Questions and Answers
Question CDVc5g6TdzBiVbj0MfuF
Question
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue. The data engineer’s original query is as follows: SELECT product_name, sum(sales_amount)
FROM sales_data -
WHERE year = 2023 -
GROUP BY product_name - How should the data engineer modify the Athena query to meet these requirements?
Choices
- A: Replace sum(sales_amount) with count(*) for the aggregation.
- B: Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.
- C: Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.
- D: Remove the GROUP BY clause.
answer?
Answer: B Answer_ET: B Community answer B (61%) C (39%) Discussion
Comment 1177119 by GiorgioGss
- Upvotes: 12
Selected Answer: B “SELECT product_name, sum(sales_amount) FROM sales_data WHERE extract(year FROM sales_date) = 2023 GROUP BY product_name;” A. This would change the query to count the number of rows instead of summing sales. C. This would filter out products with zero sales amounts. D. Removing the GROUP BY clause would result in a single sum of all sales amounts without grouping by product_name.
Comment 1305019 by pikuantne
- Upvotes: 7
None of these options make sense. I think the question is worded incorrectly. I understand that the problem is supposed to be: the products that did not have any sales in 2023 should also be visible in the report with sum of sales_amount = 0. So, the WHERE condition should be deleted and replaced with a CASE WHEN. That way all of the products in the table will be visible, but only sales for 2023 will be summed. Which is what I think this question is asking. None of the provided options do that.
Comment 1347765 by YUICH
- Upvotes: 2
Selected Answer: B hy Option (B) Works If the underlying table field is a date or timestamp (rather than a numeric year column), using WHERE year = 2023 filters out all rows that do not literally match year = 2023. By using extract(year FROM sales_data) = 2023, you are correctly filtering rows whose date (or timestamp) in the sales_data column corresponds to the year 2023. Hence, (B) resolves the problem by filtering on the correct year value from the actual date/timestamp column, ensuring all qualifying products are included in the results.
Comment 1339229 by Udyan
- Upvotes: 1
Selected Answer: C The issue might be that some products have sales amounts of 0 or NULL, and those records are being excluded from the results because Athena may not include them in the final output when performing aggregation. By using the HAVING clause, you can filter the groups based on the aggregated sales amount (sum). This ensures that only products with a non-zero sum of sales are returned in the results. The HAVING clause is used to filter results after the aggregation.
Comment 1333208 by MLOPS_eng
- Upvotes: 1
Selected Answer: C The HAVING clause filters the results to include only products with an aggregated sales amount greater than zero.
Comment 1332514 by Assassin27
- Upvotes: 1
Selected Answer: C SELECT product_name, sum(sales_amount) FROM sales_data WHERE year = 2023 GROUP BY product_name HAVING sum(sales_amount) > 0
Explanation: The HAVING clause ensures that only products with a non-zero aggregated sales amount are included in the results. This will address cases where products exist in the table but have no sales data for 2023.
Comment 1330940 by kailu
- Upvotes: 1
Selected Answer: C There is no issue with the WHERE clause from the original query, so B is not the right option IMO.
Comment 1292349 by Shatheesh
- Upvotes: 2
C, query in the question is correct you just need to get amounts grater than Zero
Comment 1217488 by valuedate
- Upvotes: 5
Selected Answer: B year should be the partition in s3 so its necessary to extract. its not a column
Comment 1213800 by VerRi
- Upvotes: 2
Selected Answer: C No need to extract the year again
Comment 1212444 by Just_Ninja
- Upvotes: 1
Selected Answer: C https://docs.aws.amazon.com/kinesisanalytics/latest/sqlref/sql-reference-having-clause.html
Comment 1203806 by Snape
- Upvotes: 1
Selected Answer: C Wrong answers
A. Replace sum(sales_amount) with count(*) for the aggregation. This option will return the count of records for each product, not the sum of sales amounts, which is the desired result.
B. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023. The year column likely stores the year value directly, so there’s no need to extract it from a date or timestamp column.
D. Remove the GROUP BY clause. Removing the GROUP BY clause will cause an error because the sum(sales_amount) aggregation function requires a GROUP BY clause to specify the grouping column (product_name in this case).
Comment 1203361 by khchan123
- Upvotes: 3
B B. Change
WHERE year = 2023toWHERE extract(year FROM sales_data) = 2023.The issue with the original query is that it assumes there is a column named
yearin thesales_datatable. However, it’s more likely that the date or timestamp information is stored in a single column, for example, a column namedsales_date.To extract the year from a date or timestamp column, you need to use the
extract()function in Athena SQL.Comment 1197696 by chris_spencer
- Upvotes: 2
None of the answer makes senses. Option C will exclude any amount that is 0. This option would be correct if it is: Add HAVING sum(sales_amount) >= 0 after the GROUP BY clause.
