Amazon AWS Certified Data Engineer - Associate Amazon-DEA-C01 Dumps in PDF

Free Amazon Amazon-DEA-C01 Real Questions (page: 7)

A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

  1. Configure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe. Write a SQL SELECT statement on the dataframe to query the required column.
  2. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
  3. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
  4. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Answer(s): B

Explanation:

The best solution is
B: Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
Here's why:
Operational Overhead: The core requirement is least operational overhead. S3 Select stands out because it directly queries data within S3, avoiding the need to move or transform the entire dataset. It requires minimal setup.
S3 Select Functionality: S3 Select uses simple SQL expressions to retrieve a subset of data from an object, such as a single column. Since Parquet is a supported format, S3 Select can efficiently parse and query the data.
Cost Efficiency: By only retrieving the necessary column, S3 Select reduces data transfer costs. You only pay for the data scanned and returned.
Alternatives' Drawbacks:
Option A (Lambda + Pandas): This approach involves loading the entire Parquet file into memory within a Lambda function, even though only one column is needed. Pandas provides querying functionality but it's not optimized for large datasets directly in S3. This increases both memory usage and Lambda execution time, leading to higher costs and potential memory limitations. It requires code development and deployment. Option C (Glue DataBrew): Glue DataBrew is suitable for data cleaning and transformation but it's overkill for a simple query, adds operational complexity because you are not transforming anything and you are working with very big data sets. It requires project setup and configuration. Option D (Glue Crawler + Athena): This is an acceptable method, however there is too much overhead. This involves crawling the S3 bucket to create metadata in the Glue Data Catalog and then using Athena to query.
While Athena is also a good option for querying data in S3, the Glue Crawler adds unnecessary operational overhead for a one-time, single-column retrieval.
Direct Querying: S3 Select aligns perfectly with the task's requirement of directly querying data in S3 without requiring additional services for data loading or transformation, ensuring the least operational overhead.
In summary, S3 Select is the simplest, fastest, and most cost-effective way to query a single column from Parquet files in S3 for a one-time task.
Authoritative Links:
Amazon S3 Select: https://docs.aws.amazon.com/AmazonS3/latest/userguide/selecting-content-from-objects.html



A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

  1. Use Apache Airflow to refresh the materialized views.
  2. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
  3. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
  4. Use an AWS Glue workflow to refresh the materialized views.

Answer(s): C

Explanation:

The correct answer is C, using the query editor v2 in Amazon Redshift to refresh the materialized views. Here's why:
The primary goal is to automate refresh schedules for Redshift materialized views with the least effort. Query editor v2 offers a straightforward interface for scheduling queries, including REFRESH MATERIALIZED VIEW .
This requires minimal setup and is directly integrated within the Redshift console. You can simply create a scheduled query to execute the REFRESH MATERIALIZED VIEW command at desired intervals.
Option A, Apache Airflow, introduces significant overhead. Airflow is a powerful orchestration tool, but setting up and managing an Airflow cluster specifically for this single task is an overkill and requires considerably more effort than necessary. This involves defining DAGs, managing dependencies, and maintaining the Airflow infrastructure.
Option B, Lambda UDF, is also less efficient.
While Lambda can execute code, using it within a Redshift UDF to refresh materialized views is complex. It would require setting up proper IAM roles and managing network configurations to allow Redshift to invoke the Lambda function. Furthermore, UDFs have limitations on execution time, which could be problematic for large materialized views.
Option D, AWS Glue workflow, is designed for ETL (Extract, Transform, Load) processes and not specifically for refreshing materialized views.
While Glue could be used to achieve this, it would again involve unnecessary complexity of defining crawlers, transformations, and jobs when a simple scheduled query would suffice. Glue is better suited for more complex data processing tasks, rather than just refreshing a MV.
Therefore, leveraging the built-in scheduling capabilities of Redshift's query editor v2 provides the simplest and most direct approach to automating materialized view refreshes. It requires the least amount of effort, configuration, and management overhead.
Refer to the official Amazon Redshift documentation for more details on scheduled queries:
Amazon Redshift Scheduled Queries REFRESH MATERIALIZED VIEW



A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?

