Question 880 of 1,000
ML Solution Monitoring, Maintenance and SecurityeasyMultiple ChoiceObjective-mapped

MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security

This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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

{
  "MonitoringScheduleName": "data-quality-monitor",
  "MonitoringType": "DataQuality",
  "ScheduleConfig": {
    "ScheduleExpression": "cron(0 * * * ? *)"
  },
  "MonitoringJobDefinition": {
    "BaseliningJobDefinition": {
      "BaselineJobName": "baseline-job-1",
      "BaseliningJobOutputConfig": {
        "MonitoringOutputS3Uri": "s3://my-bucket/baseline/"
      }
    },
    "MonitoringOutputConfig": {
      "MonitoringOutputS3Uri": "s3://my-bucket/monitoring-results/"
    },
    "Environment": {
      "max_runtime_in_seconds": "3600"
    }
  }
}

Refer to the exhibit. A team configured a SageMaker Model Monitor schedule for data quality. The baseline was created from a training dataset. After running for a day, the monitoring results show frequent violations. 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.

Exhibit

{
  "MonitoringScheduleName": "data-quality-monitor",
  "MonitoringType": "DataQuality",
  "ScheduleConfig": {
    "ScheduleExpression": "cron(0 * * * ? *)"
  },
  "MonitoringJobDefinition": {
    "BaseliningJobDefinition": {
      "BaselineJobName": "baseline-job-1",
      "BaseliningJobOutputConfig": {
        "MonitoringOutputS3Uri": "s3://my-bucket/baseline/"
      }
    },
    "MonitoringOutputConfig": {
      "MonitoringOutputS3Uri": "s3://my-bucket/monitoring-results/"
    },
    "Environment": {
      "max_runtime_in_seconds": "3600"
    }
  }
}

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

The baseline was created from a dataset that does not represent production data.

Option A is correct because SageMaker Model Monitor compares production data against a baseline statistics and constraints file. If the baseline was created from a training dataset that does not reflect the actual distribution, patterns, or schema of production data, the monitor will flag frequent violations. This is the most common cause of false-positive alerts in data quality monitoring.

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 baseline was created from a dataset that does not represent production data.

    Why this is correct

    If the baseline does not reflect real-world data, constraints will be frequently violated.

    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.

  • The environment variable max_runtime_in_seconds is too low.

    Why it's wrong here

    A low max runtime would cause the job to timeout, not produce violation results.

  • The schedule runs too often (every hour), causing overload.

    Why it's wrong here

    Hourly monitoring is reasonable and would not cause frequent violations unless the data itself has issues.

  • The monitoring output destination is incorrect.

    Why it's wrong here

    An incorrect output destination would cause the monitoring job to fail, not generate violations.

Common exam traps

Common exam trap: answer the scenario, not the keyword

A common misconception is that frequent monitoring schedules cause violations, but in reality violations stem from baseline-production mismatch, not from the monitoring frequency itself.

Trap categories for this question

  • Command / output trap

    An incorrect output destination would cause the monitoring job to fail, not generate violations.

Detailed technical explanation

How to think about this question

Under the hood, SageMaker Model Monitor uses a baseline job to generate statistics (e.g., mean, stddev) and constraints (e.g., min, max, allowed range) from the training dataset. During monitoring, each production batch is compared against these constraints using a statistical test like z-score or chi-squared. If the baseline is unrepresentative—for example, trained on clean data while production contains missing values or outliers—the monitor will flag violations even when the production data is valid. In practice, teams should use a representative sample of production data for the baseline, or periodically retrain the baseline.

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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

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.

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The baseline was created from a dataset that does not represent production data. — Option A is correct because SageMaker Model Monitor compares production data against a baseline statistics and constraints file. If the baseline was created from a training dataset that does not reflect the actual distribution, patterns, or schema of production data, the monitor will flag frequent violations. This is the most common cause of false-positive alerts in data quality monitoring.

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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Last reviewed: Jul 4, 2026

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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.