this answer

Option D is correct because the dynamic frame transform can flatten nested JSON in Glue ETL jobs, and incremental processing based on time partitions is efficient. Option A is wrong because Redshift Spectrum queries raw data but does not transform it. Option B is wrong because EMR is overkill for simple flattening.

Option C is wrong because Lambda has a 15-minute timeout and may not handle large files.

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539

Multi-Selectmedium

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.)

Select 3 answers

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

B.Use partition pruning by filtering on the date column in the ETL script.

C.Use job bookmarks to process only new data.

D.Increase the number of DPUs to the maximum allowed.

E.Convert all files to Parquet format before processing.

AnswersB, C, E

Reduces data scanned.

Why this answer

Options A, C, and D are correct. Partition pruning reduces the amount of data scanned. Using Parquet improves performance and compression.

Using a job bookmark prevents reprocessing of old data. Option B is wrong because increasing the number of DPUs may increase cost without optimization. Option E is wrong because a crawler is not needed if the schema is known.

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540

MCQmedium

Refer to the exhibit. A data engineer creates an AWS Glue job using this CloudFormation template. The job processes new data files in S3 and uses job bookmarks to track processed files. After initial success, the job runs again but processes all files again instead of only new ones. What is the most likely cause?

A.The job bookmark option is set to 'job-bookmark-disable'

B.The enable-metrics parameter is set to true

C.The MaxRetries parameter is set to 0

D.The S3 input path does not have a partitioning scheme or timestamp to identify new files

AnswerD

Job bookmarks rely on partition structure or file timestamps to track progress.

Why this answer

Option A is correct because job bookmarks require that the S3 path structure includes partitioning or a timestamp; if not, Glue cannot identify new files. Option B is wrong because the bookmark option is enabled. Option C is wrong because max retries do not affect bookmark behavior.

Option D is wrong because metrics are unrelated to bookmarks.

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541

MCQeasy

A media company is building a data pipeline to ingest user activity logs from multiple sources into Amazon S3. The logs are JSON files generated every minute. The company wants to use Amazon Athena to query the logs with minimal latency and cost. The current approach is to use AWS Kinesis Data Firehose to deliver the logs to S3 with a prefix like 'logs/2024/01/01/00/file.json'. However, when running Athena queries, the team notices high query costs because Athena scans all files in the 'logs/' prefix even when querying for a specific date. What should the team do to reduce the amount of data scanned by Athena?

A.Create an Athena view that filters by date.

B.Increase the number of partitions by using a more granular prefix like 'logs/2024/01/01/00/00/'.

C.Convert the JSON files to Apache Parquet format using AWS Glue ETL jobs.

D.Create a Hive-style partition structure in S3 with keys like 'year=2024/month=01/day=01/hour=00/' and update the Glue Data Catalog accordingly.

AnswerD

Partition pruning allows Athena to scan only relevant directories, reducing costs.

Why this answer

Option C is correct because partitioning the data in S3 by date (e.g., year=2024/month=01/day=01/hour=00/) allows Athena to use partition pruning and scan only relevant partitions. Option A is wrong because converting to Parquet helps but alone does not solve the full scan issue. Option B is wrong because increasing number of partitions without proper structure still causes full scan.

Option D is wrong because creating views does not change underlying storage or scan behavior.

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542

MCQmedium

A company runs a nightly ETL job using AWS Glue. The job reads data from a JDBC connection to an on-premises MySQL database. The job fails with an error indicating that the connection pool is exhausted. What is the most likely cause and solution?

A.The database is not reachable due to network issues. Check VPC and security groups.

B.The Glue job is hitting the AWS Glue connection pool limit. Increase the Glue connection pool size.

C.The database credentials are expired. Rotate the password in AWS Secrets Manager.

D.The Glue job is using too many executors, exhausting the database connections. Reduce the number of DPUs or increase the database max connections.

AnswerD

Glue can open multiple connections; reducing parallelism or scaling database helps.

Why this answer

Option A is correct because high parallelism in Glue creates many connections, overwhelming the database. Option B is wrong because network issues would cause timeouts, not pool exhaustion. Option C is wrong because Glue does not have connection pool limits.

Option D is wrong because password rotation would cause authentication errors.

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543

MCQmedium

A company uses AWS Glue ETL jobs to transform data in S3. The job runs successfully but takes longer than expected. The data is in Parquet format and partitioned by date. Which change would most improve performance without increasing cost?

A.Repartition the data by a different column.

B.Convert Parquet to CSV for faster serialization.

C.Increase the number of DPUs for the job.

D.Enable pushdown predicates to filter partitions early.

AnswerD

Reduces data scanned, improving performance.

Why this answer

Option B is correct because enabling pushdown predicates filters partitions at the source, reducing data scanned. Option A is wrong because increasing DPUs increases cost. Option C is wrong because converting to CSV increases data size and scan time.

Option D is wrong because repartitioning may cause shuffling overhead.

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544

Multi-Selecteasy

A data engineer is designing a data ingestion pipeline that uses AWS Lambda to process records from a Kinesis Data Stream and write to DynamoDB. Which TWO strategies can help handle increased throughput and prevent data loss? (Choose TWO.)

Select 2 answers

A.Configure the Lambda event source mapping with a batch window and set the number of concurrent batches per shard

B.Use synchronous invocation of Lambda from the producer

C.Increase the number of shards in the Kinesis data stream

D.Configure a dead-letter queue (DLQ) for the Lambda function

E.Increase the Lambda function timeout

AnswersA, C

This improves throughput and handles spikes.

Why this answer

Options B and D are correct. B: Increasing the number of shards allows higher throughput and Lambda concurrency. D: Configuring the Lambda event source mapping with a batch window and concurrent batches helps manage load.

A: Increasing Lambda timeout does not increase throughput. C: Using a DLQ captures failures but does not prevent loss; retries should be configured. E: Using synchronous invocation would block and cause throttling.

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545

MCQmedium

A company uses AWS Glue for ETL jobs. The data engineer needs to ensure that the Glue job can access an S3 bucket in another account. What is the recommended approach?

A.Create an IAM role in the target account and have the Glue job assume that role

B.Assign an IAM role to the Glue job with permissions to access the bucket, and configure the bucket policy to allow the role

C.Configure the S3 bucket policy to allow the Glue job's IAM role and also set the Glue job's resource-based policy

D.Store AWS access keys for the target account in AWS Secrets Manager and have the Glue job retrieve them

AnswerB

This is the standard cross-account access method using IAM roles.

Why this answer

Option A is correct because the Glue job's IAM role must have permissions to access the S3 bucket, and the bucket policy must allow the role. Option B is wrong because S3 bucket policies can allow cross-account access without requiring an IAM role in the other account. Option C is wrong because resource-based policies alone are not sufficient; the Glue job needs an IAM role with permissions.

Option D is wrong because Glue jobs cannot use access keys; they use IAM roles.

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546

MCQmedium

A company is using Amazon Kinesis Data Firehose to deliver streaming data to an Amazon S3 bucket. The data is delivered in JSON format. The company wants to convert the data to Apache Parquet format before delivery to reduce storage costs and improve query performance. How can this be achieved?

A.Deliver data to S3 as JSON, then use Amazon Athena to convert to Parquet.

B.Use the AWS Glue Data Catalog to define a schema and configure Firehose to use it for Parquet conversion.

C.Write an AWS Lambda function to transform the data to Parquet and deliver it to S3.

D.Configure the Firehose stream to convert data to Parquet automatically without any additional setup.

AnswerB

This is the recommended approach for converting streaming data to Parquet in Firehose.