  1. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.
  2. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
  3. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.
  4. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

Answer(s): A

Explanation:

The correct answer is A: Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.
Here's a detailed justification:
AWS Step Functions is a fully managed, serverless orchestration service that allows you to coordinate multiple AWS services into serverless workflows. It's designed for building resilient, auditable, and scalable state machines without managing servers. This aligns perfectly with the requirement of least management overhead.
Option A directly leverages the strengths of Step Functions:
Orchestration: Step Functions can easily orchestrate the execution of the Lambda function and the Glue job in a defined order (Lambda first, then Glue). Integration: Step Functions integrates seamlessly with AWS Lambda and AWS Glue, making it straightforward to call these services from within the state machine definition. Serverless: Step Functions is serverless, meaning you don't need to provision or manage any infrastructure. This minimizes operational overhead. State Management: Step Functions manages the state of the workflow, including error handling and retries. This simplifies the overall pipeline design and improves resilience.
Options B and D involve using Apache Airflow, which, while a powerful orchestration tool, requires more management overhead. You have to manage the Airflow environment itself, whether deployed on EC2 or EKS. This includes maintaining the underlying infrastructure, scaling resources, and handling upgrades. Airflow is a more complex solution than needed for simply orchestrating two tasks.
Option C, using AWS Glue workflows, is specifically designed for orchestrating Glue jobs and crawlers.
While Glue workflows can trigger external actions through triggers, they are primarily focused on Glue's ecosystem and aren't the most efficient way to integrate and orchestrate arbitrary Lambda functions before a Glue job. Step Functions offers more general-purpose orchestration capabilities.
Therefore, Step Functions (Option A) provides the least management overhead because it is a fully managed, serverless orchestration service that integrates directly with the required AWS services (Lambda and Glue). It simplifies the coordination of the pipeline without requiring you to manage any underlying infrastructure.
Relevant Links:
AWS Step Functions: https://aws.amazon.com/step-functions/ AWS Lambda: https://aws.amazon.com/lambda/ AWS Glue: https://aws.amazon.com/glue/



A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3. The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

  1. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
  2. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
  3. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
  4. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Answer(s): B

Explanation:

The correct answer is
B: Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
Here's a detailed justification:
AWS Glue is specifically designed as a fully managed ETL (Extract, Transform, Load) service and a data catalog. Its Data Catalog serves as a central repository for storing metadata about data assets. AWS Glue crawlers are automated programs that connect to data stores (like RDS, Redshift, and S3) to infer the schema, data types, and other metadata information. Crawlers automatically detect changes to the source metadata and update the Glue Data Catalog accordingly, minimizing operational overhead. Scheduling these crawlers allows for regular updates to the catalog, ensuring that the metadata is current.
Option A is less suitable because managing an Aurora database solely as a data catalog adds unnecessary operational overhead. It requires manually building and maintaining the metadata schema, connection logic, and update mechanisms using Lambda functions, which increases complexity.
Option C, using DynamoDB, suffers from similar issues as Option A. While DynamoDB is a powerful NoSQL database, it's not designed specifically for metadata management like Glue's Data Catalog. Building the metadata structure and update processes manually would be complex and time-consuming.
Option D is partially correct in using AWS Glue, but it incorrectly suggests manually extracting schema for RDS and Redshift. AWS Glue crawlers can automatically infer the schema for these data sources as well, simplifying the process. The added step to extract the schema manually creates more operational overhead.
The crawlers also handle detecting changes to the source metadata automatically.
AWS Glue directly addresses the requirements of a data catalog, metadata management, automated discovery of metadata, and change detection with minimal operational overhead. It is purpose-built for this type of task.
Authoritative Links:
AWS Glue Documentation: https://aws.amazon.com/glue/ AWS Glue Data Catalog: https://docs.aws.amazon.com/glue/latest/dg/components-datacatalog.html AWS Glue Crawlers: https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html



A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends. The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

  1. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
  2. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.
  3. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.
  4. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

Answer(s): C

Explanation:

The most cost-effective solution for handling predictable, fluctuating workloads on a DynamoDB table using provisioned capacity mode is C. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times. Schedule lower capacity during off-peak times.
Here's why:
Predictable Workload: The problem explicitly states the workload has predictable throughput load on a regular schedule (Monday morning peaks, weekend lulls). This makes scheduled capacity adjustments ideal.
Cost Efficiency: Application Auto Scaling allows you to define scaling schedules. You can automatically increase read and write capacity units (RCUs/WCUs) before the Monday morning peak and decrease them during weekends when usage is low. This ensures you only pay for the capacity you need, minimizing costs.
Consistent Performance: By pre-emptively scaling up capacity before the peak, the application maintains consistent performance without throttling. Scaling down during off-peak times prevents over-provisioning and reduces unnecessary costs.
Why not A? Simply increasing provisioned capacity to the maximum peak requirement and leaving it there would be the least cost-effective. You would be paying for unused capacity during off-peak times.
Why not B? Dividing the table would add unnecessary complexity in managing and querying data. It's also not guaranteed to provide better performance without proper sharding and load balancing strategies, which would add more complexity. Spreading queries evenly doesn't address the fundamental problem of insufficient total capacity during the peak.
Why not D? Switching to on-demand capacity mode would incur a different cost structure.
While convenient for unpredictable workloads, it's generally more expensive for predictable workloads where you can accurately provision capacity. Furthermore, the question emphasizes cost-effectiveness, and scheduled scaling in provisioned mode directly addresses that. Auto scaling within provisioned mode can be implemented but would not be as effective as scheduled scaling because the response to the load would take some time.
Supporting Concepts:
Provisioned Capacity Mode: DynamoDB's provisioned capacity mode allows you to specify the expected read and write throughput for your table. You are charged based on these provisioned units. AWS Application Auto Scaling: This service enables you to automatically scale your DynamoDB table capacity based on utilization metrics or, in this case, a schedule. Scheduled Scaling: A feature of Application Auto Scaling that allows you to set up scaling actions to occur at specific times.
Authoritative Links:
DynamoDB Provisioned Capacity Mode AWS Application Auto Scaling Scaling DynamoDB Tables Automatically with AWS Application Auto Scaling



A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution. The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?

  1. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.
  2. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company's data catalog as an external data catalog.
  3. Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company's data catalog.
  4. Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company's data catalog.

Answer(s): B

Explanation:

The correct answer is B because it offers a cost-effective and serverless approach to migrating the Hive metastore. Here's why:
Serverless Solution: AWS Glue Data Catalog is a fully managed, serverless service. This eliminates the need to manage infrastructure for the data catalog, reducing operational overhead and cost. Metastore Migration: Configuring a Hive metastore in Amazon EMR provides a temporary environment for the on-premises metastore to be migrated. This is a necessary step to transition the data catalog to the cloud. External Data Catalog: Configuring the Hive metastore in EMR to use AWS Glue Data Catalog as an external metastore makes the Glue Data Catalog the persistent, central repository for the data catalog after migration. This also avoids the need to retain the EMR metastore. Cost-Effectiveness: This approach is cost-effective because AWS Glue's pricing is based on usage (storage and metadata operations), which scales with the company's needs. Avoids the cost of more complex solutions like DMS or persistent database infrastructure for Aurora.
Here's why the other options are less ideal:
A: Migrating directly to S3 using DMS and then scanning with Glue is unusual and likely more complex than setting up a temporary Hive metastore. C: Using Amazon Aurora MySQL is viable, but over-engineered and more costly than AWS Glue Data Catalog, which is specifically designed for this purpose. Also using the term external is misleading as Aurora would be the main metastore in this scenario. D: Using the EMR Hive metastore directly is not a persistent, serverless solution, as it relies on the EMR cluster's lifecycle. You would have to keep your EMR cluster running indefinitely.
Authoritative Links:
AWS Glue Data Catalog: https://aws.amazon.com/glue/ Amazon EMR: https://aws.amazon.com/emr/



A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution. A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations. The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?

  1. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
  2. Change the distribution key to the table column that has the largest dimension.
  3. Upgrade the reserved node from ra3.4xlarge to ra3.16xlarge.
  4. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Answer(s): B

Explanation:

The best solution is B. Change the distribution key to the table column that has the largest dimension.
Here's a detailed justification:
The problem describes a situation where one node in a Redshift cluster is heavily overloaded while the others are relatively idle. This strongly indicates data skewness – an uneven distribution of data across the compute nodes.
When data is skewed, queries involving the skewed data will primarily execute on the node holding the majority of that data, leading to high CPU utilization on that node and query queuing.
The distribution key determines how data is distributed across the nodes. If the distribution key is poorly chosen, it can lead to skew. Choosing a column with a large dimension (high cardinality, many unique values) as the distribution key generally promotes a more even distribution of data. Redshift will attempt to distribute rows as evenly as possible based on the distribution key's values.
When the column has a wide range of values, this tends to result in a better spread across the nodes. This strategy is effective in balancing the workload without altering the cluster size.
Option A (changing the sort key) addresses query performance by influencing the order in which data is stored within each node. It does not directly impact how data is distributed across nodes and therefore won't solve the load imbalance issue.
While a proper sort key improves query execution speed within a node, it doesn't address the root cause of one node being overloaded.
Option C (upgrading the node size) could temporarily alleviate the problem by providing more CPU resources to the overloaded node. However, it doesn't address the underlying data skewness issue. The problem will likely reappear as data volumes grow. Upgrading is also a more expensive solution than simply adjusting the distribution key. Furthermore, the question specifically asks for a solution that maintains the current number of compute nodes.
Option D (changing the primary key) is incorrect because Redshift does not enforce primary keys as constraints.
While defining a primary key can aid query optimization and documentation, it has no impact on data distribution or query execution from a data skew perspective. Redshift's query optimizer can leverage primary key information, but changing the definition does not change the distribution of data across nodes.
Therefore, changing the distribution key to a high-cardinality column is the most effective way to redistribute the data more evenly across the nodes, balancing the workload and reducing the CPU load on the overloaded node without increasing the cluster size. This improves overall query performance and cluster efficiency.
Further reading:
Amazon Redshift documentation on distribution keys AWS Big Data Blog: Choosing the right distribution style for Amazon Redshift



