[Sep 13, 2026] DEA-C01 Ultimate Study Guide - TestValid
Ultimate Guide to Prepare DEA-C01 Certification Exam for SnowPro Advanced in 2026
NEW QUESTION # 84
A marketing company collects clickstream data. The company sends the clickstream data to Amazon Kinesis Data Firehose and stores the clickstream data in Amazon S3. The company wants to build a series of dashboards that hundreds of users from multiple departments will use.
The company will use Amazon QuickSight to develop the dashboards. The company wants a solution that can scale and provide daily updates about clickstream activity.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)
- A. Use Amazon S3 analytics to query the clickstream data.
- B. Use Amazon Athena to query the clickstream data
- C. Access the query data through a QuickSight direct SQL query.
- D. Access the query data through QuickSight SPICE (Super-fast, Parallel, In-memory Calculation Engine). Configure a daily refresh for the dataset.
- E. Use Amazon Redshift to store and query the clickstream data.
Answer: B,D
Explanation:
Athena would be cheaper than Redshift. S3 analytics is irrelevant. The functionality in SPICE should be more cost effective than direct SQL by reducing the frequency and volume of queries.
NEW QUESTION # 85
In Which Data Modelling Technique, Data Engineer generally refer the terms Hubs & Satellites?
- A. Star Schema
- B. Data Hub
- C. Snowflake Schema
- D. Data Vault
Answer: D
Explanation:
Explanation
In Data Vault modelling, Hubs are entities of interest to the business.
They contain just a distinct list of business keys and metadata about when each key was first loaded and from where.
In Data Vault modelling, Satellites connect to Hubs or Links. They are Point in Time: so we can ask and answer the question, "what did we know when?" Satellites contain data about their parent Hub or Link and metadata about when the data was load-ed, from where, and a business effectivity date.
NEW QUESTION # 86
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?
- A. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.
- B. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.
- C. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.
- D. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output.
Answer: D
Explanation:
https://docs.aws.amazon.com/glue/latest/dg/tutorial-add-crawler.html
NEW QUESTION # 87
Which one is not the Core benefits of micro-partitioning
- A. Micro-partitions can overlap in their range of values, helps data skewing.
- B. Columns are stored independently within micro-partitions, often referred to as colum-nar storage.
- C. Snowflake micro-partitions are derived automatically they do not need to be explicitly defined up-front or maintained by users.
- D. Columns are also compressed individually within micro-partitions.
- E. Enables extremely efficient DML and fine-grained pruning for faster queries.
Answer: A
Explanation:
Explanation
The benefits of Snowflake's approach to partitioning table data include:
In contrast to traditional static partitioning, Snowflake micro-partitions are derived automatically; they don't need to be explicitly defined up-front or maintained by users.
As the name suggests, micro-partitions are small in size (50 to 500 MB, before compression), which enables extremely efficient DML and fine-grained pruning for faster queries.
Micro-partitions can overlap in their range of values, which, combined with their uniformly small size, helps prevent skew.
Columns are stored independently within micro-partitions, often referred to as columnar storage. This enables efficient scanning of individual columns; only the columns referenced by a query are scanned.
Columns are also compressed individually within micro-partitions. Snowflake automatically de-termines the most efficient compression algorithm for the columns in each micro-partition.
NEW QUESTION # 88
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations, The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account.
The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.
Which combination of steps will meet this requirement? (Choose two.)
- A. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.
- B. Add the KMS key as a resource that the QuickSight service role can access.
- C. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
- D. Add the S3 bucket as a resource that the QuickSight service role can access.
- E. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the BI-Account account.
Answer: B
NEW QUESTION # 89
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?
- A. Update the MSK broker instance to a larger instance type. Restart the MSK cluster.
- B. Expand the storage of the Apache ZooKeeper nodes.
- C. Expand the storage of the MSK broker. Configure the MSK cluster storage to expand automatically.
- D. Specify the Target Volume-in-GiB parameter for the existing topic.
Answer: C
Explanation:
The RootDiskUsed metric in Amazon MSK indicates that the storage space on the broker's disk is filling up. To resolve this, the company can expand the storage for the MSK brokers to prevent them from running out of disk space.
Amazon MSK supports automatic storage scaling, which can be configured to avoid similar issues in the future. By enabling auto-scaling, MSK will automatically expand the storage when needed, ensuring that the brokers have enough space to handle new topics or additional data.
ZooKeeper is used for managing the state of Kafka brokers, but the RootDiskUsed metric pertains to the Kafka brokers, not the ZooKeeper nodes.
Expanding ZooKeeper storage will not resolve the issue.
While increasing the instance size may improve performance, it does not address the issue of running out of disk space on the broker's root volume, which is causing the CloudWatch alarm.