Why this answer

Option B is correct because Kinesis Data Firehose can convert the input data to Parquet or ORC format using a schema from the AWS Glue Data Catalog. Option A is incorrect because Firehose does not support a built-in transformation to Parquet without a schema. Option C is incorrect because Lambda can be used for custom transformations, but Firehose natively supports Parquet conversion using Glue.

Option D is incorrect because Athena is a query service, not a transformation service.

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547

MCQmedium

A data engineer is troubleshooting a Lambda function that reads from the Kinesis stream 'my-data-stream'. The Lambda function is able to read data but occasionally fails with 'KMS.AccessDeniedException'. What is the most likely cause?

A.The Lambda function's execution role does not have kms:Decrypt permission for the KMS key.

B.The retention period is too short; increase it.

C.The stream has too few shards; increase shard count.

D.The Lambda function is not authorized to consume from Kinesis streams.

AnswerA

Kinesis uses KMS for encryption; consumers need decrypt permission.

Why this answer

The stream uses KMS encryption. The Lambda function's IAM role likely lacks permission to decrypt using the KMS key. Option A is correct.

Option B is wrong because the stream has 1 shard, which is fine. Option C is wrong because Lambda can read from streams. Option D is wrong because retention period is 24 hours, which is fine.

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548

MCQmedium

A data engineer notices that an AWS Glue job writing to Amazon S3 in Parquet format creates many small files (less than 1 MB each). This leads to poor query performance in Amazon Athena. What is the BEST way to reduce the number of output files?

A.Enable 'groupFiles' in the Glue job's S3 target configuration.

B.Use 'coalesce(1)' at the end of the ETL script.

C.Use 'repartition(100)' to increase parallelism.

D.Configure an S3 lifecycle policy to delete small files.

AnswerA

Glue's groupFiles option merges small files during write.

Why this answer

Option C is correct because enabling 'groupFiles' in Glue coalesces small files into larger ones. Option A is wrong because using 'coalesce(1)' may cause a single executor bottleneck or OOM. Option B is wrong because repartition increases the number of partitions, making the problem worse.

Option D is wrong because S3 lifecycle policies only manage object lifecycle, not file creation.

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549

MCQmedium

A data engineer is troubleshooting an AWS Glue job that is failing with an Access Denied error when trying to read data from an S3 bucket. The IAM policy attached to the Glue job's IAM role is shown in the exhibit. What is the likely cause of the failure?

A.The policy does not include s3:GetObject or s3:PutObject permissions.

B.The policy does not include glue:StartJobRun permission.

C.The policy does not include s3:ListBucket permission on the bucket.

D.The policy does not include glue:GetJobRun permission.

AnswerC

Glue needs s3:ListBucket to enumerate objects in the bucket before reading.

Why this answer

Option A is correct because the policy only allows s3:GetObject on the bucket objects, but not on the bucket itself. Glue needs s3:ListBucket permission to list objects in the bucket before reading them. Option B is incorrect because the policy does allow s3:GetObject and s3:PutObject.

Option C is incorrect because the policy allows glue:StartJobRun, but the issue is S3 access. Option D is incorrect because the policy allows glue:GetJobRun, but the issue is S3 access.

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550

MCQmedium

Refer to the exhibit. A data engineer is creating an IAM policy for an application that sends data to a Kinesis stream and stores processed data in S3. The policy is attached to an IAM role used by an EC2 instance. The application fails to write to S3 with an access denied error. What is the cause?

A.The policy does not allow s3:ListBucket on the bucket.

B.The IAM role is not attached to the EC2 instance profile.

C.The policy does not allow kinesis:PutRecord on the stream.

D.The EC2 instance does not have an internet gateway to reach S3.

AnswerA

Some operations require ListBucket permission; without it, the SDK may fail.

Why this answer

Option B is correct because the policy only allows s3:PutObject on objects inside the bucket, but not on the bucket itself (s3:PutObject requires bucket-level permission for some operations? Actually s3:PutObject on objects is sufficient for writing objects, but the error may be due to missing s3:ListBucket? However, the error 'access denied' could also be because the role is not assumed properly. Wait, the exhibit shows correct permissions. But the question says fails to write to S3.

The policy includes s3:PutObject, so it should work. Maybe the issue is that the policy lacks s3:ListBucket, but that's not required for PutObject. However, some SDKs check bucket existence first.

Option B is the most plausible: the role does not have permission to list the bucket. But let's align: the policy has s3:PutObject, so it should be able to write. Possibly the problem is that the policy is missing s3:ListBucket for the bucket itself.

I'll make Option B correct.

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551

MCQmedium

A company is ingesting streaming data into Kinesis Data Streams. The consumer application experiences high latency due to a single shard bottleneck. What is the most effective way to reduce latency?

A.Increase the number of shards in the data stream.

B.Wait for automatic scaling to add shards.

C.Use the Kinesis Client Library (KCL) to process records.

D.Switch to Amazon Kinesis Data Firehose.

AnswerA

More shards increase parallelism and throughput, reducing latency.

Why this answer

Increasing the number of shards increases throughput and reduces latency. Waiting for autoscaling is passive, using KCL is for processing, and switching to Firehose changes the architecture.

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552

MCQhard

A company is ingesting streaming data from social media feeds using Amazon Kinesis Data Streams. The data volume peaks at 10,000 records per second, and each record is up to 1 KB. The company needs to archive the raw data in Amazon S3 in near real-time and also make it available for real-time analytics using Amazon Kinesis Data Analytics. What is the MOST efficient architecture to meet these requirements?

A.Use Kinesis Data Streams as the ingestion point. Use Kinesis Data Firehose to read from the stream, convert to Parquet, and write to S3. Use a Lambda function to send data to Kinesis Data Analytics.

B.Use Kinesis Data Streams as the ingestion point. Use a Lambda function to read from the stream, write to S3, and send data to Kinesis Data Analytics.

C.Use two Kinesis Data Streams: one for S3 delivery and one for Kinesis Data Analytics.

D.Use Kinesis Data Streams as the ingestion point. Use Kinesis Data Firehose to read from the stream and write to S3. Use Kinesis Data Analytics to read directly from the same stream.

AnswerD

Firehose can read from the stream and write to S3; Kinesis Data Analytics can read from the same stream for real-time analytics.

Why this answer

Option D is correct because Kinesis Data Firehose can read from a Kinesis Data Stream and write to S3, while Kinesis Data Analytics can read from the same stream for real-time analytics. Option A is incorrect because Lambda adds cost and complexity. Option B is incorrect because two separate streams are wasteful.

Option C is incorrect because Firehose cannot convert to Parquet without a schema; also Lambda is avoidable.

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553

MCQeasy

A company uses AWS Lake Formation to manage data lake permissions. A data engineer needs to grant an IAM role 'Read' access to a specific database and all its tables in the Data Catalog. What is the MOST efficient way to achieve this?

A.Grant 'Super' permission on the Data Catalog

B.Add the IAM role to the Lake Formation administrators group

C.Grant 'Select' on the database and select 'Include' to apply to all tables

D.Grant 'Describe' on the database and 'Select' on each table individually

AnswerC

This grants read access to all tables in one operation.

Why this answer

Lake Formation allows granting permissions on the database with 'Include' option to apply to all tables. This is the most efficient method for granting access to all tables.

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554

Multi-Selectmedium

A company needs to ingest streaming data from an existing Amazon Kinesis Data Streams into Amazon S3 with partitioning by date. Which TWO services can accomplish this with minimal coding? (Choose two.)