A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?

  1. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
  2. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.
  3. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.
  4. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

Answer(s): A

Explanation:

The best solution is A because it leverages AWS Glue for cost-effective data cataloging and schema management while providing a modern serverless data warehousing option. AWS Glue Data Catalog provides a central metadata repository for data sources, making data discoverable and accessible. The AWS Glue
Schema Registry tackles the evolving data structure challenge by automatically detecting and managing schema changes, which is crucial for handling data from upgraded IoT devices. Amazon Redshift Serverless allows running analytics queries without managing infrastructure, providing a cost-effective solution compared to provisioned clusters (option B).
Option B involves creating a provisioned Redshift cluster and Redshift Spectrum, which is more expensive than a serverless solution, especially when resource utilization is sporadic or unpredictable. It also necessitates manual maintenance of the Redshift cluster.
Option C relies on Amazon Athena for data exploration but doesn't offer a robust data ingestion and transformation pipeline into a dedicated data warehouse. Athena's primary use case is direct querying of data in S3, not building an optimized analytics platform.
Option D, while using Glue Data Catalog and Schema Registry, employs Lambda UDFs and Step Functions for data ingestion into Redshift Serverless. This introduces unnecessary complexity and operational overhead compared to directly using AWS Glue's ETL capabilities. Glue's ETL can handle data ingestion, transformation, and loading into Redshift Serverless seamlessly.
Therefore, option A offers the most cost-effective and efficient solution by combining the data cataloging capabilities of AWS Glue with the serverless data warehousing power of Redshift Serverless, effectively addressing the requirements while minimizing cost and operational complexity.
Supporting Links:
AWS Glue Data Catalog: https://aws.amazon.com/glue/features/ AWS Glue Schema Registry: https://aws.amazon.com/blogs/big-data/using-the-aws-glue-schema-registry-to-centrally-manage-schemas-and-improve-data-quality/ Amazon Redshift Serverless: https://aws.amazon.com/redshift/serverless/



Share your comments for Amazon Amazon-DEA-C01 exam with other users:

A
AI Tutor Explanation
5/15/2026 9:49:16 AM

Question 5:
I can’t see the [Image] in Question 5, but I can explain the likely reasoning.

  • Correct components: SAS adapter and disk (options B and D).
  • Why: This question is about diagnosing a storage-path issue inside the node. The SAS adapter connects disks to the controller, and the disks themselves are where I/O problems or failures usually originate. The cluster switches and network interface cards are more related to the network path rather than the direct storage path, unless the symptom points to a network fault.

How to examine these two components:
  • SAS adapter
- Check link status and port mapping. - Verify firmware version and compatibility. - Inspect cabling to disk shelves and any expanders. - Look for adapter errors in system logs.
  • Disk
- Check health status for each disk (fail/degraded, SMART data). - Inspect LEDs on the disk and shelf. - Review reallocation, pending operations, and overall disk state with storage commands/logs. - Confirm hot spares and disk replacement readiness.
If you want, I can walk through the exact commands you’d use in ONTAP or a CLI.

A
AI Tutor Explanation
5/14/2026 11:59:47 AM

Question 12:
Here’s why Question 12’s correct choices are C and D.

  • C (Azure DevOps, build and upload to asset library)
- What it means: Create a deployable package from a branch in Azure DevOps, then use an LCS asset upload step to push that package into the Dynamics 365 F&O asset library. - Why it’s valid: This is a standard path to prepare and publish a deployable package to LCS for deployment.
  • D (Visual Studio, create deployment package and upload)
- What it means: Use Visual Studio to generate a Dynamics 365 deployment package, then upload that package to the LCS asset library. - Why it’s valid: Visual Studio can produce the deployable package, which is what LCS expects in the asset library.
Why A and B are not correct:
  • A: Visual Studio export the project and upload to asset library
- You don’t export a project to the asset library; you export a deployable package and upload that.
  • B: Azure DevOps, queue a build and upload the model to the asset library
- The asset library stores deployable packages, not models. You upload a package, not a model.

