DEA-C01 Mock Exam 84 — 25 Questions

Question 1

A data engineer needs to design a data ingestion pipeline that captures streaming data from mobile app events into Amazon S3 for analytics. The pipeline must support real-time processing of events and allow for schema evolution over time. Which AWS services should the engineer use? (Choose THREE.)

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

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Analytics is correct because it enables real-time processing of streaming data using SQL or Apache Flink, allowing the engineer to analyze mobile app events as they arrive. This supports the requirement for real-time processing before the data is stored in Amazon S3 for analytics.

Answer

AWS Glue ETL jobs

Answer

AWS AppFlow

Question 2

A data pipeline uses Amazon Kinesis Data Streams with enhanced fan-out consumers. The team notices that one consumer falls behind and data accumulates. Which action will help this consumer catch up without affecting other consumers?

Accepted Answer

Register an enhanced fan-out consumer for the slow consumer.. Registering a new enhanced fan-out consumer with its own dedicated read throughput allows it to catch up independently. Increasing shards affects all consumers, and increasing iterator age may help but doesn't increase throughput.

Answer

Increase the retention period of the stream.

Answer

Increase the number of shards in the stream.

Answer

Use a Lambda consumer instead of an enhanced fan-out consumer.

Question 3

A company needs to ingest real-time clickstream data from a web application into Amazon Redshift with minimal latency. The data volume is high and requires processing before loading. Which architecture is MOST appropriate?

Accepted Answer

Kinesis Data Streams -> Kinesis Data Firehose -> Redshift. D is correct because Kinesis Data Streams captures high-volume clickstream data in real time, and Kinesis Data Firehose can buffer, transform (e.g., with Lambda), and load the data directly into Amazon Redshift with near-zero latency. This architecture is purpose-built for streaming ingestion with minimal overhead, unlike batch or intermediary storage approaches.

Answer

AWS Glue ETL jobs scheduled every 5 minutes -> Redshift

Answer

S3 -> Lambda -> Redshift

Answer

DynamoDB Streams -> Lambda -> Redshift

Question 4

Which TWO practices improve the performance of AWS Glue ETL jobs? (Choose two.)

Accepted Answer

Use pushdown predicates to filter data at the source. Pushdown predicates (Option A) improve AWS Glue ETL performance by filtering data at the source before it is read into the job. This reduces the volume of data transferred and processed, which is especially effective when using formats like Parquet or ORC that support predicate pushdown natively. By applying filters early, Glue avoids scanning unnecessary partitions or rows, leading to faster execution and lower costs.

Answer

Increase the number of DPUs to the maximum allowed

Answer

Use the smallest possible file size for input data

Answer

Enable AWS Glue job metrics and debug logging

Question 5

Refer to the exhibit. A data engineer is configuring an IAM policy for an AWS Glue ETL job that reads data from the 'my-data-bucket' S3 bucket, transforms it, and writes the output back to the same bucket. The engineer wants to prevent accidental deletion of objects. Based on the policy, which statement is true about the Glue job's permissions?

Accepted Answer

The job can read and write objects, but cannot delete objects.. The IAM policy explicitly denies the `s3:DeleteObject` action, which prevents the Glue job from deleting objects in the 'my-data-bucket' S3 bucket. The policy allows `s3:GetObject` and `s3:PutObject` actions, enabling the job to read and write objects as required for the ETL process. This ensures the job can perform its transformation tasks without the risk of accidental deletion.

Answer

The job can write objects but cannot read objects.

Answer

The job can read objects but cannot write objects.

Answer

The job can read and write, but may also delete objects.

Question 6

A company needs to ingest CSV files from an FTP server into Amazon S3 daily. The files are typically 50 MB each, and the process should be fully managed with minimal operational overhead. Which AWS service should be used?

Accepted Answer

AWS Transfer Family. AWS Transfer Family is the correct choice because it provides a fully managed, serverless solution for transferring files to and from Amazon S3 using FTP, FTPS, or SFTP protocols. It eliminates the need to manage any FTP infrastructure, directly integrates with S3 as a destination, and handles the daily ingestion of 50 MB CSV files with minimal operational overhead, aligning perfectly with the requirement for a fully managed service.

Answer

AWS Lambda with FTP library

Answer

AWS DataSync

Answer

Amazon AppFlow

Question 7

A company wants to transform data in Amazon S3 using SQL queries without provisioning servers. The transformations are ad-hoc and run occasionally. Which service should be used?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it enables serverless, ad-hoc SQL querying directly on data stored in Amazon S3 without requiring any infrastructure provisioning. Since the transformations are occasional and ad-hoc, Athena's pay-per-query model and zero setup overhead align perfectly with the requirement.

Answer

AWS Glue

Answer

Amazon Redshift Spectrum

Answer

Amazon EMR

Question 8

Refer to the exhibit. A data engineer runs this AWS Glue Data Catalog DDL statement to create a table. The CSV files in 's3://my-bucket/sales/' use a pipe delimiter (|) instead of a comma. What change is needed to correctly read the data?