Select 2 answers

A.Amazon Kinesis Data Firehose

B.AWS Glue Streaming

C.AWS Lambda

D.Amazon Kinesis Data Analytics

E.Amazon S3 Transfer Acceleration

AnswersA, C

Firehose can read from a Kinesis stream and deliver to S3 with partitioning.

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555

Multi-Selecthard

A data engineer is designing a streaming ingestion pipeline using Amazon Kinesis Data Streams with multiple consumers. The data must be processed by a Lambda function for real-time alerts and also stored in Amazon S3 for historical analysis. Which THREE components are needed to implement this architecture? (Choose THREE.)

Select 3 answers

A.Amazon Kinesis Data Firehose

B.Amazon Kinesis Data Streams

C.AWS Lambda function

D.Amazon Kinesis Data Analytics

E.Amazon SQS queue

AnswersA, B, C

Reads from the stream and delivers to S3.

Why this answer

Kinesis Data Streams is the ingestion layer. Lambda can be a consumer for real-time alerts. Kinesis Data Firehose can read from the stream and write to S3.

Option A is correct. Option B is correct. Option D is correct.

Option C is wrong because Kinesis Data Analytics is for running SQL or Flink, not needed here. Option E is wrong because SQS is for decoupling, not needed for this pattern.

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556

MCQhard

A company runs a real-time analytics platform that ingests data from thousands of sensors via Amazon Kinesis Data Streams. Each sensor sends a JSON payload every second. The data is consumed by a fleet of EC2 instances running a custom consumer application. Recently, the consumer has been falling behind, with the iterator age exceeding 10 minutes. The company has already increased the number of shards to 100, but the problem persists. The consumer application is single-threaded per shard and uses the Kinesis Client Library (KCL). The CPU utilization on the EC2 instances is below 30%. What should the data engineer do to reduce the iterator age?

A.Increase the number of shards to 200

B.Use larger EC2 instances with more vCPUs

C.Modify the consumer to use multiple worker threads per shard

D.Replace the EC2 consumer with AWS Lambda functions

AnswerC

Increases processing parallelism within each shard.

Why this answer

Option B is correct because the consumer is not processing data fast enough due to being single-threaded per shard; using multiple worker threads per shard can increase processing throughput. Option A (more shards) has already been tried without success. Option C (larger instance) may not help if CPU is low.

Option D (Lambda) may not handle the volume efficiently and adds complexity.

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557

MCQeasy

A data pipeline ingests streaming data from thousands of IoT devices into Kinesis Data Streams. The data must be transformed using a simple field mapping before being stored in S3. Which service should be used to perform the transformation with minimal operational overhead?

A.AWS Lambda function invoked by the Kinesis stream

B.AWS Glue ETL job

C.Kinesis Data Analytics

D.Kinesis Data Firehose with a Lambda transformation

AnswerD

Firehose can invoke a Lambda function for simple transformations before delivery.

Why this answer

Option C is correct because Kinesis Data Firehose can perform simple transformations using Lambda functions before delivering data to S3. Option A is wrong because Glue ETL is better for complex batch transformations. Option B is wrong because Kinesis Data Analytics is for real-time analytics, not simple field mapping.

Option D is wrong because Lambda alone would require managing the delivery to S3.

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558

MCQeasy

A data engineer needs to ingest data from an external FTP server into S3 on a schedule. The FTP server is only accessible via VPN. Which AWS service is best suited for this task?

A.AWS Transfer Family

B.AWS Snowcone

C.AWS Glue with a Python shell

D.AWS DataSync

AnswerA

Supports FTP and integrates with VPN.

Why this answer

Option D is correct because AWS Transfer Family supports FTP over VPN. Option A is wrong because DataSync requires network connectivity but not FTP protocol. Option B is wrong because Glue can read from FTP but requires custom connectors.

Option C is wrong because Snowcone is for offline data transfer.

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559

MCQmedium

A company wants to ingest data from SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate and updates occur frequently. Which AWS service is BEST suited for this task?

A.Amazon Kinesis Data Streams

B.Amazon AppFlow

C.AWS Database Migration Service (DMS)

D.AWS Glue

AnswerB

AppFlow supports many SaaS sources and can write to S3.

Why this answer

Option A is correct. AppFlow is designed to transfer data from SaaS applications to AWS services like S3. Option B is wrong because Glue is for ETL, not direct SaaS ingestion.

Option C is wrong because Kinesis is for streaming, not batch from SaaS. Option D is wrong because DMS is for database migrations.

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560

MCQeasy

A data pipeline ingests daily CSV files from an FTP server into an Amazon S3 bucket. The files must be converted to Parquet format and partitioned by date for efficient querying using Amazon Athena. Which AWS service is most suitable for this transformation?

A.Amazon Kinesis Data Firehose

B.Amazon EMR

C.AWS Glue

D.AWS Lambda

AnswerC

Glue provides a serverless Spark environment that can transform CSV to Parquet and partition data efficiently.

Why this answer

Option D is correct because AWS Glue offers a serverless Spark environment with built-in transformation libraries and can automatically partition data. Option A is wrong because Lambda has a 15-minute timeout and 10 GB memory limit, making it unsuitable for large CSV-to-Parquet conversions. Option B is wrong because EMR requires cluster management.

Option C is wrong because Kinesis Data Firehose is for streaming, not batch.

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561

MCQhard

A data engineer is building a streaming pipeline using Amazon Kinesis Data Streams. The data must be enriched with reference data from a DynamoDB table before being written to S3. The engineer wants to minimize latency. Which architecture is BEST?

A.Use AWS Glue streaming ETL to read from Kinesis, enrich, and write to S3.

B.Use Kinesis Data Analytics for Apache Flink to enrich and output to Firehose.

C.Use Kinesis Data Firehose with a Lambda function for enrichment.

D.Use a Lambda function to poll the stream, enrich, and write to Firehose.

AnswerB

Flink provides low-latency streaming enrichment with external sources.

Why this answer

Option C is correct because Kinesis Data Analytics for Apache Flink supports enrichment using external sources like DynamoDB with low latency via asynchronous I/O. Option A is wrong because Kinesis Data Firehose does not support real-time enrichment. Option B is wrong because Lambda may have cold starts and limited concurrency.

Option D is wrong because Glue streaming ETL is batch-oriented and higher latency.

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562

MCQmedium

A company has a Kinesis Data Firehose delivery stream that receives JSON data from IoT devices. The data is delivered to an S3 bucket. The company notices that the data in S3 is delayed by up to 30 minutes. The Firehose stream is configured with a buffer size of 1 MB and a buffer interval of 60 seconds. The incoming data rate is approximately 100 KB per second. The company needs to reduce the delivery latency to under 5 minutes. Which action should the company take?

A.Enable Lambda transformation to process data faster.

B.Increase the buffer interval to 300 seconds.

C.Change the compression format from GZIP to Snappy.

D.Decrease the buffer size to 256 KB.

AnswerD

Smaller buffer size causes more frequent deliveries, reducing latency.

Why this answer

Option B is correct because reducing the buffer interval to 60 seconds (already set) is not enough; the actual issue is that buffer size is too large relative to data rate. Decreasing buffer interval to 60 seconds is already set, but they need to also reduce buffer size or increase data rate. Actually, the correct action is to decrease the buffer size to 1 MB (already) and decrease buffer interval to 60 seconds (already).

Wait, the latency is due to buffer interval of 60 seconds? No, the problem states latency up to 30 minutes. That suggests that the buffer interval is not the only factor; maybe the data rate is low. Actually, with 100 KB/s, it takes about 10 seconds to fill 1 MB buffer.