A
AI Tutor Explanation
5/14/2026 11:49:11 AM

Question 3:
Question 3 asks for two valid ways to meet the purchase order creation validation (warn if the vendor is on the exclusion list for the customer/product and block/alert accordingly).
Correct answers: C and D

  • C: In Application Explorer, create a form extension and implement validation.
- Extend the Purchase Order creation form and add validation logic (e.g., before save/submit) to check VendExclusions for the customer/item and show a warning or block the PO as needed.
  • D: Implement Chain of Command (CoC) and method wrapping by creating a form extension class.
- Use CoC to wrap the target PO creation method, perform the exclusion check after calling super(), and enforce the rule (warning or prevent creation).
Why not A or B?
  • A (class with a form data source event handler) is not the standard pattern for this UI-level validation scenario and is less direct for enforcing creation-time behavior.
  • B (table extension with validation) cannot sufficiently enforce the UI-level workflow or trigger user-facing warnings during PO creation.

So, the two valid techniques are C and D, reflecting the recommended form-extension patterns: direct form validation and CoC-based method wrapping.

A
AI Tutor Explanation
5/14/2026 11:42:28 AM

Question 12:
Here’s how to understand question 12.

  • What the question asks: Two valid ways to prepare and deploy a software deployable package to a test environment (via the asset library in LCS).

  • The correct options: C and D.

- C: In Azure DevOps, queue a build from the corresponding branch to produce a deployable package, then upload that package to the LCS asset library. This uses a release/build workflow and requires an LCS connection set up in Azure DevOps.
- D: In Visual Studio, create a Dynamics 365 deployment package using the dev tools, then upload that deployable package to the LCS asset library. This is the Visual Studio–based path to generate and publish a deployable package.
  • Why A and B are not correct:

- A: Exporting the project from Visual Studio and uploading the project itself to the asset library isn’t the correct artifact; the asset library expects a deployable package, not a raw project export.
- B: Queuing a build and “uploading the model” to the asset library isn’t the standard deployable package workflow; the target artifact should be a deployable package, not a model file.
Key concept: Deployable packages are published to the LCS Asset Library, and you can create them either from Visual Studio or from Azure DevOps as part of a build/release pipeline.

A
AI Tutor Explanation
5/13/2026 11:23:34 PM

Question 6:
Here’s how question 6 works.
Key constraint: All new and extended objects must be in an existing model named FinanceExt. Creating a brand-new model is not allowed.
Why the two correct options work:

  • B. Overlay the existing FinanceExt model and populate the solution definition.
- This keeps all changes inside FinanceExt by overlaying the base model with your solution details.
  • C. Extend the existing FinanceExt model and populate the project model definition.
- This uses the extension approach to add your changes while still residing in FinanceExt, organized via the project model definition.
Why the other options are not correct:
  • A. Create a new model that extends the existing FinanceExt model.
- Not allowed, since no new models may be created.
  • D. Modify DefaultModelForNewProject and name the model FinanceExt.
- The goal is to place changes in FinanceExt, not to name or create a new model via config; this doesn’t describe a valid way to satisfy the constraint.
In short: use B or C; both keep changes inside the existing FinanceExt model.

A
AI Tutor Explanation
5/13/2026 11:00:29 PM

Question 2:
I don’t have the text for Question 2 here. Please paste the exact Question 2 (including all answer choices) or describe the topic it covers.
Once I have it, I’ll:

  • Identify the exam objective it targets.
  • Break down the stem and each option.
  • Use elimination and key concepts to justify why options are correct or incorrect.
  • Give the final answer with a concise explanation of the reasoning.

If there’s an image in Question 2, describe it or transcribe any text shown.

A
AI Tutor Explanation
5/10/2026 12:51:57 AM

Which statement is true about using default environment variables? The environment variables can be read in workflows using the ENV: variable_name syntax. The environment variables created should be prefixed with GITHUB_ to ensure they can be accessed in workflows The environment variables can be set in the defaults: sections of the workflow The GITHUB_WORKSPACE environment variable should be used to access files from within the runner.
Correct answer: The statement "The GITHUB_WORKSPACE environment variable should be used to access files from within the runner." is true.
Why the others are false:

  • ENV: variable_name syntax is not how you read env vars in workflows. Use ${{ env.VARIABLE }} in expressions or $VARIABLE in a shell.
  • Environment variables don’t need a GITHUB_ prefix. Custom vars can have any name; GITHUB_ is only for built-in, predefined vars.
  • defaults: cannot set environment variables. It configures defaults for run steps (like shell or working-directory), not env vars.