Accepted Answer

Change the 'field.delim' property to '|'.. The AWS Glue Data Catalog DDL statement uses the default 'field.delim' property, which expects comma-separated values. Since the CSV files use a pipe delimiter (|), the table will not parse rows correctly. Setting 'field.delim' to '|' in the SerDe properties tells the Hive-compatible SerDe to split on pipes instead of commas, enabling correct data ingestion.

Answer

Change the LOCATION to read from a subfolder.

Answer

Add a partition projection configuration.

Answer

Run a crawler to detect the schema automatically.

Question 9

A company uses AWS DMS to migrate a 2 TB PostgreSQL database to Amazon Aurora PostgreSQL. The migration is taking longer than expected due to the initial load. Which AWS service can be used to accelerate the initial load by transferring the database files directly?

Accepted Answer

AWS Snowball. AWS Snowball is a petabyte-scale data transport solution that uses physical storage devices to transfer large amounts of data into and out of AWS. For a 2 TB PostgreSQL database, the initial load via DMS over the network can be slow due to bandwidth constraints, especially for large datasets. By using Snowball, you can export the database files (e.g., using pg_dump or physical file copy) to the device, ship it to AWS, and have the data loaded directly into Amazon Aurora PostgreSQL, bypassing the network bottleneck and significantly accelerating the initial load.

Answer

Amazon S3 Transfer Acceleration

Answer

AWS Direct Connect

Answer

Amazon Kinesis Data Firehose

Question 10

A company uses AWS DMS to migrate data from an on-premises Oracle database to Amazon RDS for PostgreSQL. The migration is ongoing with continuous replication. The data engineer notices that some changes are not being captured in the target database. What is the MOST likely cause?

Accepted Answer

The source Oracle database is not configured to retain archived redo logs for a sufficient period.. In AWS DMS continuous replication (CDC), the service reads changes from the source Oracle database's archived redo logs. If the logs are rotated or deleted before DMS has a chance to process them, changes are lost. Option D is correct because insufficient retention of archived redo logs is the most likely cause of missing changes in the target.

Answer

The VPC peering connection between on-premises and AWS is down.

Answer

The DMS task's table mapping is incorrectly configured.

Answer

DMS is not publishing task logs to CloudWatch Logs.

Question 11

A data engineer needs to transform JSON data from a Kinesis Data Stream into Parquet format and store it in an S3 data lake. The transformation includes simple field mapping and data type conversions. Which AWS service is the most cost-effective for performing this transformation in near-real-time?

Accepted Answer

AWS Lambda function triggered by Kinesis Data Firehose. AWS Lambda functions triggered by Kinesis Data Firehose are the most cost-effective solution for near-real-time transformations because Lambda allows you to perform lightweight field mapping and data type conversions on streaming data with a pay-per-invocation model, while Firehose handles buffering, compression, and direct delivery to S3 in Parquet format. This serverless approach avoids the overhead and cost of provisioning clusters or running continuous jobs, making it ideal for simple transformations on high-frequency, low-latency streams.

Answer

Amazon Athena with CTAS (CREATE TABLE AS SELECT)

Answer

AWS Glue ETL job

Answer

Amazon EMR with Spark Streaming

Question 12

A data engineer is designing a data pipeline that ingests CSV files from an FTP server to Amazon S3. The files arrive hourly and each file is about 500 MB. The engineer wants to minimize operational overhead and cost. Which approach is best?

Accepted Answer

Use AWS Transfer for SFTP and point the endpoint to an S3 bucket. AWS Transfer for SFTP provides a fully managed FTP service that writes directly to S3, eliminating the need to manage servers. Lambda with boto3 is code-heavy; EC2 requires management; Snowball is for large offline transfers.

Answer

Write a Python script in AWS Lambda using boto3 to download from FTP and upload to S3

Answer

Use AWS Snowball Edge to transfer files weekly

Answer

Deploy an Amazon EC2 instance with a cron job to run wget and aws s3 cp

Question 13

The exhibit shows the output of describing an Amazon Kinesis Data Stream. A producer is sending records but the consumer is not receiving all records. What is the most likely cause?

Accepted Answer

The stream has only one shard, causing write throttling. The stream has only one shard, which provides a maximum throughput of 1 MB/s or 1000 records/s for writes. If the producer exceeds this, records will be throttled. The retention period is 24 hours, which is fine. The stream status is ACTIVE. There is no indication of a faulty shard. The consumer might be slow, but the question asks for cause of not receiving all records; throttling due to insufficient shards is a common issue.

Answer

The stream is in ACTIVE status, which prevents reading

Answer

The retention period is too short

Answer

The hash key range is too wide

Question 14

The exhibit shows an IAM policy attached to a role used by an AWS Glue ETL job. The job reads from an S3 bucket and writes to another S3 bucket. However, the job fails with an access denied error when trying to write to the output bucket. What is the most likely cause?

Accepted Answer

The policy only allows PutObject on the my-data-lake bucket, but the job writes to a different bucket. The policy allows s3:PutObject on my-data-lake/*, but if the output bucket is different (e.g., my-output-bucket), the policy does not cover it. The error is due to missing permissions on the output bucket. The Glue service role may not have permissions to write to the output bucket. The policy does not restrict resource to only one bucket, but the ARN specifies my-data-lake. The job might be trying to write to a different bucket. There is no issue with Glue actions.