Kafka topics do not have a direct volume specification. Storage for Kafka is managed at the broker level, not at the individual topic level. Therefore, this option does not apply to the issue of disk usage.
NEW QUESTION # 90
A company uses an AWS Lambda function to transfer files from a legacy SFTP environment to Amazon S3 buckets. The Lambda function is VPC enabled to ensure that all communications between the Lambda function and other AVS services that are in the same VPC environment will occur over a secure network.
The Lambda function is able to connect to the SFTP environment successfully. However, when the Lambda function attempts to upload files to the S3 buckets, the Lambda function returns timeout errors. A data engineer must resolve the timeout issues in a secure way.
Which solution will meet these requirements in the MOST cost-effective way?
- A. Create a VPC gateway endpoint for Amazon S3. Route network traffic to the VPC gateway endpoint.
- B. Create a VPC interface endpoint for Amazon S3. Route network traffic to the VPC interface endpoint.
- C. Use a VPC internet gateway to connect to the internet. Route network traffic to the VPC internet gateway.
- D. Create a NAT gateway in the public subnet of the VPC. Route network traffic to the NAT gateway.
Answer: A
Explanation:
While interface endpoints is a viable solution, it can be more complex and expensive compared to a gateway endpoint. VPC interface endpoints charge per hour and per gigabyte of data transferred.
NEW QUESTION # 91
A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, "No space left on device." The data engineer needs to identify the source of the error and provide a solution.
Which combinations of steps will meet this requirement MOST cost-effectively? (Choose two.)
- A. Enable the --write-shuffie-files-to-s3 job parameter. Use the salting technique.
- B. Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.
- C. Scale out the workers vertically to address data skewness.
- D. Use error logs in Amazon CloudWatch to monitor data skew.
- E. Scale out the number of workers horizontally to address data skewness.
Answer: A,B
Explanation:
Use the Spark UI and AWS Glueexposed Spark metrics to pinpoint where partitions are disproportionately large (data skew) and where spill files are filling executor disk.
Enable the --write-shuffle-files-to-s3 job parameter so shuffle spills go to S3 instead of running out of local disk, and apply a salting technique on skewed keys to spread data more evenly across partitions.
NEW QUESTION # 92
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?
- A. Change the data format from .csv to Apache Parquet. Apply Snappy compression.
- B. Compress the .csv files by using gzip compression.
- C. Change the data format from .csv to JSON format. Apply Snappy compression.
- D. Compress the .csv files by using Snappy compression.
Answer: A
Explanation:
Apache Parquet is a columnar storage format optimized for analytical queries. It is highly efficient for query performance, especially when queries involve selecting specific columns, as it allows for column pruning and predicate pushdown optimizations.
NEW QUESTION # 93
Which are the two ways to access elements in a JSON object?
- A. Use SemiColon notation to traverse a path in a JSON object:
<col-umn>:<level1_element>;<level2_element>;<level3_element>. - B. use bracket notation to traverse the path in an object:
<col-umn>['<level1_element>']['<level2_element>']. - C. use Curly bracket notation to traverse the path in an object:
<col-umn>{'<level1_element>'}{'<level2_element>'}. - D. Use dot notation to traverse a path in a JSON object:
<col-umn>:<level1_element>.<level2_element>.<level3_element>.
Answer: B,D
NEW QUESTION # 94
Which Scenario Data engineer decide Materialized views are not useful. Select All that apply.
- A. The view's base table change frequently.
- B. Query results contain results that require significant processing.
- C. Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).
- D. The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
Answer: A
Explanation:
Explanation
A materialized view is a pre-computed data set derived from a query specification (the SELECT in the view definition) and stored for later use. Because the data is pre-computed, querying a material-ized view is faster than executing a query against the base table of the view. This performance dif-ferencecan be significant when a query is run frequently or is sufficiently complex. As a result, ma-terialized views can speed up expensive aggregation, projection, and selection operations, especially those that run frequently and that run on large data sets.
Materialized views require Enterprise Edition.
Materialized views are designed to improve query performance for workloads composed of com-mon, repeated query patterns. However, materializing intermediate results incurs additional costs. As such, before creating any materialized views, you should consider whether the costs are offset by the savings from re-using these results frequently enough.
Materialized views are particularly useful when:
Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).
Query results contain results that require significant processing, including:
1. Analysis of semi-structured data.
2. Aggregates that take a long time to calculate.
The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
The view's base table does not change frequently.
NEW QUESTION # 95
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?
- A. Transition the data from Amazon S3 to an Amazon Aurora cluster.
- B. Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.
- C. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.
- D. Transition the data to Amazon S3 Glacier Flexible Retrieval.
Answer: C
Explanation:
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.
NEW QUESTION # 96
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AWS Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Daily.csv in a second S3 bucket.
Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day's CSV file.
A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid.
Which solution will meet these requirements with the LEAST effort?
- A. Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required fields and empty files.
- B. Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.
- C. Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.
- D. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
Answer: A
Explanation:
AWS Glue Data Quality rules can be integrated directly into the AWS Glue ETL pipeline to automatically check for missing or invalid data in the daily file. You can use Data Quality Definition Language (DQDL) to define rules that verify the completeness and validity of the data before the pipeline writes the output. This solution ensures that the previous day's file is only overwritten if the new data is complete and valid, minimizing manual intervention and operational effort.
While Lambda could be used, it requires additional development and integration outside of the existing Glue ETL pipeline. This adds complexity and operational overhead.
The "Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field" approach assumes that filling missing values with the most common ones is acceptable, but it might not always be the correct solution for all cases. The requirement specifies that the file should be validated and replaced only if it's complete and valid.
Using Athena to drop missing rows doesn't fully address the problem, as it could result in loss of data and still requires manual intervention to validate the file before copying it. Additionally, it introduces unnecessary steps compared to using built-in AWS Glue Data Quality capabilities.
NEW QUESTION # 97
A Data Engineer ran a stored procedure containing various transactions During the execution, the session abruptly disconnected preventing one transactionfrom committing or rolling hark.The transaction was left in a detached state and created a lock on resources
...must the Engineer take to immediately run a new transaction?
- A. Call the system function SYSTEM$ABORT_TRANSACTION.
- B. Set the LOCK_TIMEOUTto FALSE in the stored procedure
- C. Set the transaction abort on error to true in the stored procedure.
- D. Call the system function SYSTEM$CANCEL_TRANSACTION.
Answer: A
Explanation:
Explanation
The system function SYSTEM$ABORT_TRANSACTION can be used to abort a detached transaction that was left in an open state due to a session disconnect or termination. The function takes one argument: the transaction ID of the detached transaction. The function will abort the transaction and release any locks held by it. The other options are incorrect because they do not address the issue of a detached transaction. The system function SYSTEM$CANCEL_TRANSACTION can be used to cancel a running transaction, but not a detached one. The LOCK_TIMEOUT parameter can be used to set a timeout period for acquiring locks on resources, but it does not affect existing locks. The TRANSACTION_ABORT_ON_ERROR parameter can be used to control whether a transaction should abort or continue when an error occurs, but it does not affect detached transactions.
NEW QUESTION # 98
A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?
- A. AWS Lambda functions
- B. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows
- C. AWS Glue workflows
- D. AWS Step Functions tasks
Answer: D
Explanation:
https://docs.aws.amazon.com/step-functions/latest/dg/connect-emr.html
https://docs.aws.amazon.com/step-functions/latest/dg/connect-glue.html
NEW QUESTION # 99
A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.
A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.
Which solution will meet this requirement?
- A. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
- B. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
- C. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
- D. Create a data quality ruleset with Data Quality Definition language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
Answer: B
Explanation:
Data Quality Ruleset: Creating a ruleset with Data Quality Definition Language (DQDL) rules allows for defining and evaluating data quality on specific AWS Glue tables, enabling automated checks on data quality.
Scheduled Execution: Running the ruleset daily ensures that data quality scores are regularly updated.
AWS Glue Data Source in Amazon DataZone: Configuring Amazon DataZone with an AWS Glue data source enables seamless integration, allowing data quality scores from AWS Glue Data Quality to be published to the Amazon DataZone portal.
NEW QUESTION # 100
A finance company uses Amazon Redshift as a data warehouse. The company stores the data in a shared Amazon S3 bucket. The company uses Amazon Redshift Spectrum to access the data that is stored in the S3 bucket. The data comes from certified third-party data providers. Each third-party data provider has unique connection details.
To comply with regulations, the company must ensure that none of the data is accessible from outside the company's AWS environment.
Which combination of steps should the company take to meet these requirements? (Choose two.)
- A. Replace the existing Redshift cluster with a new Redshift cluster that is in a private subnet. Use an interface VPC endpoint to connect to the Redshift cluster. Use a NAT gateway to give Redshift access to the S3 bucket.
- B. Use federated queries to access the data from each data provider. Do not upload the data to the S3 bucket. Perform the federated queries through a gateway VPC endpoint.
- C. Create an AWS CloudHSM hardware security module (HSM) for each data provider. Encrypt each data provider's data by using the corresponding HSM for each data provider.
- D. Define table constraints for the primary keys and the foreign keys.
- E. Turn on enhanced VPC routing for the Amazon Redshift cluster. Set up an AWS Direct Connect connection and configure a connection between each data provider and the finance company's VPC.
Answer: A,E
NEW QUESTION # 101
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