Extra tip:
  • GITHUB_WORKSPACE (or ${{ github.workspace }}) is the root path on the runner where the repository is checked out. Use it to access files, e.g., $GITHUB_WORKSPACE/... or ${{ github.workspace }}/....

A
AI Tutor Explanation
5/7/2026 11:34:48 PM

As an administrator for this subscription, you have been tasked with recommending a solution that prohibits users from copying corporate information from managed applications installed on unmanaged devices. Which of the following should you recommend? Windows Virtual Desktop. Microsoft Intune. Windows AutoPilot. Azure AD Application Proxy.

  • Microsoft Intune

Reason: Intune can enforce app protection policies (MAM) on managed apps, even on unmanaged devices, to prevent data leakage (e.g., restricting copy/paste, saving to non-approved apps, etc.). The other options don’t address data protection within apps on unmanaged devices.

A
AI Tutor Explanation
5/4/2026 2:43:10 AM

Question 34:

  • Question 34 asks: Which role(s) has the capability to create Policies? Choose two.
  • Likely correct roles: Compliance Manager and Compliance admin (A and B). These roles typically have policy governance rights to create and manage policies.
  • Why not the others: Compliance User is usually a limited user, not allowed to create policies. Risk Manager focuses on risks, not policy creation in standard setups.
  • Note on the provided key: The answer shown is “C” (Compliance User), which conflicts with “Choose two.” This is probably a key error. In practice, verify against your instance’s ACLs or official docs.
  • Quick check tip: In ServiceNow, review the ACLs and role permissions for the Policy records to see which roles can create policies.

A
AI Tutor Explanation
4/29/2026 11:59:58 PM

function of appnav in sdwan

  • AppNav (Application Navigation) is the Cisco SD-WAN feature that provides application-level visibility and control.
  • It maintains a catalog of applications (AppIDs) with attributes (ports/protocols, categories) and classifies traffic accordingly.
  • It allows policy authors to reference apps by name in policies, enabling application-aware routing, QoS, and service chaining based on the app’s requirements.
  • The SD-WAN fabric uses AppNav data to steer traffic along the best path, improving user experience for critical apps while optimizing WAN usage.

A
AI Tutor Explanation
4/29/2026 5:25:25 AM

Question 1:

  • Correct answer: C. Extract the hardware ID information of each computer to a CSV file and upload the file from the Microsoft Intune admin center.

  • Why this is correct

A
AI Tutor Explanation
4/29/2026 5:23:14 AM

Question 5:

  • Correct answer: A. User4 and User1 only

  • Why this is correct:
- The Automatic Enrollment setting in Intune has MDM user scope: GroupA. Only users in GroupA can enroll devices via MDM auto-enrollment. - Device6 will be enrolled via Windows Autopilot and Intune, so enrollment is allowed only for users in GroupA. - Based on the group memberships in the scenario, User4 and User1 are in GroupA, while User2 and User3 are not. Therefore only User4 and User1 can enroll Device6.
  • Quick tip for the exam:
- Remember: MDM user scope determines who can auto-enroll devices; MAM scope controls app protection enrollment. When a new Autopilot device is enrolled, the signing-in user must be in the MDM scope.

A
AI Tutor Explanation
4/29/2026 5:17:10 AM

Why this is correct

  • Correct answer: C. Extract the hardware ID information of each computer to a CSV file and upload the file from the Microsoft Intune admin center.

  • Why this is correct:
- Windows Autopilot requires devices to be registered by their hardware IDs (hash) before Autopilot can deploy Windows 10 Enterprise. - Collect the hardware IDs from the new Phoenix machines, save them in a CSV, and upload that CSV in the Intune/Windows Autopilot area. This maps each device to an Autopilot deployment profile. - After registration, you can assign Autopilot profiles (Windows 10 Enterprise, etc.). Other options (serial number CSV, generalizing, or Mobility settings) are not the initial Autopilot registration steps.

A
AI Tutor Explanation
4/25/2026 1:53:46 PM

Question 7:

  • Correct answer: B — A risk score is computed based on the number of remediations needed compared to the industry peer average.