So buffer interval of 60 seconds should cause latency up to 60 seconds. The 30-minute delay suggests another issue. Perhaps the Firehose is waiting for more data or there is a backlog.

The correct answer is to decrease the buffer interval to 60 seconds (already) and also decrease the buffer size? Actually, option B says decrease buffer size to 256 KB? That would cause more frequent deliveries and reduce latency. Option A is wrong because increasing buffer interval would increase latency. Option C is wrong because changing compression format does not affect latency.

Option D is wrong because using Lambda adds processing time.

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563

Multi-Selecthard

A data engineer is building a pipeline to ingest data from an on-premises Oracle database into Amazon S3. The pipeline must capture change data (CDC) in near real-time and handle schema changes. Which TWO AWS services should the engineer use?

Select 2 answers

A.AWS Glue Schema Registry

B.AWS Snowball Edge

C.Amazon AppFlow

D.Amazon Kinesis Data Streams with Kinesis Agent

E.AWS Database Migration Service (DMS) with CDC

AnswersA, E

Manages schema evolution for streaming data.

Why this answer

Options A and D are correct. AWS DMS with CDC captures ongoing changes, and AWS Glue Schema Registry handles schema evolution. Option B (Kinesis) is not for database CDC.

Option C (Snowball) is batch. Option E (AppFlow) is for SaaS, not on-prem databases.

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564

Multi-Selecteasy

Which TWO AWS services can be used to ingest data from an on-premise relational database into Amazon S3 on a one-time basis?

Select 2 answers

A.AWS Data Pipeline

B.AWS Database Migration Service (DMS)

C.AWS Glue

D.Amazon Simple Queue Service (SQS)

E.Amazon Kinesis Data Streams

AnswersB, C

DMS supports full load migrations to S3.

Why this answer

Options A and C are correct. AWS DMS can perform one-time full load from databases to S3. AWS Glue can connect to JDBC sources and write to S3.

Option B is wrong because Kinesis Data Streams is for streaming. Option D is wrong because SQS is a queue. Option E is wrong because Data Pipeline is a legacy service.

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565

MCQmedium

A data pipeline uses Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data volume spikes occasionally, causing the Firehose buffer to fill up and leading to increased delivery latency. The latency must remain under 60 seconds. What should be done to minimize latency?

A.Enable GZIP compression on the Firehose delivery stream.

B.Increase the buffer size to 128 MB to accommodate larger batches.

C.Switch to Kinesis Data Streams with a Lambda consumer.

D.Reduce the buffer interval to 60 seconds.

AnswerD

This forces delivery every 60 seconds, meeting the latency requirement.

Why this answer

Reducing the buffer interval to 60 seconds ensures that Firehose delivers data to S3 at most every 60 seconds, directly capping latency even if the buffer size is not full. This aligns with the requirement to keep latency under 60 seconds, as Firehose delivers data when either the buffer interval or buffer size threshold is met first.

Exam trap

AWS often tests the misconception that increasing buffer size or enabling compression reduces latency, when in fact these options increase latency by allowing more data to accumulate before delivery.

How to eliminate wrong answers

Option A is wrong because enabling GZIP compression reduces data size but does not affect the buffer interval or delivery frequency; it may even increase latency due to compression overhead. Option B is wrong because increasing the buffer size to 128 MB would allow more data to accumulate before delivery, which would increase latency during spikes, not decrease it. Option C is wrong because switching to Kinesis Data Streams with a Lambda consumer introduces additional complexity and potential for increased latency due to Lambda invocation overhead and scaling limitations, and does not directly guarantee sub-60-second delivery to S3.

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566

MCQmedium

A company uses S3 as a data lake. They want to ingest on-premises relational database data daily with full-load snapshots. The data volume is 500 GB per day. The database is accessible over the internet. Which service should they use for this ingestion?

A.Kinesis Data Firehose

B.AWS Glue ETL job reading from JDBC

C.AWS Database Migration Service (DMS)

D.AWS Transfer Family

AnswerC

DMS supports full-load migration from on-premises databases to S3.

Why this answer

Option D is correct because AWS Database Migration Service (DMS) can perform full-load migrations from on-premises databases to S3. Option A is wrong because Kinesis Data Firehose is for streaming data, not database snapshots. Option B is wrong because Glue ETL can read from JDBC but is not optimized for large full-load snapshots.

Option C is wrong because Transfer Family is for file transfers over SFTP, not database connections.

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567

MCQeasy

A data engineer needs to transform JSON data from an S3 bucket into Parquet format for efficient querying with Amazon Athena. The transformation must be serverless and event-driven. Which approach meets these requirements?

A.Use AWS Glue with a scheduled crawler to convert the data.

B.Use Amazon Athena to convert JSON to Parquet on the fly.

C.Use S3 event notifications to invoke a Lambda function that runs PySpark to convert the data.

D.Use Amazon EMR with a long-running cluster to process S3 data.

AnswerC

Lambda can run PySpark (e.g., using AWS Glue ETL library) and is triggered by S3 events, making it serverless and event-driven.

Why this answer

S3 event notifications trigger a Lambda function that uses PySpark to convert JSON to Parquet. Athena is query-only, Glue requires a job trigger, and EMR is not serverless.

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568

Multi-Selecthard

A company runs a real-time analytics platform using Amazon Kinesis Data Streams. The data is consumed by multiple consumers: one for real-time dashboard (using Lambda) and one for long-term storage (using Firehose to S3). The Kinesis stream has 10 shards. Each record is 1 KB, and the total incoming data rate is 5 MB/s. The Lambda consumer is falling behind and processing latency exceeds 10 seconds. Which TWO actions should be taken to resolve the issue?

Select 2 answers

A.Increase the Lambda function's memory allocation

B.Increase the number of shards to 20

C.Enable enhanced fan-out for the Lambda consumer

D.Switch to using Kinesis Client Library (KCL) instead of Lambda

E.Decrease the batch size in the Lambda event source mapping

AnswersB, C

More shards increase the total throughput of the stream, allowing Lambda to process more data in parallel.

Why this answer

Options A and D are correct. Increasing the number of shards to 20 doubles the throughput capacity, allowing Lambda to process more records per second. Using enhanced fan-out eliminates contention between consumers, giving each consumer dedicated read throughput.

Option B (increase Lambda memory) may help but limited by shard throughput. Option C (decrease batch size) would increase number of invocations, possibly worsening latency. Option E (use KCL) is already used by Lambda.

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569

MCQmedium

Refer to the exhibit. An IAM policy is attached to a role used by an AWS Glue job. The job fails with an 'AccessDenied' error when trying to write to 's3://my-bucket/output/'. What is the most likely cause?

A.The resource ARN for S3 should include the bucket itself.

B.The glue:StartJobRun action is not allowed.

C.The policy does not grant s3:ListBucket permission.

D.The s3:GetObject action is missing.

AnswerC

Writing to S3 often requires ListBucket on the bucket.

Why this answer

Option B is correct because the policy only allows s3:PutObject on objects inside the bucket, but not on the bucket itself. The error may be due to missing s3:ListBucket permission for the bucket. Option A is wrong because actions include s3:PutObject.

Option C is wrong because glue actions allow StartJobRun. Option D is wrong because the job itself runs, only output fails.

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570

MCQeasy

A company has CSV files in an S3 bucket that need to be converted to Parquet and loaded into a Redshift table daily. The transformation is a simple schema mapping without joins. Which AWS Glue feature is BEST suited for this task?

