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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Store Management | 26% | - Manage data lifecycle
|
| Topic 2: Data Operations and Support | 22% | - Monitor data pipelines
|
| Topic 3: Data Security and Governance | 18% | - Ensure data encryption
|
| Topic 4: Data Ingestion and Transformation | 34% | - Apply programming concepts
|
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NEW QUESTION # 297
A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?
Answer: D
Explanation:
To create a new table named cities_usa in Amazon Athena based on a subset of data from the existing cities_world table, you should use an INSERT INTO statement combined with a SELECT statement to filter only the records where the country is 'usa'. The correct SQL syntax would be:
* Option A: INSERT INTO cities_usa (city, state) SELECT city, state FROM cities_world WHERE country='usa';This statement inserts only the cities and states where the country column has a value of
'usa' from the cities_world table into the cities_usa table. This is a correct approach to create a new table with data filtered from an existing table in Athena.
Options B, C, and D are incorrect due to syntax errors or incorrect SQL usage (e.g., the MOVE command or the use of UPDATE in a non-relevant context).
References:
* Amazon Athena SQL Reference
* Creating Tables in Athena
NEW QUESTION # 298
A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?
Answer: C
Explanation:
Problem Analysis:
Input Requirements: Gaming app generates approximately 20 KB data records, which must be ingested and made available to three internal consumers with dedicated throughput.
Key Requirements:
High throughput for ingestion from each device.
Dedicated processing bandwidth for each consumer.
Key Considerations:
Amazon Kinesis Data Streams supports high-throughput ingestion with PutRecords API for batch writes.
The Enhanced Fan-Out feature provides dedicated throughput to each consumer, avoiding bandwidth contention.
This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.
Solution Analysis:
Option A: Kinesis Data Streams + Enhanced Fan-Out
PutRecords API is designed for batch writes, improving ingestion performance.
Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.
Option B: Data Firehose + Dedicated Throughput Request
Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.
Option C: Data Firehose + Enhanced Fan-Out
Firehose does not support enhanced fan-out. This option is invalid.
Option D: Kinesis Data Streams + EC2 Instances
Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.
Final Recommendation:
Use Kinesis Data Streams with Enhanced Fan-Out for high-throughput ingestion and dedicated consumer bandwidth.
Reference:
Kinesis Data Streams Enhanced Fan-Out
PutRecords API for Batch Writes
NEW QUESTION # 299
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first 30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?
Answer: A
Explanation:
The most cost-effective solution in this case is to apply a lifecycle policy to transition records to Amazon S3 Standard-IA storage after 30 days. Here's why:
* Amazon S3 Lifecycle Policies: Amazon S3 offers lifecycle policies that allow you to automatically transition objects between different storage classes to optimize costs. For data that is frequently accessed in the first 30 days and infrequently accessed after that, transitioning from the S3 Standard storage class to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days makes the most sense. S3 Standard-IA is designed for data that is accessed less frequently but still needs to be retained, offering lower storage costs than S3 Standard with a retrieval cost for access.
* Cost Optimization: S3 Standard-IA offers a lower price per GB than S3 Standard. Since the data will be accessed infrequently after 30 days, using S3 Standard-IA will lower storage costs while still allowing for immediate retrieval when necessary.
* Compliance with Regulations: Since the records need to be immediately accessible for the first 30 days, the use of S3 Standard for that period ensures compliance with regulatory requirements. After 30 days, transitioning to S3 Standard-IA continues to meet access requirements for infrequent access while reducing storage costs.
* Alternatives Considered:
* Option B (S3 Intelligent-Tiering): While S3 Intelligent-Tiering automatically moves data between access tiers based on access patterns, it incurs a small monthly monitoring and automation charge per object. It could be a viable option, but transitioning data to S3 Standard- IA directly would be more cost-effective since the pattern of access is well-known (frequent for
30 days, infrequent thereafter).
* Option C (S3 Glacier Deep Archive): Glacier Deep Archive is the lowest-cost storage class, but it is not suitable in this case because the data needs to be accessed immediately within 30 days and on an infrequent basis thereafter. Glacier Deep Archive requires hours for data retrieval, which is not acceptable for infrequent access needs.
* Option D (S3 Standard-IA for all records): Using S3 Standard-IA for all records would result in higher costs for the first 30 days, as the data is frequently accessed. S3 Standard-IA incurs retrieval charges, making it less suitable for frequently accessed data.
References:
* Amazon S3 Lifecycle Policies
* S3 Storage Classes
* Cost Management and Data Optimization Using Lifecycle Policies
* AWS Data Engineering Documentation
NEW QUESTION # 300
A data engineering team is using an Amazon Redshift data warehouse for operational reporting. The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
Which table views should the data engineer use to meet this requirement?
Answer: B
Explanation:
The STL ALERT EVENT LOG table view records anomalies when the query optimizer identifies conditions that might indicate performance issues. These conditions include skewed data distribution, missing statistics, nested loop joins, and broadcasted data. The STL ALERT EVENT LOG table view can help the data engineer to identify and troubleshoot the root causes of performance issues and optimize the query execution plan. The other table views are not relevant for this requirement. STL USAGE CONTROL records the usage limits and quotas for Amazon Redshift resources. STL QUERY METRICS records the execution time and resource consumption of queries. STL PLAN INFO records the query execution plan and the steps involved in each query. References:
STL ALERT EVENT LOG
System Tables and Views
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 301
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to useData Quality Definition Language (DQDL)rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?
Answer: D
Explanation:
TheDatasetMatchrule in DQDL checks for full value equivalence between mapped fields. A value of1.0 indicates a100% match. The correct syntax and metric for an exact match scenario are:
"Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Options with "100" use incorrect syntax since DQDL usesfloating-point scores(e.g., 1.0, 0.95), not percentages.
NEW QUESTION # 302
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