Explanation:
  • Risk360 uses a remediation-based score. It benchmarks how many actions are required to fix issues against peers, giving a relative risk posture.
  • Why not the others:
- A: Not just total risk events by location. - C: Time to mitigate isn’t the primary scoring method. - D: Not a four-stage breach scoring approach.
Note: The page text shows a mismatch (it lists D as the answer), but the study guide describes the remediation-based scoring (B) as the correct concept.

A
AI Tutor Explanation
4/25/2026 1:42:20 PM

Question 104:

  • Correct answer: D) Multi-Terabyte (TB) Range

  • Brief explanation:
- clustering keys organize data into micro-partitions to improve pruning when queries filter on those columns. - The performance benefit is most significant for very large tables; for small tables the overhead of maintaining clustering outweighs gains. - Therefore, as a best practice, define clustering keys on tables at the TB scale.

C
Community Helper
4/25/2026 2:03:10 AM

Q23: Fabric Admin is correct. Because Domain admin cannot create domains. Only Fabric Admin can among the given options. Q51: Wrapping @pipeline.parameter.param1 inside {} will return a string. But question requires the expression to return Int, so correct answer should be @pipeline.parameter.param1 (no {})

A
AI Tutor Explanation
4/23/2026 3:07:03 PM

Question 62:

  • Correct answer: D (per the page)

  • Note: The explanation text on the page describes option B (use ZDX score and Analyze Score to trigger the Y Engine analysis), indicating a mismatch between the stated answer and the rationale.

  • Key concept: For fast root-cause analysis, leverage telemetry and auto-correlated insights:
- Use the user’s ZDX score for AWS and run Analyze Score to activate the Y Engine, which correlates metrics across network, client, and application to pinpoint the issue quickly.
  • Why the other options are less effective:
- A: Only checks for outages; doesn’t provide actionable root-cause analysis. - C: Deep Trace helps visibility but is manual and time-consuming. - D: Packet capture is invasive and slow; not the quickest path to root cause.

A
AI Tutor Explanation
4/23/2026 12:26:21 PM

Question 32:

  • Answer: A (2.4GHz)

  • Why: Lower-frequency signals have longer wavelengths and experience less attenuation when passing through walls and obstacles. Higher frequencies (5GHz, 6GHz) are more easily blocked by walls. NFC operates over very short distances and is not meant to penetrate walls. So 2.4 GHz best penetrates physical objects like walls.

A
AI Tutor Explanation
4/21/2026 8:48:36 AM

Question 3:

  • False is the correct answer (Option B).

Why:
  • In Snowflake, a database is a metadata object that exists within a single Snowflake account. Accounts are isolated—there isn’t one database that lives in multiple accounts.
  • You can access data across accounts via data sharing or database replication, but these create separate database objects in the other accounts (e.g., a database in the consumer account created from a share), not a single shared database across accounts.

So a single database cannot exist in more than one Snowflake account.

A
Anonymous User
4/16/2026 10:54:18 AM

Question 1:

  • Correct answer: E — date = sys.argv[1]
  • Why this is correct:
- When a Databricks Job passes parameters to a notebook, those parameters are supplied to the notebook's Python process as command-line arguments. The first argument after the script name is sys.argv[1], so date = sys.argv[1] captures the passed date value directly.
  • How it compares to other options:
- date = spark.conf.get("date") reads from Spark config, not from job parameters. - input() waits for user input at runtime, which isn’t how job parameters are provided. - date = dbutils.notebooks.getParam("date") would work if the notebook were invoked via dbutils.notebook.run with parameters, not

A
Anonymous User
4/15/2026 4:42:07 AM

Question 528:

  • Correct answer: NSG flow logs for NSG1 (Option B)

  • Why:
- Traffic Analytics uses NSG flow logs to analyze traffic patterns. You must have NSG flow logs enabled for the NSGs you want to monitor. - An Azure Log Analytics workspace is also required to store and query the traffic data. - Network Watcher must be available in the subscription for traffic analytics to function.
  • What to configure (brief steps):
- Ensure Network Watcher is enabled in the East US region (for the subscription/region). - Enable NSG flow logs on NSG1. - Ensure a Log Analytics workspace exists and is accessible (read/write) so Traffic Analytics can store and query logs.
  • Why other options aren’t correct:
- “Diagnostic settings for VM1” or “Diagnostic settings for NSG1” alone don’t guarantee flow logs are captured and sent to Log Analytics, which Traffic Analytics relies on. - “Insights for VM1” is not how Traffic Analytics collects traffic data.

A
Anonymous User
4/15/2026 2:43:53 AM

Question 23:
The correct answer is Domain admin (option B), not Fabric admin.