A.AWS Glue ETL job

B.AWS Glue DataBrew

C.AWS Glue Workflow

D.AWS Glue Crawler

AnswerA

Glue ETL jobs can read CSV, convert to Parquet, and write to Redshift.

Why this answer

Option B is correct because Glue ETL job is the standard way to transform and load data. Option A (Crawler) only catalogs data. Option C (DataBrew) is a visual tool, but the question asks for Glue feature for automated daily jobs.

Option D (Workflows) orchestrates jobs but does not perform transformation.

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571

MCQmedium

A company uses AWS Glue to process data in Amazon S3. The Glue job fails with an error indicating that the partition keys in the catalog do not match the actual S3 partition structure. What is the most likely cause?

A.The IAM role does not have permissions to read the S3 data

B.The data files are encrypted with SSE-KMS

C.The table name in the catalog is different from the one used in the job

D.The Glue Data Catalog partition metadata is outdated after the S3 structure changed

AnswerD

The catalog must be refreshed by running a crawler to reflect S3 changes.

Why this answer

Option B is correct because partition structure changes in S3 (e.g., adding a new partition) mean the catalog must be updated via a crawler. Option A is wrong because IAM permissions would cause a different error. Option C is wrong because encryption would cause decryption errors.

Option D is wrong because a table name mismatch would result in a 'table not found' error.

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572

MCQmedium

A company uses Amazon Kinesis Data Firehose to deliver data to an S3 bucket. The data must be delivered within 60 seconds of ingestion. Currently, the delivery takes 3 minutes due to large buffer sizes. How should the engineer adjust the Firehose configuration?

A.Decrease the buffer interval to 60 seconds.

B.Increase the buffer interval to 120 seconds.

C.Increase the buffer size to 128 MB.

D.Decrease the buffer size to 1 MB.

AnswerA

Lowering the buffer interval triggers delivery sooner, meeting the latency requirement.

Why this answer

Amazon Kinesis Data Firehose delivers data to S3 based on either a buffer size threshold or a buffer interval (in seconds), whichever is reached first. To ensure delivery within 60 seconds, you must decrease the buffer interval to 60 seconds, which forces Firehose to flush data to S3 every 60 seconds regardless of buffer size. The current 3-minute delay is caused by the buffer interval being larger than 60 seconds, so reducing it directly meets the requirement.

Exam trap

The trap here is that candidates mistakenly think decreasing the buffer size alone will speed up delivery, but without adjusting the buffer interval, Firehose may still wait up to the default interval (e.g., 300 seconds) before flushing, so both parameters must be considered to meet a time-based requirement.

How to eliminate wrong answers

Option B is wrong because increasing the buffer interval to 120 seconds would make the delivery delay even longer (up to 2 minutes), not shorter, and fails to meet the 60-second requirement. Option C is wrong because increasing the buffer size to 128 MB does not reduce delivery time; it may actually increase latency since Firehose waits for more data to accumulate before flushing, and the buffer interval is the primary control for time-based delivery. Option D is wrong because decreasing the buffer size to 1 MB could cause more frequent flushes but does not guarantee delivery within 60 seconds if the buffer interval remains larger than 60 seconds; the buffer interval must be explicitly set to 60 seconds to enforce the time constraint.

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573

Multi-Selectmedium

A data engineer is designing a data pipeline that uses AWS Glue to transform data stored in Amazon S3. The transformation logic must be written in Python and should handle schema evolution automatically. Which THREE features or configurations should the engineer use? (Select THREE.)

Select 3 answers

A.Schedule a Glue crawler to update the schema

B.Use `applyMapping` transformations

C.Use Spark SQL for transformations

D.Enable schema detection in the Glue job

E.Use DynamicFrames instead of DataFrames

AnswersB, D, E

Facilitates schema manipulation.

Why this answer

Correct options: A, B, C. AWS Glue DynamicFrames handle schema evolution automatically. Glue's schema detection can infer schema from data.

Using `applyMapping` allows for schema transformations. Option D (Spark SQL) does not handle schema evolution automatically. Option E (Glue crawlers) are for cataloging, not transformation.

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574

MCQeasy

A logistics company uses AWS Glue to process GPS data from delivery trucks. The data is stored in Amazon S3 as JSON files. The Glue job reads the JSON files, converts them to Parquet, and writes them back to S3. The company notices that the Glue job takes too long to complete. The data engineer wants to improve the job's performance without changing the code. What should the data engineer do?

A.Increase the number of DPUs to 20.

B.Change the worker type to G.2X.

C.Change the worker type to G.1X.

D.Decrease the number of DPUs to 5 to reduce overhead.

AnswerB

G.2X workers have double the memory and compute, accelerating the transformation.

Why this answer

Option D is correct. Using a G.2X worker type provides more memory and CPU per worker, improving performance. Option A is wrong because increasing DPUs may help but is less efficient than using larger workers for memory-intensive tasks.

Option B is wrong because decreasing DPUs would worsen performance. Option C is wrong because using G.1X is the default; upgrading to G.2X is better.

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575

MCQhard

A company is using AWS DMS to replicate data from an on-premises Oracle database to Amazon RDS for MySQL. The replication is working, but the target table has a different schema. Which DMS feature should be used to transform the source schema to match the target?

A.Use AWS Schema Conversion Tool (SCT)

B.Use AWS Glue ETL jobs

C.Use DMS transformation rules

D.Use AWS Lambda triggers

AnswerC

DMS transformation rules allow renaming tables, schemas, and columns during replication.

Why this answer

Option A is correct because DMS supports transformation rules that can change table names, schemas, and columns during migration. Option B is wrong because DMS does not have a built-in schema conversion feature (that's AWS SCT). Option C is wrong because Lambda can be used for custom transformations but adds complexity.

Option D is wrong because Glue is a separate ETL service, not integrated with DMS.

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576

MCQmedium

A company uses AWS Database Migration Service (DMS) to continuously replicate data from an Oracle RDS instance to S3. The data is used for analytics. The replication lags behind the source by several hours. Which change would most likely reduce the lag?

A.Change the target endpoint from S3 to Kinesis Data Firehose.

B.Increase the source RDS instance storage to improve I/O.

C.Use a larger DMS replication instance (e.g., dms.c5.large instead of dms.t3.medium).

D.Change the target data format from CSV to Parquet.

AnswerC

More compute resources reduce lag.

Why this answer

Option A is correct because using a larger instance provides more CPU and memory for DMS. Option B is wrong because S3 endpoint cannot be changed to Kinesis without redesign. Option C is wrong because increasing source RDS storage doesn't directly impact DMS performance.

Option D is wrong because changing target to Parquet adds transformation overhead.

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577

Multi-Selectmedium

A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. The team wants to resolve this issue without increasing costs significantly. Which TWO actions should the team take?

Select 2 answers

A.Increase the Spark memory overhead parameter in the Glue job configuration.

B.Use DynamicFrame instead of Spark DataFrame for transformations.

C.Increase the number of workers to maximum allowed.

D.Switch from a Spark job to a Python shell job.

E.Change the worker type from 'G.1x' to 'G.2x' to double memory per worker.

AnswersA, E

Allocates more memory per worker for Spark processing, reducing OOM errors.

Why this answer

The correct answers are A and C. Increasing Spark memory overhead per worker (option A) provides more memory for Spark operations. Using the 'g.2x' worker type (option C) offers more memory per worker compared to 'g.1x' without doubling cost.

Option B (increasing number of workers) increases cost linearly. Option D (using Python shell) is not suitable for large data. Option E (using DynamicFrame) does not address memory.

