Reliable Study Data-Engineer-Associate Questions & Exam Data-Engineer-Associate Bootcamp
Reliable Study Data-Engineer-Associate Questions & Exam Data-Engineer-Associate Bootcamp
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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q123-Q128):
NEW QUESTION # 123
A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.
Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low- maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.
Which combination of solutions will meet these requirements? (Select TWO.)
- A. Configure an Amazon Made discovery job for the S3 bucket.
- B. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.
- C. Write custom scripts in an application to mask the PII data and to control access.
- D. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.
- E. Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.
Answer: B,E
Explanation:
To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.
* A. AWS Glue DataBrew for Masking:
* AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.
NEW QUESTION # 124
A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake. Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Run an MSCK REPAIR TABLE command in Athena.
- B. Use the AWS Glue API to manually update the Data Catalog.
- C. Schedule an AWS Glue crawler to periodically update the Data Catalog.
- D. Run a REFRESH TABLE command in Athena.
Answer: C
Explanation:
Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.
NEW QUESTION # 125
A car sales company maintains data about cars that are listed for sale in an are a. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?
- A. Use AWS Glue to process incoming data. Use AWS Lambda and S3 Event Notifications to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
- B. Use an Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Apache Hive for one-time queries and analytical reporting. Use Amazon OpenSearch Service to bulk ingest the data into compute optimized instances. Use OpenSearch Dashboards in OpenSearch Service for the dashboard.
- C. Use AWS Glue to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards in Amazon OpenSearch Service for the dashboard.
- D. Use a provisioned Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
Answer: A
Explanation:
For processing the incoming car listings in a cost-effective, scalable, and automated way, the ideal approach involves using AWS Glue for data processing, AWS Lambda with S3 Event Notifications for orchestration, Amazon Athena for one-time queries and analytical reporting, and Amazon QuickSight for visualization on the dashboard. Let's break this down:
AWS Glue: This is a fully managed ETL (Extract, Transform, Load) service that automatically processes the incoming data files. Glue is serverless and supports diverse data sources, including Amazon S3 and Redshift.
AWS Lambda and S3 Event Notifications: Using Lambda and S3 Event Notifications allows near real-time triggering of processing workflows as soon as new data is uploaded into S3. This approach is event-driven, ensuring that the listings are processed as soon as they are uploaded, reducing the latency for data processing.
Amazon Athena: A serverless, pay-per-query service that allows interactive queries directly against data in S3 using standard SQL. It is ideal for the requirement of one-time queries and analytical reporting without the need for provisioning or managing servers.
Amazon QuickSight: A business intelligence tool that integrates with a wide range of AWS data sources, including Athena, and is used for creating interactive dashboards. It scales well and provides real-time insights for the car listings.
This solution (Option D) is the most cost-effective, because both Glue and Athena are serverless and priced based on usage, reducing costs when compared to provisioning EMR clusters in the other options. Moreover, using Lambda for orchestration is more cost-effective than AWS Step Functions due to its lightweight nature.
Reference:
AWS Glue Documentation
Amazon Athena Documentation
Amazon QuickSight Documentation
S3 Event Notifications and Lambda
NEW QUESTION # 126
A company stores customer records in Amazon S3. The company must not delete or modify the customer record data for 7 years after each record is created. The root user also must not have the ability to delete or modify the data.
A data engineer wants to use S3 Object Lock to secure the data.
Which solution will meet these requirements?
- A. Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.
- B. Place a legal hold on individual objects in the S3 bucket. Set the retention period to 7 years.
- C. Enable governance mode on the S3 bucket. Use a default retention period of 7 years.
- D. Set the retention period for individual objects in the S3 bucket to 7 years.
Answer: A
Explanation:
The company wants to ensure that no customer records are deleted or modified for 7 years, and even the root user should not have the ability to change the data. S3 Object Lock in Compliance Mode is the correct solution for this scenario.
* Option B: Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.In Compliance Mode, even the root user cannot delete or modify locked objects during the retention period. This ensures that the data is protected for the entire 7-year duration as required. Compliance mode is stricter than governance mode and prevents all forms of alteration, even by privileged users.
Option A (Governance Mode) still allows certain privileged users (like the root user) to bypass the lock, which does not meet the company's requirement. Option C (legal hold) and Option D (setting retention per object) do not fully address the requirement to block root user modifications.
References:
* Amazon S3 Object Lock Documentation
NEW QUESTION # 127
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming dat
a. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use the streaming ingestion feature of Amazon Redshift.
- B. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
- C. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.
- D. Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
Answer: A
Explanation:
The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day's data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company. Reference:
Using streaming ingestion with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion
NEW QUESTION # 128
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