  • Domain admin provides domain-level management: create domains/subdomains and assign workspaces within those domains, which matches the tasks while following least privilege.
  • Fabric admin is global-level access and is more privileges than needed for this scenario (it would grant broader control across the Fabric environment).

A
Anonymous User
4/14/2026 12:31:34 PM

Question 2:
For question 2, the key concept is the Longest Prefix Match. Routers pick the route whose subnet mask is the most specific (largest prefix length) that still matches the destination IP.
From the options:

  • A) 10.10.10.0/28 ? 10.10.10.0–10.10.10.15
  • B) 10.10.13.0/25 ? 10.10.13.0–10.10.13.127
  • C) 10.10.13.144/28 ? 10.10.13.144–10.10.13.159
  • D) 10.10.13.208/29 ? 10.10.13.208–10.10.13.215

The destination Host A’s IP must fall within 10.10.13.208–10.10.13.215 for the /29 to be the best match. Since /29 is the longest prefix among the matching options, Router1 will use 10.10.13.208/29.
Thus, the correct answer is D.

S
srameh
4/14/2026 10:09:29 AM

Question 3:

  • Correct answer: Phase 4, Post Accreditation

  • Explanation:
- In DITSCAP, the four phases are: - Phase 1: Definition (concept and requirements) - Phase 2: Verification (design and testing) - Phase 3: Validation (fielding and evaluation) - Phase 4: Post Accreditation (ongoing operations and lifecycle management) - The description—continuing operation of an accredited IT system and addressing changing threats throughout its life cycle—fits the Post Accreditation phase, which covers operations, maintenance, monitoring, and reauthorization as threats and environment evolve.

O
onibokun10
4/13/2026 7:50:14 PM

Question 129:
Correct answer: CNAME

  • A CNAME record creates an alias for a domain, so newapplication.comptia.org will resolve to whatever IP address www.comptia.org resolves to. This ensures both names point to the same resource without duplicating the IP.
  • Why not the others:
- SOA defines authoritative information for a zone. - MX specifies mail exchange servers. - NS designates name servers for a zone.
  • Notes: The alias name (newapplication.comptia.org) should not have other records if you use a CNAME for it, and CNAMEs aren’t used for the zone apex (root) domain. This scenario uses a subdomain, so a CNAME is appropriate.

A
Anonymous User
4/13/2026 6:29:58 PM

Question 1:

  • Correct answer: C

  • Why this is best:
- Uses OS Login with IAM, so SSH access is granted via Google accounts rather than distributing per-user SSH keys. - Granting the compute.osAdminLogin role to a Google group gives admin access to all team members in a centralized, auditable way. - Access is auditable: Cloud Audit Logs show who accessed which VM, satisfying the security requirement to determine who accessed a given instance.
  • How it works:
- Enable OS Login on the project/instances (enable-oslogin metadata). - Add the team’s

A
Anonymous User
4/13/2026 1:00:51 PM

Question 2:

  • Answer: D. Azure Advisor

  • Why: To view security-related recommendations for resources in the Compute and Apps area (including App Service Web Apps and Functions), you use Azure Advisor. Advisor surfaces personalized best-practice recommendations across resources, including security, and shows which resources are affected and the severity.

  • Why not the others:
- Azure Log Analytics is for ad-hoc querying of telemetry, not for viewing security recommendations. - Azure Event Hubs is for streaming telemetry data, not for security recommendations.
  • Quick tip: In the portal, navigate to Azure Advisor and check the Security recommendations for App Services to see actionable items and affe

D
Don
4/11/2026 5:36:42 AM

Recommend using AI for Solutions rather the Answer(s) submitted here

M
Mogae Malapela
4/8/2026 6:37:56 AM

This is very interesting

A
Anon
4/6/2026 5:22:54 PM

Are these the same questions you have to pay for in ExamTopics?

L
LRK
3/22/2026 2:38:08 PM

For Question 7 - while the answer description indicates the correct answer, the option no. mentioned is incorrect. Nice and Comprehensive. Thankyou

R
Rian
3/19/2026 9:12:10 AM

This is very good and accurate. Explanation is very helpful even thou some are not 100% right but good enough to pass.

G
Gerrard
3/18/2026 6:58:37 AM

The DP-900 exam can be tricky if you aren't familiar with Microsoft’s specific cloud terminology. I used the practice questions from free-braindumps.com and found them incredibly helpful. The site breaks down core data concepts and Azure services in a way that actually mirrors the real test. As a resutl I passed my exam.

V
Vineet Kumar
3/6/2026 5:26:16 AM

interesting

AI Tutor 👋 I’m here to help!