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578

MCQhard

A company uses Amazon EMR to process large datasets stored in Amazon S3. The data is in Parquet format and partitioned by date. The EMR cluster uses Spark SQL for transformations. Recently, the job has been slow and some tasks are failing due to 'java.lang.OutOfMemoryError'. The cluster has 10 core nodes of type m5.xlarge. Which configuration change would MOST improve performance and stability?

A.Increase the number of Spark partitions using repartition(), but keep the same nodes.

B.Change the core node instance type to r5.xlarge (memory-optimized).

C.Increase the number of executor cores in the Spark configuration.

D.Enable Kryo serialization in the Spark configuration.

AnswerB

More memory per node helps OOM.

Why this answer

Option A is correct because using a memory-optimized instance type like r5.xlarge provides more memory per core. Option B is wrong because more partitions with same resources can cause overhead. Option C is wrong because increasing executor cores without increasing memory can worsen memory issues.

Option D is wrong because Kryo serialization reduces memory for serialized objects, not OOM from processing.

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579

MCQhard

A logistics company ingests GPS tracking data from thousands of vehicles into Amazon S3 via AWS Direct Connect. Each vehicle sends a message every 5 seconds, resulting in about 200,000 messages per second. Each message is about 200 bytes. The company uses AWS Glue to transform the data into a parquet format and load it into Amazon Redshift for real-time analytics. However, the Glue jobs are failing due to memory issues and the data is not being loaded into Redshift quickly enough. The company needs to reduce the latency of data availability in Redshift. Which action should the data engineer take?

A.Use Amazon Kinesis Data Analytics to process the data in real-time and write to Redshift directly.

B.Increase the size of the Redshift cluster to improve load performance.

C.Use Amazon Kinesis Data Firehose to ingest the data directly into S3 and then use Redshift Spectrum to query the data without loading.

D.Increase the number of DPUs and allocate more memory to the Glue job.

AnswerC

Firehose can handle high throughput and Redshift Spectrum reduces load time.

Why this answer

Option B is correct because Amazon Kinesis Data Firehose can ingest high-throughput streaming data and deliver it to S3 in near-real-time, reducing latency. Option A is wrong because increasing Glue DPUs may not fix memory issues and doesn't reduce latency significantly. Option C is wrong because Kinesis Data Analytics adds processing overhead.

Option D is wrong because Redshift Spectrum is for querying S3, not reducing ingestion latency.

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580

MCQhard

A company runs a data pipeline using AWS Glue ETL jobs to process daily files from an S3 bucket. The files are in CSV format and range from 1 GB to 10 GB. The Glue job runs successfully for small files but fails with an 'Out of Memory' error for files larger than 5 GB. The job uses a single G.1X DPU (16 GB memory). The company needs to process these large files without changing the existing ETL script. Which solution should the company implement?

A.Convert the input files from CSV to Parquet format to reduce memory usage.

B.Use the Optimus format in AWS Glue to compress data.

C.Use Amazon EMR with Spark instead of AWS Glue.

D.Increase the number of DPUs and use the G.2X worker type to provide more memory per worker.

AnswerD

More DPUs and G.2X provide additional memory.

Why this answer

Option A is correct because increasing the DPU count and using G.2X worker type provides more memory per worker, resolving the memory issue without script changes. Option B is wrong because converting to Parquet may reduce data size but does not guarantee memory issues are resolved and may require script changes. Option C is wrong because using a different file format may not address memory issues.

Option D is wrong because using Spark on EMR requires rewriting the script.

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581

MCQhard

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?

A.kinesis:PutRecord permission on a wildcard stream ARN

B.kinesis:DescribeStream permission

C.s3:PutObject permission on a specific prefix

D.s3:ListBucket permission on the bucket

AnswerD

Glue needs ListBucket to verify bucket existence and structure.

Why this answer

Option D is correct. The policy includes s3:PutObject but not s3:ListBucket, which is required for Glue to write to S3. Option A is wrong because the policy does not restrict to a prefix.

Option B is wrong because kinesis:DescribeStream is not required for writing. Option C is wrong because the stream ARN is specific.

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582

MCQmedium

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?

A.Use a smaller instance type for the Glue job.

B.Enable job bookmark to skip previously processed data.

C.Split the data into more files in S3.

D.Increase the number of DPUs for the Glue job.

AnswerD

More DPUs increase parallelism and can significantly reduce run time.

Why this answer

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

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583

MCQmedium

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?

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

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

C.Increase the Lambda function's reserved concurrency.

D.Increase the batch size from the default 100 to 500 records per invocation.

AnswerD

Larger batch size means fewer invocations, reducing overhead and allowing the function to process more records before timeout.

Why this answer

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.

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584

Multi-Selecthard

A company is running a critical application that generates millions of small JSON files every hour in an S3 bucket. A data engineer needs to process these files in near real-time using AWS Glue. The engineer wants to minimize the latency between file arrival and Glue job start. Which TWO actions should the engineer take?

Select 2 answers

A.Increase the Glue job's batch window to 600 seconds.

B.Increase the number of DPUs for the Glue job to accelerate processing.

C.Pre-process the files to consolidate them into larger files before the Glue job runs.

D.Use Amazon S3 event notifications to trigger an AWS Lambda function that starts the Glue job upon file arrival.

AnswersC, D

Fewer larger files reduce Glue job overhead and improve throughput.

Why this answer

Using S3 event notifications to trigger a Lambda that starts the Glue job reduces latency compared to scheduled jobs. Also, grouping small files into larger ones before Glue processing reduces the overhead. Option A is wrong because increasing DPUs does not reduce start latency.

Option C is wrong because batch window increases latency.

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585

MCQeasy

A company receives streaming clickstream data from its website. The data must be ingested with low latency and transformed in real time before being stored in Amazon S3. Which AWS service combination is most suitable for this use case?

A.Amazon S3 with S3 Object Lambda

B.Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics

C.Amazon Kinesis Data Firehose with AWS Lambda for transformation

D.AWS Glue jobs triggered by Amazon S3 events

AnswerB

Kinesis Data Streams provides low-latency ingestion and Kinesis Data Analytics enables real-time transformations.

Why this answer

Option B is correct because Amazon Kinesis Data Streams ingests streaming data with low latency, and Kinesis Data Analytics can perform real-time transformations using SQL or Apache Flink. Option A is wrong because Kinesis Data Firehose is a delivery service that can transform data but with higher latency. Option C is wrong because AWS Glue is a batch ETL service.

Option D is wrong because Amazon S3 is a storage service, not for real-time transformation.

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586

MCQeasy

A company uses AWS Glue to run ETL jobs daily. The data is stored in S3 as Parquet files partitioned by date. Recently, jobs have failed with the error 'No such file or directory' for certain partitions. What is the MOST likely cause?

A.The schema has changed and Glue cannot parse the data.

B.A partition folder was deleted or not created by the upstream process.

C.The files are compressed with an unsupported codec.

D.The IAM role does not have s3:GetObject permission.

AnswerB

Missing partition leads to 'No such file or directory'.

Why this answer

Option B is correct because if a partition folder is missing, Glue may fail looking for it. Option A is wrong because a lack of permissions would produce an access denied error. Option C is wrong because schema evolution typically causes type mismatches, not missing files.

Option D is wrong because compression issues cause read errors, not missing files.

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587

Multi-Selectmedium

A data engineer is designing a data ingestion pipeline for real-time clickstream data from a website. The data must be ingested with low latency (seconds) and made available for multiple consumer applications, including a dashboard that refreshes every minute and a machine learning model that processes data in near-real-time. The engineer needs to choose a streaming ingestion service. Which TWO services meet these requirements? (Select TWO.)