Answer

The policy is missing permissions for AWS KMS to decrypt/encrypt objects

Answer

The policy does not allow glue:StartJobRun on the specific job

Answer

The policy does not include s3:ListBucket permission

Question 15

A data engineer is troubleshooting an AWS Glue ETL job that fails with the error: 'An error occurred while calling o137.pyWriteDynamicFrame. No such file or directory: s3://bucket/output/part-00000.parquet'. The job reads from a JDBC source and writes to S3. What is the most likely cause?

Accepted Answer

The output S3 path does not exist and the Glue job does not have permission to create it. The error 'No such file or directory: s3://bucket/output/part-00000.parquet' indicates that the Glue job is trying to write to an S3 path that does not exist. By default, AWS Glue does not automatically create the target S3 bucket or prefix; it requires the path to already exist or the IAM role to have s3:PutObject permissions that allow the S3 service to create the object. Since the error occurs at the write stage (pyWriteDynamicFrame) and not during read, the most likely cause is that the output S3 path does not exist and the Glue job lacks the necessary permissions to create it.

Answer

The schema of the source data has changed, causing a mismatch during write

Answer

The Glue job ran out of memory during the transformation phase

Answer

The IAM role attached to the Glue job lacks permissions to read from the JDBC source

Question 16

A company needs to transform JSON data from an S3 bucket into a structured format for Amazon Redshift. The transformation should be done serverlessly. Which service should be used?

Accepted Answer

AWS Glue. AWS Glue is the correct choice because it is a fully managed, serverless ETL service designed specifically for transforming and preparing data for analytics, including converting JSON to structured formats like Parquet or ORC for Amazon Redshift. It can crawl the S3 source, infer schemas, and run Spark-based transformation jobs without provisioning any infrastructure, aligning perfectly with the serverless requirement.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

AWS Lambda

Question 17

Which TWO options are valid methods to ingest on-premises relational database data into Amazon S3 for analytics? (Choose 2.)

Accepted Answer

AWS Glue ETL job with JDBC connection to source. AWS Glue ETL jobs can connect to on-premises relational databases via JDBC, extract data, and write it directly to Amazon S3 in formats like Parquet or ORC. This is a fully managed, serverless approach suitable for batch ingestion and transformation of structured data for analytics.

Answer

AWS Snowball Edge

Answer

Amazon Kinesis Data Streams with Direct Put

Answer

Amazon AppFlow

Question 18

A company uses AWS Glue ETL jobs to transform data in Amazon S3. The data is partitioned by date and hour. The job reads the latest hour's data, performs aggregations, and writes results to a separate S3 bucket. The job runs every hour and processes approximately 500 MB of input data. The team notices that the job takes longer than expected, often exceeding the 1-hour window. Which action would most effectively reduce the job's runtime?

Accepted Answer

Increase the number of workers (DPUs) for the Glue job.. Increasing the number of workers (DPUs) for the Glue job directly addresses the root cause: the job is CPU- or memory-bound due to insufficient parallelism for the 500 MB hourly workload. By allocating more DPUs, AWS Glue can distribute the aggregation and write operations across more executors, reducing wall-clock time and keeping the job within the 1-hour window. This is the most effective action because the job's bottleneck is compute capacity, not data format or processing framework.

Answer

Use a Python shell job instead of a Spark job.

Answer

Switch from using DynamicFrame to using Spark SQL for transformations.

Answer

Repartition the input data into more partitions before reading.

Question 19

A company is ingesting data from multiple on-premises databases into AWS using AWS Database Migration Service (DMS). The data must be continuously replicated with minimal downtime. However, the source databases do not support native CDC. What should the data engineer do to enable continuous replication?

Accepted Answer

Use AWS DMS with log-based CDC if the source databases support it; otherwise, use DMS with batch replication and schedule frequent refreshes.. When source databases do not support native CDC, AWS DMS can still achieve continuous replication by using log-based CDC if the database engine supports it (e.g., Oracle, SQL Server, MySQL, PostgreSQL). If log-based CDC is not supported, DMS batch replication with frequent scheduled refreshes provides near-continuous replication by periodically extracting full or incremental changes, minimizing downtime. This approach leverages DMS's built-in task settings for change data capture and resumption without requiring external streaming services.

Answer

Use Amazon Kinesis Data Streams with a custom producer to capture database changes.

Answer

Use Amazon Redshift Spectrum to directly query the on-premises databases.

Answer

Set up AWS Glue jobs to run every minute to extract and load the data.

Question 20

A data engineer is designing a data lake on S3 with fine-grained access control using AWS Lake Formation. Which FOUR permissions can be managed by Lake Formation?

Accepted Answer

SELECT on a table. AWS Lake Formation supports fine-grained permissions including SELECT, INSERT, DESCRIBE, and DELETE on tables. ALTER TABLE is not a Lake Formation permission; it is managed by other services like IAM. Therefore, the correct answers are A, B, C, and E.

Answer

ALTER TABLE on a table