Simulado · AWS
Simulado AWS Certified Data Engineer - Associate (DEA-C01)
Resposta curta
O simulado de AWS Certified Data Engineer - Associate (DEA-C01) tem 50 questões em 130 minutos, com nota de corte de 72%, conforme configurado aqui. O jeito mais rápido de saber se você está pronto é fazer uma prova completa no relógio e ler a nota por domínio, não só o total.
Domínios cobrados
- Data Ingestion and Transformation
- Data Operations and Support
- Data Security and Governance
- Data Store Management
Questões de exemplo, com explicação
Três questões do banco, abertas para ler.
A data team wants to monitor a critical Step Functions data pipeline so they are alerted when executions fail and when the daily run does not start by 02:00 UTC (a missed schedule). They prefer native, low-operational-overhead monitoring. Which combination of services best satisfies both the failure alert and the missed-run alert?
- Enable AWS CloudTrail data events on the state machine and parse them with Athena once per day to find failures.
- Configure the state machine's Catch blocks to write a row to DynamoDB on failure and have someone check the table periodically.
- Use the ExecutionsFailed CloudWatch metric with an alarm to SNS for failures, and an EventBridge Scheduler trigger plus a CloudWatch alarm on ExecutionsStarted (treating missing data as breaching) for missed runs. ✓ (correta)
- Rely solely on reading CloudWatch Logs Insights queries manually each morning to detect both failures and missed runs.
Por que esta é a correta
Using the ExecutionsFailed metric with an SNS alarm plus an ExecutionsStarted alarm that treats missing data as breaching is correct: Step Functions publishes ExecutionsFailed and ExecutionsStarted CloudWatch metrics natively, so a metric alarm to SNS gives immediate failure notification, and an alarm that treats no-data-as-breaching over the expected window detects a missed start without custom code. Manually reading Logs Insights each morning is wrong because it is not automated alerting, adds delay, and depends on a human remembering to run queries. Enabling CloudTrail data events and parsing with Athena daily is wrong because CloudTrail is for API audit, the daily batch parse adds latency, and it does not provide proactive alarms on failures or missed runs. Writing failure rows to DynamoDB and checking periodically is wrong because it requires custom polling, only captures failures (not missed starts), and provides no proactive notification.
A company stores most analytical data in Amazon S3 but keeps current order status in an Amazon Aurora PostgreSQL OLTP database. Analysts need a single Athena query that joins historical S3 data with live order rows in Aurora, without copying the Aurora data into S3 first. Which Athena feature enables this, and what is the key operational consideration?
- Athena partition projection, which can read directly from RDS endpoints when the table is registered in Glue
- Athena CTAS, which transparently federates to relational databases at query time
- Redshift Spectrum, because Athena cannot query external relational sources at all
- Athena Federated Query with the Aurora/JDBC connector (a Lambda function); the Lambda concurrency and source database load must be monitored because each query invokes the connector ✓ (correta)
Por que esta é a correta
Athena Federated Query uses data source connectors implemented as Lambda functions to read from non-S3 sources like Aurora at query time, enabling cross-source joins without copying data; because the connector Lambda issues live queries against Aurora, you must watch Lambda concurrency limits and the load placed on the OLTP database. 'Partition projection reading from RDS' confuses two unrelated features: partition projection is an S3 partition-pruning optimization and cannot read relational endpoints. 'CTAS transparently federates' is wrong because CTAS writes query output to S3 and does not itself provide source connectivity; federation is a separate mechanism. 'Redshift Spectrum because Athena cannot query relational sources' is false on both counts: Athena CAN query relational sources via federation, and Redshift Spectrum specifically queries S3 data from Redshift, not arbitrary live RDS tables.
A startup needs to load a vendor's daily 50 GB JSON export from Amazon S3 directly into Amazon Redshift for analytics. The data needs only light type casting, no complex multi-source joins, and the team wants the fastest, most cost-efficient bulk load using Redshift's native parallelism without provisioning a separate ETL cluster. Which ingestion method should they use?
- Stream the file line-by-line through Amazon Kinesis Data Streams into Redshift.
- Use an AWS Glue Spark job to read the JSON and write each row to Redshift via individual INSERT statements.
- Use the Redshift COPY command to bulk load the JSON files from S3 in parallel across all slices. ✓ (correta)
- Use Amazon Data Firehose to deliver the file into Redshift as a continuous stream.
Por que esta é a correta
The Redshift COPY command is correct because it is the native, highest-throughput bulk-load mechanism, reading files from S3 in parallel across all compute slices with optional simple type handling, and it requires no separate ETL cluster, making it the fastest and most cost-efficient option for this straightforward load. An AWS Glue Spark job issuing per-row INSERT statements is wrong because row-by-row inserts bypass Redshift's parallel load path and are dramatically slower and more expensive, and spinning up Spark is unnecessary for light casting. Streaming the file through Kinesis Data Streams is wrong because the source is a single bounded daily file, and forcing it through a streaming service adds shards, ordering, and consumer complexity with no benefit for a bulk load. Using Amazon Data Firehose is wrong because Firehose is built for continuous streaming delivery and micro-batches into Redshift via COPY under the hood, which is slower and less direct than simply issuing one COPY for a known daily file.
Faça o simulado completo
- 50 questões sorteadas do banco, com cronômetro de 130 minutos
- Nota por domínio e revisão de cada questão
- Tutor de IA para conversar sobre cada questão que você errar
- O que você errou alimenta seu plano de estudo
Simulado independente e não oficial. AWS e o nome da prova são marcas de seus donos; o UpSkillScore não é afiliado a eles. Formato e nota de corte são a configuração do simulado no UpSkillScore, não uma declaração oficial.
Perguntas frequentes
Quantas questões tem o simulado de AWS Certified Data Engineer - Associate (DEA-C01)?
Tem 50 questões e limite de 130 minutos. A nota de corte do simulado é 72%.
Este é o exame oficial de AWS Certified Data Engineer - Associate (DEA-C01)?
Não. É um simulado independente, montado para espelhar a estrutura da prova real. Ele não substitui o exame oficial.
Preciso de conta para fazer?
Você lê a estrutura e as questões de exemplo sem conta. Fazer o simulado completo cronometrado, a nota por domínio e o tutor de IA exigem conta.