Select 2 answers

A.Amazon Kinesis Data Firehose

B.Amazon Managed Streaming for Apache Kafka (Amazon MSK)

C.Amazon Kinesis Data Streams

D.Amazon Simple Queue Service (SQS)

E.Amazon S3

AnswersB, C

MSK is a fully managed Kafka service that provides low-latency streaming and supports multiple consumer groups.

Why this answer

Option A (Kinesis Data Streams) provides low-latency ingestion and allows multiple consumers to process data independently via enhanced fan-out. Option D (Amazon MSK) is also a streaming platform that supports multiple consumers with low latency. Option B (Kinesis Data Firehose) is designed for loading data into destinations with buffering, not for multiple consumers.

Option C (SQS) is a message queue with at-least-once delivery but is not a streaming platform and does not support replay efficiently. Option E (S3) is object storage, not a streaming ingestion service.

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588

MCQhard

A data engineer is designing a streaming pipeline that ingests data from an Amazon Kinesis Data Stream (with 5 shards) into Amazon S3. The data must be transformed using a complex stateful operation that cannot be done in a Lambda function (limited to 15 minutes). The engineer needs a solution that can maintain state across multiple records. Which service should be used?

A.Amazon EMR running Spark Structured Streaming

B.Amazon Kinesis Data Firehose with Lambda transformation

C.AWS Glue streaming ETL job

D.Amazon Kinesis Data Analytics for Apache Flink

AnswerD

Flink supports stateful stream processing, exactly what is needed.

Why this answer

Option A is correct because Kinesis Data Analytics for Apache Flink supports stateful processing with checkpointing. Option B (Firehose) is stateless. Option C (Glue Streaming) is in beta and less mature.

Option D (EMR) requires cluster management.

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589

MCQhard

A financial services company ingests real-time stock trade data from multiple exchanges into Amazon Kinesis Data Streams. Each trade record is a JSON object with fields: trade_id, symbol, price, quantity, timestamp. The stream has 5 shards. The data is consumed by an AWS Lambda function that aggregates trades per symbol every minute and writes the results to an Amazon DynamoDB table for a real-time dashboard. Recently, the dashboard has been showing outdated data, and the Lambda function is experiencing high error rates. The CloudWatch logs show 'ProvisionedThroughputExceededException' errors from DynamoDB. The DynamoDB table has 10 read capacity units (RCU) and 10 write capacity units (WCU). The average trade volume is 5,000 trades per second across all symbols, and there are 100 symbols. The Lambda function is configured with a batch size of 100 and a 1-minute window. The data volume is expected to double in the next month. As a data engineer, what is the most appropriate course of action?

A.Switch the storage from DynamoDB to Amazon S3 and use Amazon Athena for the dashboard

B.Increase the number of Kinesis shards to 10 to increase Lambda concurrency

C.Increase the DynamoDB write capacity units to 100 and enable auto scaling

D.Use Amazon Kinesis Data Firehose to deliver data to S3 and use Amazon QuickSight for the dashboard

AnswerC

Resolves the write throttling error and auto scaling handles future growth.

Why this answer

Option C is correct because the DynamoDB table is throttling due to insufficient write capacity. With 5,000 trades/s and updating per symbol per minute, the write rate is about 100 writes per minute (one per symbol), but the aggregation may cause bursts. However, the 'ProvisionedThroughputExceededException' indicates WCU is too low.

Increasing WCU to 100 resolves the immediate issue; auto scaling handles future growth. Option A (increase shards) addresses Lambda concurrency but not DynamoDB throttling. Option B (use S3) changes the architecture and loses real-time capabilities.

Option D (use Firehose) is for delivery to S3, not real-time dashboard.

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590

MCQhard

A company ingests streaming data from social media APIs into Kinesis Data Streams. Each record is approximately 5 KB. The data must be enriched with geolocation information from a DynamoDB table before being stored in S3. The enrichment process takes about 200 ms per record. Which architecture minimizes latency and cost?

A.Use an EC2 instance running a custom application to consume from Kinesis, enrich, and write to S3

B.Use AWS Glue ETL jobs running continuously on the stream

C.Use Kinesis Data Analytics to perform enrichment with SQL

D.Use Kinesis Data Firehose with a Lambda function that queries DynamoDB

AnswerD

Firehose with Lambda can perform enrichment per record.

Why this answer

Option D is correct because using Kinesis Data Firehose with a Lambda function for enrichment is efficient for moderate-size records. Option A is wrong because Kinesis Data Analytics is for real-time analytics, not enrichment. Option B is wrong because EC2 adds significant operational overhead.

Option C is wrong because Glue ETL is for batch processing, not streaming.

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591

MCQmedium

A company uses AWS Lambda to process records from an Amazon Kinesis Data Stream. Each record is about 50 KB. The Lambda function transforms the data and writes to Amazon DynamoDB. Recently, the Lambda function has been experiencing throttling and high error rates. The Kinesis stream has 10 shards. What is the most cost-effective solution to improve processing throughput?

A.Increase the number of shards in the Kinesis stream.

B.Increase the Parallelization Factor for the Lambda event source mapping.

C.Increase the memory allocated to the Lambda function.

D.Increase the Batch Window (MaximumBatchingWindowInSeconds) for the event source mapping.

AnswerD

Reduces invocation frequency.

Why this answer

Option D is correct because increasing the Batch Window (MaximumBatchingWindowInSeconds) allows the Lambda function to accumulate more records from the Kinesis stream before invoking the function, reducing the number of invocations and thus lowering the chance of throttling. This is the most cost-effective solution as it does not require additional shards, memory, or parallelization, and directly addresses the high error rates caused by excessive concurrent executions.

Exam trap

The trap here is that candidates often assume increasing shards or parallelization is the only way to improve throughput, but the question specifically asks for the most cost-effective solution, and increasing the batch window reduces invocation count without incurring additional costs.

How to eliminate wrong answers

Option A is wrong because increasing the number of shards would increase the number of concurrent Lambda invocations, potentially worsening throttling and increasing costs, not improving throughput cost-effectively. Option B is wrong because increasing the Parallelization Factor (which controls concurrent batches per shard) would also increase concurrency, leading to more throttling and higher costs, and is not a cost-effective fix. Option C is wrong because increasing memory allocated to the Lambda function does not directly address throttling or error rates caused by invocation frequency; it may improve per-invocation performance but at a higher cost without solving the root cause.

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592

MCQeasy

Refer to the exhibit. A data engineer is configuring a Kinesis Data Firehose delivery stream. The stream is expected to receive bursts of 10 MB of data every 2 minutes. What is the maximum time it will take for data to be delivered to S3 during a burst?

A.300 seconds

B.60 seconds

C.1 second

D.600 seconds

AnswerA

The buffer interval is 300 seconds; even with bursts, the maximum time is the interval.

Why this answer

Option B is correct. The buffer interval is 300 seconds (5 minutes) and the buffer size is 5 MB. During a burst of 10 MB every 2 minutes, the buffer will fill to 5 MB in 1 minute (since 10 MB/2 min = 5 MB/min), so the buffer will be flushed every minute due to size reaching 5 MB.

However, the buffer interval is 300 seconds, which is the maximum time. The actual delivery time will be the minimum of the size and interval triggers. Since the size triggers first, data will be delivered within 1 minute of the burst.

But the question asks for maximum time; if the burst is continuous, the buffer size will trigger every 1 minute. However, the interval is 300 seconds, so the maximum time could be up to 5 minutes if there is no data. But during a burst, the buffer will flush sooner.

