The answer is an incorrect data type mapping in ApplyMapping, specifically when the "value" column contains non-numeric strings that cannot be cast to double. This causes a Spark stage failure because the ApplyMapping transformation attempts a type conversion that fails at runtime—Spark cannot coerce text like "N/A" or "abc" into a numeric double, leading to task-level exceptions that cascade into a full stage failure. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how schema mismatches manifest in distributed processing; a common trap is assuming the error originates from source data corruption rather than the explicit casting logic in your ETL script. Remember that ApplyMapping is strict: it does not silently ignore conversion failures. A useful memory tip is "ApplyMapping is a type enforcer, not a negotiator"—if the data doesn't fit the declared type, the job fails.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Exhibit
Refer to the exhibit. A data scientist runs the following AWS Glue ETL job script (Spark) to prepare data for ML:
```python
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
datasource = glueContext.create_dynamic_frame.from_options(
connection_type = "s3",
connection_options = {"paths": ["s3://bucket/input/"]},
format = "csv",
format_options = {"withHeader": True}
)
applymapping = ApplyMapping.apply(frame = datasource, mappings = [("id", "int", "id", "int"), ("value", "string", "value", "double")])
...
```
The job fails with an error: "Job run failed: org.apache.spark.SparkException: Job aborted due to stage failure: Task failed while writing rows." What is the most likely cause of this error?
A data scientist runs the exhibit AWS Glue ETL job. The job fails with a Spark stage failure error. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Refer to the exhibit. A data scientist runs the following AWS Glue ETL job script (Spark) to prepare data for ML:
```python
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
datasource = glueContext.create_dynamic_frame.from_options(
connection_type = "s3",
connection_options = {"paths": ["s3://bucket/input/"]},
format = "csv",
format_options = {"withHeader": True}
)
applymapping = ApplyMapping.apply(frame = datasource, mappings = [("id", "int", "id", "int"), ("value", "string", "value", "double")])
...
```
The job fails with an error: "Job run failed: org.apache.spark.SparkException: Job aborted due to stage failure: Task failed while writing rows." What is the most likely cause of this error?
A
The output path is missing.
Why wrong: Missing output path error would occur before the job runs.
B
The S3 bucket does not exist.
Why wrong: Missing bucket error would occur at job initialization, not during task execution.
C
The job does not have enough memory.
Why wrong: Insufficient memory usually results in OutOfMemoryError, not a task writing failure.
D
The data type mapping in ApplyMapping is incorrect; "value" column contains non-numeric strings that cannot be cast to double.
Casting string to double fails on non-numeric data, causing task failure.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The data type mapping in ApplyMapping is incorrect; "value" column contains non-numeric strings that cannot be cast to double.
The Spark stage failure error in an AWS Glue ETL job is most likely caused by a data type mismatch during the ApplyMapping transformation. When the 'value' column contains non-numeric strings that cannot be cast to double, Spark throws a stage failure because it cannot complete the required type conversion, leading to task failures and job termination.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
The output path is missing.
Why it's wrong here
Missing output path error would occur before the job runs.
✗
The S3 bucket does not exist.
Why it's wrong here
Missing bucket error would occur at job initialization, not during task execution.
✗
The job does not have enough memory.
Why it's wrong here
Insufficient memory usually results in OutOfMemoryError, not a task writing failure.
✓
The data type mapping in ApplyMapping is incorrect; "value" column contains non-numeric strings that cannot be cast to double.
Why this is correct
Casting string to double fails on non-numeric data, causing task failure.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often attribute Spark stage failures to resource issues (memory or missing paths) rather than recognizing that data type casting errors during transformations are a primary cause of stage-level failures in Glue ETL jobs.
Trap categories for this question
Command / output trap
Missing output path error would occur before the job runs.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue uses Apache Spark's Catalyst optimizer to plan execution stages. When ApplyMapping attempts to cast a column to double, Spark's code generation inserts a 'cast' expression that throws a 'NumberFormatException' for non-numeric strings, causing the task to fail. This failure is retried up to the Spark 'spark.task.maxFailures' default (4), after which the stage fails. In real-world scenarios, this often occurs when CSV or JSON source data contains unexpected nulls, text like 'N/A', or locale-specific number formats.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The data type mapping in ApplyMapping is incorrect; "value" column contains non-numeric strings that cannot be cast to double. — The Spark stage failure error in an AWS Glue ETL job is most likely caused by a data type mismatch during the ApplyMapping transformation. When the 'value' column contains non-numeric strings that cannot be cast to double, Spark throws a stage failure because it cannot complete the required type conversion, leading to task failures and job termination.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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