Comment 1194639 by Christina666
- Upvotes: 2
Selected Answer: C Gemini: C. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.
Zero Sales Products: The original query is likely missing products that had zero sales amount in 2023. This modification filters the grouped results, ensuring only products with positive sales are displayed. Why Other Options Don’t Address the Core Issue:
A. Replace sum(sales_amount) with count(*) for the aggregation. This would show how many sales transactions a product had, but not if it generated any revenue. It wouldn’t solve the issue of missing products. B. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023. This is functionally equivalent to the original WHERE clause if the year column is already an integer type. It wouldn’t fix missing products. D. Remove the GROUP BY clause. This would aggregate all sales for 2023 with no product breakdown, losing the granularity needed.
Comment 1173184 by kj07
- Upvotes: 4
Not A because the engineer wants a sum not the total count. Not C because it will filter out the data with sales_amount zero. Not D because it will return just one result and the engineer wants the sales for multiple products.
B should be the right answer if the sales_data is a date field.
Comment 1138366 by rralucard_
- Upvotes: 2
Selected Answer: C https://docs.aws.amazon.com/athena/latest/ug/select.html
Question iISCzyGUXj0nvAaziDoD
Question
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?
Choices
- A: 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.
- B: Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
- C: Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
- D: Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.
answer?
Answer: B Answer_ET: B Community answer B (71%) D (29%) Discussion
Comment 1331469 by imymoco
- Upvotes: 1
Selected Answer: B only one column → S3 select
Comment 1322817 by JoeAWSOCM
- Upvotes: 4
Selected Answer: D S3 select is for querying one object. Here the requirement is to query one column from multiple objects. Also S3 select is discontinued for new users. So answer could be D
Comment 1265337 by catoteja
- Upvotes: 1
Amazon S3 Select is no longer available to new customers. Existing customers of Amazon S3 Select can continue to use the feature as usual
But with it you can only query one object xD. Glue + athena
Comment 1231367 by dungct
- Upvotes: 3
but s3 select can only select one object
Comment 1230687 by hogs
- Upvotes: 2
Selected Answer: B omly once
Comment 1217157 by FunkyFresco
- Upvotes: 2
Selected Answer: B if is one-time task
Comment 1177127 by GiorgioGss
- Upvotes: 2
Selected Answer: B https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-select.html
Comment 1138370 by rralucard_
- Upvotes: 3
Selected Answer: B https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage-inventory-athena-query.html S3 Select allows you to retrieve a subset of data from an object stored in S3 using simple SQL expressions. It is capable of working directly with objects in Parquet format.
Question edtce3XeFZm5iaVJNh7S
Question
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions. The data engineer requires a less manual way to update the Lambda functions. Which solution will meet this requirement?
Choices
- A: Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.
- B: Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
- C: Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
- D: Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function’s alias.
answer?
Answer: B Answer_ET: B Community answer B (100%) Discussion
Comment 1137879 by TonyStark0122
- Upvotes: 20
B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions. Explanation: Lambda layers allow you to centrally manage shared code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into a Lambda layer, you can simply update the layer whenever changes are made to the scripts, and all the Lambda functions that use the layer will automatically inherit the updates. This approach reduces manual effort and ensures consistency across the functions.
Comment 1226775 by pypelyncar
- Upvotes: 4
Selected Answer: B Centralized Code Management: Lambda layers allow you to store and manage the custom Python scripts in a central location outside the individual Lambda function code. This eliminates the need to update the script in each Lambda function manually. Reusable Code: Layers provide a way to share code across multiple Lambda functions. Any changes made to the layer code are automatically reflected in all the functions using that layer, streamlining updates. Reduced Deployment Size: By separating core functionality into layers, you can keep the individual Lambda function code focused and smaller. This reduces deployment package size and potentially improves Lambda execution times.
Comment 1277511 by JavierEF
- Upvotes: 2
Selected Answer: B Lambda Layers is a feature created with this literal objective in mind.
Comment 1235360 by John2025
- Upvotes: 2
B is right
Comment 1219531 by 4c78df0
- Upvotes: 1
Selected Answer: B B is correct
Comment 1218784 by 4c78df0
- Upvotes: 1
Selected Answer: B B is correct
Comment 1212615 by FunkyFresco
- Upvotes: 1
Selected Answer: B Lamba layers
Comment 1184939 by ba72eb9
- Upvotes: 2
Option B
Comment 1173428 by kj07
- Upvotes: 2
Typical use case for Lambda Layers. Option B.