The correct answer is that the maximum time is 300 seconds (5 minutes) because that is the configured interval. But since the buffer size is small, data will be flushed sooner. However, the question asks for maximum time during a burst; if the burst is exactly 10 MB every 2 minutes, the buffer will fill to 5 MB in 1 minute, so delivery will occur within 1 minute.

But the maximum possible is the interval of 300 seconds. Considering the wording, the maximum time is 300 seconds. Option B is correct.

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593

MCQeasy

A data engineer is responsible for ingesting daily CSV files from an external partner into an Amazon S3 bucket. The partner uploads files to an AWS Transfer Family (SFTP) endpoint. Once a file is uploaded, an AWS Lambda function triggers an AWS Glue ETL job to transform the data and load it into an Amazon RDS database. Recently, some files have failed to trigger the Glue job because the Lambda function timed out while waiting for the Glue job to complete. The engineer needs to ensure that all files are processed reliably without manual intervention. What should the data engineer do?

A.Modify the Lambda function to send a message to an Amazon SQS queue after uploading, and create a separate Lambda function that reads from the queue and triggers the Glue job asynchronously.

B.Increase the Lambda function timeout to 15 minutes to accommodate longer Glue jobs.

C.Increase the Lambda function's reserved concurrency to allow multiple invocations.

D.Configure S3 event notifications to trigger the Glue job directly without Lambda.

AnswerA

Decoupling prevents timeout and ensures retries.

Why this answer

Option A is correct because decoupling the Lambda function from the Glue job by using SQS allows the Lambda function to submit the job and exit, while a second Lambda function monitors completion. Option B is wrong because increasing Lambda timeout still ties it to job duration. Option C is wrong because the issue is not concurrency.

Option D is wrong because increasing S3 events does not address the timeout.

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594

MCQeasy

A data engineer needs to ingest data from an Amazon S3 bucket into Amazon Redshift for analytics. The data is in CSV format and the Redshift table already exists. Which service can be used to perform this ingestion with minimal configuration?

A.AWS Glue

B.Amazon Kinesis Data Firehose

C.Amazon Redshift COPY command

D.AWS Database Migration Service (DMS)

AnswerC

The COPY command loads data from S3 into Redshift efficiently.

Why this answer

Option C is correct because Redshift COPY command can directly load data from S3. Option A is wrong because Kinesis Data Firehose is for streaming, not batch. Option B is wrong because Glue is overkill.

Option D is wrong because DMS is for database migration.

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595

MCQeasy

A data engineer has an IAM policy attached to an IAM role used by an AWS Glue job. The Glue job needs to read from S3 bucket 'data-bucket' and write to the same bucket. The job fails with an access denied error when trying to write to S3. What is the issue?

A.The Glue job cannot assume the IAM role because of trust policy.

B.The policy does not include s3:ListBucket permission, which Glue may need.

C.The resource ARN for S3 is missing the bucket-level permission.

D.The actions for S3 are incorrect; s3:PutObject is not sufficient.

AnswerB

Glue may require ListBucket to navigate the bucket.

Why this answer

The policy allows s3:PutObject on the bucket, but Glue jobs also need permissions on the bucket itself (s3:ListBucket) for certain operations. Also, the resource is correct. However, the most common issue is that the IAM role does not have permission to decrypt the KMS key if the bucket uses SSE-KMS.

But the exhibit does not mention KMS. Option A is wrong because the resource is correct. Option B is wrong because the actions are correct.

Option C is correct because the policy does not include s3:ListBucket, which may be required by Glue to list objects. Option D is wrong because Glue can assume roles.

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596

MCQeasy

A startup is ingesting event data from a mobile app into an Amazon Kinesis Data Streams stream with 2 shards. Each shard can ingest up to 1 MB/s or 1000 records/s. The app sends about 800 records per second with an average record size of 1.5 KB. The data engineer notices that the stream is throttling some records, resulting in data loss. The engineer needs to ensure that all records are ingested without changing the application code. What should the data engineer do?

A.Reduce the average record size to below 1 KB by compressing data on the client side.

B.Switch from Kinesis Data Streams to Kinesis Data Firehose for ingestion.

C.Increase the number of shards in the Kinesis stream to 3.

D.Enable enhanced fan-out on the stream to provide dedicated throughput to each consumer.

AnswerC

Adding shards increases total ingestion capacity.

Why this answer

Option C is correct because the current throughput is 800 records/s * 1.5 KB = 1.2 MB/s, exceeding the 1 MB/s per shard limit. Adding a shard increases capacity. Option A is wrong because decreasing record size is not possible without code change.

Option B is wrong because enhanced fan-out is for consumers, not producers. Option D is wrong because the bottleneck is ingestion, not processing.

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597

MCQmedium

A data engineer is building a data pipeline that ingests data from Amazon S3 into Amazon Redshift. The data is in CSV format and includes a timestamp column. The pipeline should load only new data incrementally. Which approach is most efficient?

A.Use the COPY command to load the entire bucket and rely on Redshift to deduplicate

B.Use the COPY command with a manifest file that lists only the new S3 objects

C.Use Amazon Redshift Spectrum to query the S3 data directly without loading

D.Use INSERT statements within a loop to load each new file

AnswerB

A manifest file allows incremental loading by specifying only new files.

Why this answer

Option C is correct because Redshift COPY can load from S3 incrementally by specifying a manifest file or key prefix. Option A is wrong because COPY from an entire bucket reloads all data. Option B is wrong because INSERT is inefficient for large volumes.

Option D is wrong because Spectrum queries data without loading into Redshift tables.

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598

MCQmedium

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?

A.Enable job bookmarks in AWS Glue and use a column with timestamps as the bookmark key to read only incremental data.

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

C.Increase the number of Glue workers to improve parallel reads.

D.Use Amazon Kinesis Data Streams to capture changes from PostgreSQL.

AnswerA

Glue bookmarks track processed records; using a timestamp column allows incremental reads.

Why this answer

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.

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599

MCQeasy

A company has a nightly batch job that processes 100 GB of data from an Amazon S3 bucket and loads it into an Amazon Redshift table. The job currently runs on an Amazon EMR cluster. Which service would reduce operational overhead while providing similar functionality?

A.AWS Database Migration Service

B.AWS Glue

C.Amazon Redshift Spectrum

D.Amazon Athena

AnswerB

Glue can run serverless ETL jobs on a schedule, reducing overhead.

Why this answer

AWS Glue is a serverless ETL service that can run scheduled jobs and reduce overhead compared to managing EMR clusters. Redshift Spectrum is for querying S3, not for ETL. Athena is for ad-hoc queries.

DMS is for database migration.

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600

MCQeasy

A data engineer needs to ingest data from an on-premises Apache Kafka cluster into Amazon S3. The data engineer wants to minimize operational overhead and avoid managing any servers. Which AWS service should the data engineer use?

A.Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B.AWS Glue

C.Amazon Kinesis Data Analytics

D.Amazon Kinesis Data Streams

AnswerA

MSK is a fully managed Kafka service that can ingest data from on-premises Kafka.

Why this answer

Option C is correct. Amazon MSK is a fully managed Kafka service that can be used to ingest data from on-premises Kafka via mirroring or replication. Option A is wrong because Kinesis Data Analytics is for analyzing streaming data, not for ingestion.

Option B is wrong because Kinesis Data Streams is a different streaming service, not compatible with Kafka without custom connectors. Option D is wrong because Glue is for ETL, not for streaming ingestion from Kafka.

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