AWS Certified Data Engineer - Associate (DEA-C01)

Creating an S3 event notification configured with the s3:ObjectCreated:* event type ensures that the notification triggers whenever a new object is successfully uploaded to the bucket (including Put, Post, Copy, and Multipart Upload completion). Using a suffix filter rule for .csv ensures that the notification is generated, and thus the Lambda function is invoked, only when the uploaded object's key name ends with .csv. Setting the Lambda function's ARN directly as the destination for the event notification provides a direct invocation mechanism without intermediate services. This combination precisely meets the requirements (trigger on CSV upload only) with the minimum number of AWS resources and configuration steps, resulting in the least operational overhead.

Configuring the event type s3:ObjectTagging:* is incorrect because this event triggers when tags are added or modified on an S3 object, not when the object is initially created or uploaded. The requirement is to trigger the Lambda function upon file upload.

Using the event type s3:* is inefficient and overly broad. While it includes object creation events, it also includes many other events like object deletion (s3:ObjectRemoved:*), tagging (s3:ObjectTagging:*), etc. Although the suffix filter would prevent the Lambda from processing non-CSV files, the event notification system would still generate notifications for non-creation events involving CSV files (like deletion or tagging), potentially causing unnecessary triggers or requiring more complex handling logic within the Lambda function if it were invoked for events other than creation. Using s3:ObjectCreated:* is more specific and efficient.

Introducing an Amazon SNS topic as an intermediary between the S3 event notification and the Lambda function adds an extra component to manage (the SNS topic itself, its access policies, and the Lambda subscription to the topic). While this architecture is valid and useful for scenarios requiring fan-out to multiple subscribers or decoupling, it increases the operational overhead compared to directly invoking the Lambda function from the S3 event notification. The requirement specifically asks for the solution with the least operational overhead.

First, consider the query engine. The data resides in Amazon S3. Amazon Athena is a serverless query service that allows you to analyze data directly in Amazon S3 using standard SQL. It scales automatically and you pay only for the queries you run, making it highly cost-effective for querying data stored in S3, especially when query patterns might vary. Loading the data into Amazon Redshift would incur additional ETL effort and the costs associated with running a Redshift cluster (provisioned or serverless), which might not be the most cost-effective approach compared to querying directly from S3 with Athena. Amazon S3 Analytics is used for analyzing storage access patterns, not querying the data content itself.

Second, consider the QuickSight data access method. QuickSight offers two modes: Direct Query and SPICE (Super-fast, Parallel, In-memory Calculation Engine).

• Direct Query mode runs queries against the underlying data source (Athena, in this case) in real-time as users interact with the dashboard. With hundreds of users, this could lead to a high volume of Athena queries, potentially increasing costs significantly (Athena charges per query/data scanned) and potentially impacting dashboard performance.
• SPICE imports a copy of the data into a highly optimized, in-memory cache within QuickSight. Queries from dashboards then hit the SPICE layer, providing fast performance and reducing the query load on the underlying source. SPICE datasets can be scheduled to refresh periodically (e.g., daily). For a large number of users accessing the same dataset, SPICE is generally more cost-effective and provides better performance than Direct Query mode, especially when the underlying source is pay-per-query like Athena. The requirement for daily updates aligns perfectly with SPICE's scheduled refresh capability.

Therefore, the most cost-effective and scalable solution involves using Amazon Athena to query the clickstream data directly from S3 and accessing this data in QuickSight through SPICE, configured with a daily refresh.

The requirement is to populate a new table, cities_us, with a specific subset of data (cities located in the US) from an existing table, cities_world. While the question uses the word "create," the provided SQL options focus on data manipulation (UPDATE, INSERT, MOVE) rather than table creation (CREATE TABLE). The most direct way to create a table based on a query result in Athena is using CREATE TABLE AS SELECT (CTAS). Since CTAS is not offered as an option, the question likely assumes the table cities_us already exists with the appropriate columns (city, state), and the task is to insert the relevant data into it.

The standard SQL syntax for inserting data into a table based on a selection from another table is INSERT INTO ... SELECT ....

The statement INSERT INTO cities_usa (city,state) SELECT city, state FROM cities_world WHERE country=’usa’; correctly performs this operation:

1. INSERT INTO cities_usa (city,state): Specifies the target table (cities_usa) and the columns (city, state) into which data will be inserted.
2. SELECT city, state FROM cities_world: Selects the required columns from the source table (cities_world).
3. WHERE country=’usa’: Filters the rows from cities_world to include only those where the country is 'usa'.

This statement effectively copies the city and state for all US cities from the source table into the target table.

The statement UPDATE cities_usa ... is incorrect because UPDATE modifies existing rows, it does not insert new rows. The statement MOVE ... is not valid SQL syntax for this purpose. The statement INSERT INTO cities_usa SELECT city, state FROM cities_world WHERE country=’usa’; is also syntactically valid if the columns city and state are the first two columns (or the only columns) in the cities_usa table and match the order in the SELECT list. However, explicitly listing the target columns, as done in the correct answer, is generally considered better practice as it is more robust to changes in table structure.

