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NEW QUESTION # 147
A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte- scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Change the format of the files that are in the dataset to Apache Parquet.
- B. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day.
- C. Use the query result reuse feature of Amazon Athena for the SQL queries.
- D. Add an Amazon ElastiCache cluster between the BI application and Athena.
Answer: C
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/performance-tuning.html
Use the Query Result Reuse Feature of Amazon Athena. This leverages Athena's built-in feature to reduce redundant data scans and thus lowers query costs.
NEW QUESTION # 148
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
- B. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.
- C. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
- D. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
Answer: C
NEW QUESTION # 149
Mark the incorrect statement in case Data engineer using the COPY INTO <table> command to load data from files into Snowflake tables?
- A. For Data loading of files with semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-16.
- B. For Local environment, Files are first copied ("staged") to an internal (Snowflake) stage, then loaded into a table.
- C. For loading data from all semi-structured supported file formats (JSON, Avro, etc.), as well as unloading data, UTF-8 is the only supported character set.
- D. UTF-32 & UTF-16 both encoding character sets supported for loading data from de-limited files (CSV, TSV, etc.)
Answer: A
Explanation:
Explanation
For Data Loading of delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets, you must explicitly specify the encoding to use for loading.
For semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-8.
Rest of the statements are correct.
NEW QUESTION # 150
Which Snowflake feature facilitates access to external API services such as geocoders. data transformation, machine Learning models and other custom code?
- A. Java User-Defined Functions (UDFs)
- B. Security integration
- C. External tables
- D. External functions
Answer: D
Explanation:
Explanation
External functions are Snowflake functions that facilitate access to external API services such as geocoders, data transformation, machine learning models and other custom code. External functions allow users to invoke external services from within SQL queries and pass arguments and receive results as JSON values. External functions require creating an API integration object and an external function object in Snowflake, as well as deploying an external service endpoint that can communicate with Snowflake via HTTPS.
NEW QUESTION # 151
A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The company runs the data stream in an account named Account A. The company wants to use an AWS Lambda function in an account named Account B to process the data from the data stream. The company creates a Lambda execution role in Account B that has permissions to access data from the data stream in Account A.
What additional step must the company take to meet this requirement?
- A. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B.
- B. Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account
- C. Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.
- D. Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.
Answer: D
Explanation:
To enable cross-account Lambda processing of Kinesis Data Streams, the stream in Account A must explicitly allow the Lambda execution role from Account B. This is done by adding a resource-based policy on the Kinesis data stream to grant kinesis:SubscribeToShard and related read permissions to the cross-account role. Without this resource-based policy, the Lambda in Account B cannot consume the data.
NEW QUESTION # 152
Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).
- A. HASH_AGG(*)
- B. HASH_AGG(<expr>, <expr>)
- C. HASH COMPARE(*)
- D. HASH_AGG_COMPARE (*)
- E. HASH (*)
Answer: A,B
Explanation:
Explanation
The functions that will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results are:
HASH_AGG(*): This function computes a hash value over all columns and rows in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.
HASH_AGG(<expr>, <expr>): This function computes a hash value over two expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:
HASH (*): This function computes a hash value over all columns in a single row. The function returns one value per row, not one value per table, query result, or window.
HASH_AGG_COMPARE (): This function compares two hash values computed by HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
HASH COMPARE(): This function compares two hash values computed by HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
NEW QUESTION # 153
A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.
The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.
Which combination of steps will meet these requirements with the LEAST development effort?
(Choose two.)
- A. Use Amazon OpenSearch to correlate the logs and traces.
- B. Use AWS Glue to correlate the logs and traces.
- C. Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.
- D. Use FluentBit to collect logs. Use OpenTelemetry to collect traces.
- E. Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.
Answer: A,D
Explanation:
FluentBit is a lightweight, efficient tool to collect, process, and forward logs from the EKS cluster.
It integrates well with AWS services like CloudWatch or OpenSearch for storing and analyzing logs. OpenTelemetry is an open-source standard for collecting distributed traces, making it ideal for monitoring and analyzing application performance in a microservice architecture. Using these tools requires minimal development effort since they are widely adopted and have strong integrations with AWS.
Amazon OpenSearch (formerly Elasticsearch) is a fully managed service that makes it easy to store, search, and analyze log data, including traces from distributed systems. It is commonly used to correlate logs and traces because it provides robust querying capabilities and visualization features (via Kibana or OpenSearch Dashboards). This solution offers the necessary tools for log-trace correlation with minimal custom development effort.
While CloudWatch is suitable for log collection, Amazon Kinesis is more suited for real-time data streaming rather than collecting traces for correlation.
Kinesis also requires more configuration and development effort compared to OpenTelemetry for tracing.
Similar to Kinesis, Amazon MSK is primarily used for data streaming rather than trace collection.
It requires additional setup and custom development for correlation compared to the more straightforward integration provided by FluentBit and OpenTelemetry.
AWS Glue is primarily used for ETL (extract, transform, load) operations in a data lake. It is not designed for real-time log and trace correlation and would require significant development effort compared to the built-in capabilities of OpenSearch.
NEW QUESTION # 154
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. Add the S3 bucket as a resource that the QuickSight service role can access.
- D. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the BI-Account account.