Question u1vZCTFgF12c7U5mpJF2
Question
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?
Choices
- A: Use Apache Airflow to refresh the materialized views.
- B: Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
- C: Use the query editor v2 in Amazon Redshift to refresh the materialized views.
- D: Use an AWS Glue workflow to refresh the materialized views.
answer?
Answer: C Answer_ET: C Community answer C (88%) 12% Discussion
Comment 1333503 by magnorm
- Upvotes: 2
Selected Answer: C https://docs.aws.amazon.com/redshift/latest/mgmt/query-editor-v2-schedule-query.html
Comment 1227594 by pypelyncar
- Upvotes: 2
Selected Answer: C the company can automate the refresh schedules for materialized views with minimal effort. This approach leverages the built-in capabilities of Amazon Redshift, reducing the need for additional services, configurations, or custom code. It aligns with the principle of using the simplest and most straightforward solution that meets the requirements, minimizing operational overhead and complexity
Comment 1208157 by d8945a1
- Upvotes: 3
Selected Answer: C We can schedule the refresh using query scheduler from Query Editor V2.
Comment 1194640 by Christina666
- Upvotes: 2
Selected Answer: C Amazon Redshift can automatically refresh materialized views with up-to-date data from its base tables when materialized views are created with or altered to have the autorefresh option. For more details, refer to the documentation here, https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html.
Comment 1186790 by [Removed]
- Upvotes: 2
Selected Answer: C https://docs.aws.amazon.com/redshift/latest/mgmt/query-editor-v2-schedule-query.html
Comment 1180978 by FuriouZ
- Upvotes: 2
Selected Answer: C You can set autorefresh for materialized views using CREATE MATERIALIZED VIEW. You can also use the AUTO REFRESH clause to refresh materialized views automatically.
Comment 1177133 by GiorgioGss
- Upvotes: 1
Selected Answer: C https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-refresh.html
Comment 1173189 by kj07
- Upvotes: 1
You can set AUTO REFRESH option on creation. So I will vote with C.
Comment 1172582 by confusedyeti69
- Upvotes: 1
Selected Answer: C Lambda requires code and configuring permissions. A and D are additional overheads as well. Vote C
Comment 1167432 by damaldon
- Upvotes: 1
B. https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-UDFs.html
Comment 1138385 by rralucard_
- Upvotes: 2
Selected Answer: B AWS Lambda allows running code in response to triggers without needing to provision or manage servers. However, creating a UDF within Amazon Redshift to call a Lambda function for this purpose involves writing custom code and managing permissions between Lambda and Redshift.
Question EzmG52BuwPapIdLcVqj7
Question
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?
Choices
- 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.
- B: 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.
- C: Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.
- D: 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?
Answer: A Answer_ET: A Community answer A (86%) 14% Discussion
Comment 1227600 by pypelyncar
- Upvotes: 6
Selected Answer: A Step Functions is a managed service for building serverless workflows. You define a state machine that orchestrates the execution sequence. This eliminates the need to manage and maintain your own workflow orchestration server like Airflow.
Comment 1269321 by hcong
- Upvotes: 3
Selected Answer: C AWS Glue is a fully managed ETL (extract, transform, load) service that makes it easy to orchestrate data pipelines. Using the AWS Glue workflow to run Lambda functions and glue jobs is the easiest and least expensive option because it’s a fully managed service that requires no additional workflow tools or infrastructure to configure and manage. Other options require additional tools or resources to configure and manage, and are therefore more expensive to manage.
Comment 1222507 by tgv
- Upvotes: 2
Selected Answer: A Step Functions can handle both Lambda and Glue in this scenario, making it the best choice.
Comment 1210741 by hnk
- Upvotes: 4
Selected Answer: A B and D require additional effort C Glue workflows do not have a direct integration with lambda hence the best choice is A
Comment 1181390 by FuriouZ
- Upvotes: 3
Selected Answer: A Key word orchestrating is most likely step functions
Comment 1138395 by rralucard_
- Upvotes: 4
Selected Answer: A Option A, using AWS Step Functions, is the best solution to meet the requirement with the least management overhead. Step Functions is designed for easy integration with AWS services like Lambda and Glue, providing a managed, low-code approach to orchestrate workflows. This allows for a more straightforward setup and less ongoing management compared to the other options.