Amazon Athena supports the CREATE TABLE AS SELECT (CTAS) statement, which creates a new table based on the results of a SELECT query. The schema of the new table is derived from the columns and data types returned by the SELECT statement.

To create the table with the same schema but without copying the data, Athena provides the WITH NO DATA clause for CTAS statements. The statement CREATE TABLE new_table AS SELECT * FROM old_table WITH NO DATA; instructs Athena to:

1. Determine the schema by evaluating the SELECT * FROM old_table query.
2. Create new_table with that derived schema.
3. Skip the execution of the SELECT query for data population due to the WITH NO DATA clause.

This results in a new, empty table (new_table) with the identical schema as old_table.

The statement CREATE TABLE new_table (LIKE old_table); is not supported syntax in Athena for creating a table based on another table's schema. The LIKE clause in Athena's CREATE TABLE is used differently, typically related to SerDe properties when creating tables based on files.

The statement INSERT INTO new_table SELECT * FROM old_table; is used to copy data into an existing table (new_table) and does not create the table itself.

The statement CREATE TABLE new_table AS SELECT * FROM old_table; is a standard CTAS statement that does copy all data from old_table into new_table, which violates the requirement for the new table to be empty.

Amazon S3 Event Notifications provide a mechanism to automatically trigger downstream actions when specific events occur within an S3 bucket, such as the creation of a new object (s3:ObjectCreated:*). You can configure S3 Event Notifications to send a message to various targets, including an AWS Lambda function.

When a new file arrives in the designated S3 bucket, S3 will automatically invoke the configured Lambda function, passing event details that include the bucket name and the object key (file name). The Lambda function can then use this information to connect to the Amazon Redshift cluster (e.g., using the Redshift Data API or a standard database driver) and execute a COPY command to load the specific new file into the target Redshift table. This approach provides a highly responsive, serverless, and near real-time ingestion pipeline.

Scheduling a COPY command using the query editor v2 is time-based, not event-based. It runs on a fixed schedule (e.g., every hour), leading to potential delays between file arrival and ingestion, thus not meeting the near real-time requirement.

The zero-ETL integration is specifically designed for replicating data changes from Amazon Aurora or Amazon RDS for MySQL databases to Amazon Redshift, not for ingesting files from S3.

AWS Glue job bookmarks are used to track processed data, enabling Glue jobs to process only new data since the last run. While Glue jobs can be triggered by S3 events (typically via EventBridge), running a full Glue ETL job for each arriving file might introduce latency and overhead compared to a lightweight Lambda function executing a COPY command, making it less suitable for near real-time, per-file ingestion.

The scenario describes performance degradation for Amazon Redshift Spectrum queries against CSV files stored in Amazon S3. The queries select subsets of columns and aggregate metrics based on daily orders. The data includes many columns (>100), and all files are stored in a single path. To improve query performance with minimal development effort, we need to address the inefficiencies in data format and layout.

1. Columnar Format: CSV is a row-based format. When Spectrum queries select only a subset of columns from a wide table (100+ columns), it must still read entire rows, including data from columns not requested. Switching to a columnar format like Apache Parquet or ORC allows Spectrum to read data only for the columns specified in the query, significantly reducing the amount of data scanned and improving performance. Configuring the third-party application to generate files directly in a columnar format, if supported, would be the most efficient way to achieve this, minimizing downstream processing effort.

2. Partitioning: The queries aggregate metrics based on daily orders, implying frequent filtering or grouping by date. Storing all files in a single path forces Spectrum to list and potentially scan all files, even if the query only targets a specific date range. Partitioning the data in the S3 bucket based on the order date (e.g., creating prefixes like s3://your-bucket/orders/order_date=YYYY-MM-DD/) allows Spectrum to perform partition pruning. Spectrum can identify and scan only the partitions (and files within them) that match the date predicates in the WHERE clause, drastically reducing the data scanned for date-specific queries.

Combining these two approaches addresses the key performance bottlenecks:

• Using a columnar format optimizes queries that select specific columns.
• Partitioning by date optimizes queries that filter or aggregate by date.

Let's consider the other options:

• Configuring the application to create files in JSON format does not solve the performance issue for selecting subsets of columns, as JSON is also row-oriented.
• Loading JSON data into a Redshift SUPER column involves moving data from S3 into Redshift's local storage, which changes the architecture away from Redshift Spectrum and adds complexity.
• Developing an AWS Glue ETL job to consolidate multiple daily files into one large file might offer minor benefits by reducing S3 listing overhead, but it doesn't address the core inefficiencies of the row-based format or lack of partitioning. It also adds development effort.

Therefore, partitioning the data by order date in S3 and configuring the source application to use a columnar format are the most effective steps to improve Redshift Spectrum query performance with potentially the least development effort.

AWS Glue Data Quality is a feature designed specifically for validating data within AWS Glue ETL jobs. It allows defining rules using the Data Quality Definition Language (DQDL) to assess various aspects of data quality.

