AWS DEA-C01 Certification Exam Sample Questions

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AWS DEA-C01 Sample Questions:

01. An AWS Step Functions Standard workflow uses a Map state in Distributed mode to process about 2 million objects in an Amazon S3 prefix. A small number of objects are corrupt, and whenever any child workflow execution fails, the whole Map Run fails. The business accepts the run as successful when no more than 1% of the objects fail.
Which change will meet this requirement?
a) Add a Catch field to the Map state that sends the error to a Succeed state
b) Set a 1% tolerated failure percentage
c) Switch the Map state to Inline mode so that each failure stays within its iteration
d) Add a Retry field to the Map state so that the failed child workflow execution is attempted again before the run fails
 
02. A company runs about 40 AWS Glue 4.0 Spark jobs on G.1X workers. Ten jobs refresh dashboards every hour and must start promptly. The other jobs are nightly historical backfills and test runs whose only deadline is to finish before the next business day.
The company wants to reduce the cost of the backfill and test jobs without rewriting their code, moving them to another service, or affecting the hourly dashboard jobs.
Which solution will meet these requirements MOST cost-effectively?
a) Run only the backfill and test jobs with the Flex execution class
b) Run every job with the Flex execution class, including the hourly dashboard jobs
c) Move the backfill and test jobs to Amazon EMR on EC2 clusters that use Spot Instances for the task nodes
d) Enable job run queuing on the backfill jobs
 
03. An AWS Glue ETL job ingests daily order files. Normal daily volume varies widely with seasonal promotions, but a sharp drop compared with recent days usually means that a partner sent an incomplete file. A data engineer must add a data quality check to the job that fails when this happens, without retuning thresholds each season.
Which check should the data engineer add?
a) An Amazon CloudWatch alarm on the elapsed time of each job run
b) A static DQDL RowCount rule with a minimum set from last quarter's daily volume
c) A dynamic DQDL rule that fails when a run's row count drops below a set fraction of the average of recent runs
d) A DQDL IsComplete rule on the order ID column
 
04. A data engineer loads a nightly extract of customer records into an Amazon Redshift table that declares customer_id as its PRIMARY KEY. After a source system retried a transfer, analysts suspect that the table now holds duplicate customers. The engineer must verify the load before the reporting jobs run.
Which actions should the engineer take?
(Choose TWO.)
a) Run a query that groups by customer_id and returns groups having COUNT(*) greater than 1
b) Run VACUUM DELETE ONLY to remove duplicates
c) Rely on the PRIMARY KEY constraint, which rejects rows that repeat an existing key
d) Run ANALYZE on the table to refresh its statistics and duplicate count
e) Compare the table's row count with the extract's record count\
 
05. The Amazon CloudWatch Logs log groups for a company's AWS Glue jobs and AWS Lambda functions use the default retention setting, and their storage cost grows every month. The data engineering team needs the last 30 days of log events in CloudWatch Logs for troubleshooting with CloudWatch Logs Insights. Every log event must also be kept in Amazon S3 for 2 years for later analysis with Amazon Athena, delivered continuously without scheduled jobs.
Which actions should the team take to meet these requirements?
(Choose TWO.)
a) Set a 30-day retention period on each log group
b) Add a subscription filter that streams each log event to Amazon Data Firehose for delivery to Amazon S3
c) Move the log groups to the Infrequent Access log class so that events older than 30 days are removed automatically
d) Run a scheduled Lambda function that creates an export task to Amazon S3 each day
e) Create a metric filter on each log group
 
06. An Amazon DynamoDB table that stores pipeline checkpoints is defined in an AWS CloudFormation stack. An upcoming template change modifies a table property whose update behavior is Replacement. Next quarter, the team will delete the whole stack when the pipeline is decommissioned.
In both cases, the existing table and its data must remain in the account, and the team must still be able to apply the change and delete the stack.
Which template change will meet these requirements?
a) Set both DeletionPolicy and UpdateReplacePolicy to Retain on the table resource
b) Apply a stack policy that denies Update:Replace on the table
c) Turn on termination protection and drift detection for the stack
d) Set DeletionPolicy to Retain on the table resource
 
07. After each hourly load, an AWS Lambda function uses the AWS SDK to start an Amazon Athena query over the new partition. The function takes the query execution ID that Athena returns and immediately requests the query results with it. For small partitions this works. For large partitions, which take several minutes to scan, the results request fails because the query is still running, and the function exits without passing any rows to the next step.
The function has a timeout long enough for the largest query, and each hourly query must return results for the newest data.
Which change will make the function reliably retrieve the results?
a) Read the result file directly from the query result location in Amazon S3 instead of calling the results API
b) Add a fixed one-minute sleep after starting the query, then request the results with the execution ID
c) Increase the function's memory and timeout so that the SDK call that starts the query waits for the query to finish
d) Check the query state and request results only after it succeeds
 
08. An AWS Glue Spark job joins a 3 TB clickstream DataFrame with a 40 MB product DataFrame on product_id. The Spark UI shows that the join runs as a sort-merge join, that its stage writes and reads several terabytes of shuffle data with heavy spill to disk, and that this stage takes most of the runtime. Task durations are even across partitions, so the data is not skewed.
Which code change will reduce the runtime MOST without adding workers?
a) Wrap the product DataFrame in broadcast() in the join so that every executor holds a copy and the clickstream data is never shuffled
b) Call cache() on the product DataFrame before the join so that it is held in executor memory
c) Join on a composite key of product_id and a high-cardinality column
d) Convert both DataFrames to DynamicFrames and join them with the Glue Join transform
 
09. Thousands of IoT telemetry events arrive each second through Amazon EventBridge. Each event must pass through a short validate, enrich and write workflow that finishes in a few seconds and writes to Amazon DynamoDB with idempotent PUT operations. The data engineer wants to orchestrate this with AWS Step Functions.
Which approach will meet these requirements MOST cost-effectively?
a) A Standard workflow that runs a Distributed Map over each hour of events
b) A Standard workflow that starts for each incoming event
c) An Amazon MWAA DAG
d) An Express workflow per event
 
10. Several analysts and a reporting tool run the same Amazon Athena query many times during each business day. The table is already partitioned by date, and the query reads the last 30 days. The table's data in Amazon S3 changes only once each night. The company wants to cut the data scanned by these repeated runs with the least effort.
What should a data engineer do?
a) Set a per-query data scanned limit in the workgroup
b) Save the statement as a named query for all users
c) Turn on query result reuse with a maximum age for these queries
d) Configure partition projection on the table so that Athena calculates its partition values

Answers:

Question: 01
Answer: b		 Question: 02
Answer: a		 Question: 03
Answer: c		 Question: 04
Answer: a, e		 Question: 05
Answer: a, b
Question: 06
Answer: a		 Question: 07
Answer: d		 Question: 08
Answer: a		 Question: 09
Answer: d		 Question: 10
Answer: c

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