- E. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
Answer: B
NEW QUESTION # 155
While creating even Secure UDF, snowflake recommended to use randomized identifiers (e.g. gen-erated by UUID_STRING) instead of sequence-generated values?
- A. FALSE
- B. TRUE
(Correct)
Answer: B
NEW QUESTION # 156
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:
Which solution will meet this requirement with the LEAST coding effort?
- A. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.
- B. Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.
- C. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.
- D. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
Answer: C
NEW QUESTION # 157
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use API calls to access and integrate third-party datasets from AWS DataSync.
- B. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
- C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
- D. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
Answer: A
Explanation:
Data Exchange is the AWS official third-party datasets repository:
https://aws.amazon.com/data-exchange
NEW QUESTION # 158
A company runs a data platform on AWS. The data platform uses AWS Glue to provide a data catalog and to perform processing. The company notices quality issues in the data.
The company needs to implement data quality validations. The validations must include rules for known issues. The validations must have the ability to automatically detect unexpected data quality issues.
Which solution will meet these requirements with the LEAST operation overhead?
- A. Use AWS Glue DataBrew to profile the data. Configure data quality rules based on the data quality results from the profiling.
- B. Use AWS Glue jobs to implement AWS Glue Data Quality validations that include anomaly detection.
- C. Use AWS Glue jobs to implement data quality rules that use open source data quality frameworks.
- D. Use AWS Glue jobs to implement data quality validations that use SQL statements.
Answer: B
Explanation:
AWS Glue Data Quality provides native, managed data quality rules with built-in anomaly detection, allowing validation of known issues while automatically identifying unexpected data quality problems with minimal operational effort.
NEW QUESTION # 159
Which of the following statements is/are incorrect regarding Fail-safe data recovery?
- A. Short-lived tables (i.e. <1 day), such as ETL work tables, can be defined as transient to eliminate Fail-safe costs.
- B. Data stored in temporary tables is not recoverable after the table is dropped as they do not have fail-safe.
- C. If downtime and the time required to reload lost data are factors, permanent tables, even with their added Fail-safe costs, may offer a better overall solution than transient tables.
- D. Long-lived tables, such as fact tables, should always be defined as permanent to ensure they are fully protected by Fail-safe.
- E. Historical data in transient tables can be recovered by Snowflake due to Operation fail-ure after the Time Travel retention period ends using Fail-safe.
Answer: E
NEW QUESTION # 160
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. 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.
- B. 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.
- C. Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.
- D. 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.
Answer: B
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 # 161
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports.
A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge. All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently.
Which solution will meet these requirements with the LEAST operational effort?
- A. Use Quick Suite dashboards that have federated query access to Amazon Redshift.
- B. Use Quick Suite dashboards that have zero-ETL access to Amazon Redshift.
- C. Enable Amazon Q in Quick Suite. Generate Quick Suite dashboards and reports.
- D. Integrate Tableau with Amazon Redshift to give Tableau direct access to the data.
Answer: C
Explanation:
Amazon Q in QuickSight enables users to create dashboards and reports using natural language queries, removing the need for SQL knowledge. It is fully integrated and managed within QuickSight, providing the most operationally efficient way for analysts of varying skill levels to generate insights quickly without additional tooling or infrastructure.
NEW QUESTION # 162
What is the purpose of the BUILD_FILE_URL function in Snowflake?
- A. It generates a temporary URL for accessing a file in a stage.
- B. It generates a permanent URL for accessing files in a stage.
- C. It generates a staged URL for accessing a file in a stage.
- D. It generates an encrypted URL foe accessing a file in a stage.
Answer: C
Explanation:
Explanation
The BUILD_FILE_URL function in Snowflake generates a temporary URL for accessing a file in a stage. The function takes two arguments: the stage name and the file path. The generated URL is valid for 24 hours and can be used to download or view the file contents. The other options are incorrect because they do not describe the purpose of the BUILD_FILE_URL function.
NEW QUESTION # 163
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?
- A. Ingest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
- B. Use an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.
- C. Create an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.
- D. Ingest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
Answer: B
NEW QUESTION # 164
A SQL UDF evaluates an arbitrary SQL expression and returns the result(s) of the expression. Which value type it can returns?
- A. Regex
- B. Single Value
- C. Scaler or Tabular depend on input SQL expression
- D. A Set of Rows
Answer: C
NEW QUESTION # 165
Snowpipe API provides a REST endpoint for defining the list of files to ingest that Informs Snow-flake about the files to be ingested into a table. A successful response from this endpoint means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. What is name of this Endpoint?
- A. REST endpoints--> insertfiles
- B. REST endpoints --> ingestfiles
- C. REST endpoints --> loadHistoryScan
- D. REST endpoints --> insertReport
Answer: A
Explanation:
Explanation
The Snowpipe API provides a REST endpoint for defining the list of files to ingest.
Endpoint: insertFiles
Informs Snowflake about the files to be ingested into a table. A successful response from this end-point means that Snowflake has recorded the list of files to add to the table. It does not necessarily mean the files have been ingested. For more details, see the response codes below.
In most cases, Snowflake inserts fresh data into the target table within a few minutes.
To Know more about SnowFlake Rest API used for Data File ingestion, do refer:
https://docs.snowflake.com/en/user-guide/data-load-snowpipe-rest-apis.html#data-file-ingestion
NEW QUESTION # 166
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