By integrating the EvaluateDataQuality transform into the existing Glue ETL job, the data engineer can:

1. Define DQDL rules to check if the dataset is empty (e.g., RowCount > 0).
2. Define DQDL rules to check for completeness in required fields (e.g., IsComplete "required_column_name").
3. Configure the Glue job's logic based on the outcome of the data quality evaluation. If the rules pass, the job proceeds to write the transformed data to the output S3 bucket, overwriting Daily.csv. If the rules fail, the job can be configured to stop or skip the final write step, effectively leaving the previous day's Daily.csv in place.

This approach uses a built-in AWS Glue feature, requires configuration of rules rather than extensive custom coding, and integrates the validation directly into the existing ETL pipeline. This represents the least implementation effort compared to other options.

Using Amazon Athena adds an extra processing step after the Glue job. Modifying the Glue pipeline to fill missing values doesn't meet the requirement of checking validity and preventing overwrites. Invoking an AWS Lambda function requires writing custom validation code and potentially managing orchestration, increasing the effort compared to using the native Glue Data Quality feature.

The requirement is to ensure customer records in Amazon S3 cannot be deleted or modified for 7 years, even by the root user. Amazon S3 Object Lock provides two retention modes to achieve Write-Once-Read-Many (WORM) storage:

1. Governance Mode: Protects objects from deletion and modification by most users. However, users with the s3:BypassGovernanceRetention permission, including the root user, can override or remove the retention settings.
2. Compliance Mode: Provides a higher level of protection. Once an object version is locked in compliance mode, its retention mode cannot be changed, and its retention period cannot be shortened. Critically, no user, including the root user of the AWS account, can overwrite or delete an object version protected by compliance mode during its retention period.

A retention period specifies the length of time the object version remains locked. This period can be set explicitly for an object or automatically applied using a bucket's default retention settings.

To meet the requirement that even the root user cannot delete or modify the data for 7 years, Compliance Mode must be used. Setting a default retention period of 7 years on the bucket ensures that all new objects automatically inherit this lock configuration.

Placing a legal hold prevents deletion/modification but does not have a fixed duration; it remains until explicitly removed and doesn't automatically apply a 7-year term. Using governance mode does not satisfy the requirement of preventing deletion by the root user. Setting only the retention period without specifying compliance mode does not guarantee the required level of protection.

Therefore, enabling compliance mode on the bucket with a default 7-year retention period is the correct solution.

Amazon QuickSight allows calculated fields to be created at different stages:

1. Dataset Level: When you create a calculated field during dataset preparation (before publishing or saving), the calculation is performed as the data is ingested into SPICE. The results of this calculation are then stored within SPICE along with the original data. This meets the requirement for precomputing the calculation and materializing it in SPICE.
2. Analysis/Visual/Dashboard Level: Calculated fields created within an analysis (and subsequently available in visuals and published dashboards) are computed dynamically when the analysis or dashboard is viewed or interacted with. These calculations are performed on the data retrieved from SPICE (or via Direct Query), but the results of the calculation itself are not stored back into SPICE.

To ensure the exchange rate calculations are precomputed and materialized in SPICE, the calculated field must be defined and created at the dataset level. This guarantees that the computation happens during data preparation/ingestion into SPICE, and the resulting values (with the desired precision) are stored efficiently within SPICE for fast retrieval during analysis.

Defining the calculated field in the visual, analysis, or dashboard would mean the calculation happens dynamically at query time, not meeting the requirement for precomputation and SPICE materialization.

Initiating Athena queries is typically done asynchronously using the StartQueryExecution API call. AWS Lambda functions provide a cost-effective way to perform this task. A Lambda function, triggered by a schedule like Amazon EventBridge, can efficiently use the AWS SDK (Boto3 for Python) to invoke start_query_execution. The cost is minimal as Lambda billing is based on consumed compute time, which is very short for simply starting a query.

Since the queries can exceed the 15-minute Lambda limit, a different mechanism is needed to wait for completion and sequence the subsequent queries. AWS Step Functions is designed for orchestrating workflows, including those involving long-running, asynchronous tasks. A common and effective pattern for integrating Step Functions with Athena involves a polling loop. First, a Step Functions state, perhaps invoking the Lambda function described earlier, calls start_query_execution. Subsequent states then manage the waiting process: a Wait state introduces a pause, followed by another task state (again, potentially using Lambda) calling get_query_execution with the query ID to check its status (SUCCEEDED, FAILED, RUNNING). A Choice state evaluates the status, looping back to the Wait state if the query is still RUNNING, proceeding to the next query or step if SUCCEEDED, or triggering error handling if FAILED. This polling pattern allows Step Functions to manage the overall workflow across extended durations cost-effectively, with costs primarily associated with state transitions.

Combining these approaches creates a robust and cost-effective solution. An AWS Lambda function using the Athena Boto3 client's start_query_execution API call programmatically initiates each query. An AWS Step Functions workflow then orchestrates the sequence, using a polling loop with Wait states and tasks calling get_query_execution to ensure one query finishes before the next begins.

Using Amazon MWAA or AWS Batch would likely introduce higher baseline costs and operational complexity compared to this serverless approach for a daily batch process. Relying on AWS Glue Python shell jobs with manual sleep timers for polling is inefficient and not a recommended practice for robust orchestration.