MLA-C01 Practice Test 11 — 15 Questions

Question 1

A retail company is preparing a dataset for a machine learning model to predict customer churn. The dataset includes customer_id, signup_date, last_purchase_date, total_purchases, average_order_value, and churn_label. The data scientist notices that the 'total_purchases' column has missing values for 15% of the records. The company wants to use AWS Glue for data preparation. Which approach should the data scientist take to handle the missing values while minimizing bias and preserving data integrity?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use AWS Glue DynamicFrame to perform model-based imputation, predicting missing total_purchases using other features like average_order_value and signup_date.. Model-based imputation uses relationships between features (e.g., average_order_value and signup_date) to predict missing total_purchases values, minimizing bias compared to simple mean/median imputation. AWS Glue DynamicFrames support custom transformation logic, allowing you to implement a predictive model (e.g., using Spark MLlib) directly within the Glue ETL job. This approach preserves data integrity by leveraging existing data patterns rather than discarding records or introducing arbitrary constants.

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Use AWS Glue DataBrew to fill missing values with the median of total_purchases.

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Drop all records with missing total_purchases values.

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Replace missing total_purchases with the mean of the non-missing values.

Question 2

A company is fine-tuning a large language model using LoRA on SageMaker. They want to reduce GPU memory usage during training. Which configuration change would help?

Accepted Answer

Use QLoRA (quantized LoRA) with 4-bit quantization. LoRA reduces trainable parameters, and when combined with QLoRA (quantized LoRA), it further reduces memory by quantizing the base model to 4-bit or 8-bit. Increasing batch size or sequence length typically increases memory usage. Gradient accumulation also increases memory as it requires storing gradients for multiple steps. QLoRA is specifically designed for memory reduction.

Answer

Enable gradient accumulation

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Increase the sequence length

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Increase the batch size

Question 3

A team is deploying a model using SageMaker Pipelines. They have defined a pipeline with steps: preprocessing, training, evaluation, and conditional registration. The evaluation step produces a JSON file with metrics. If accuracy > 0.9, the model is registered; else, the pipeline fails. Which TWO statements about this pipeline are correct? (Choose TWO.)

Accepted Answer

The evaluation step must output a JSON file in a specific format to be used by the condition step.. The SageMaker Pipelines condition step expects the evaluation step to output a JSON file with a specific format, typically containing a metrics dictionary. The condition step then uses a property file to extract the accuracy value from that JSON, enabling the conditional logic to evaluate whether accuracy > 0.9.

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The conditional step should be implemented as a separate Lambda function called from the pipeline.

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The pipeline will automatically retry the training step if the condition fails.

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The model registration step should be placed before the condition step to ensure the model is always registered.

Question 4

A data scientist wants to monitor a deployed model for performance degradation. Which TWO metrics from Amazon CloudWatch should they use to detect issues? (Select two.)

Accepted Answer

ModelLatency. (ModelLatency) is correct because it measures the time taken for the model to respond to inference requests, and a sudden increase in latency can indicate performance degradation due to resource contention, model drift, or infrastructure issues. Option D (Invocation5XXErrors) is correct because a rise in 5XX HTTP errors from the SageMaker endpoint signals that the model is failing to process requests, often due to out-of-memory errors, timeouts, or internal faults, directly reflecting degraded service health.

Answer

ModelQuality

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CpuUtilization

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InvocationCount

Question 5

A team is using SageMaker to train a distributed model with data parallelism. They notice that the training loss is not decreasing as expected and suspect a bug in the data loading pipeline. Which SageMaker Debugger feature can help them inspect the data distributions during training?

Accepted Answer

Custom rules to monitor input tensors. SageMaker Debugger can capture tensors (including inputs and outputs) during training. By saving input tensors, the team can inspect data distributions. Rules fire on issues like overfitting or dead relu, but tensor captures allow direct data inspection. SaveConfig defines which tensors to save. Using a different instance type does not help debug data issues.

Answer

SageMaker Model Monitor

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Built-in rules such as overfit detection

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SageMaker Processing jobs

Question 6

A data scientist is preparing a large dataset for training a machine learning model. The dataset contains missing values in several columns. Which approach is the MOST efficient for handling missing values in a large dataset using AWS services?

Accepted Answer

Use Amazon SageMaker Data Wrangler to impute missing values using built-in transforms.. Amazon SageMaker Data Wrangler provides a visual interface and built-in transforms for handling missing values efficiently at scale, without writing custom code. Glue ETL is more code-heavy, and imputation with pandas is not scalable for large datasets. Removing all rows with missing values is not always optimal and may not be efficient.

Answer

Use AWS Glue ETL to write a custom Python script that imputes missing values with the mean.

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Use pandas in a SageMaker notebook to impute missing values with the median.

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Remove all rows with missing values from the dataset.

Question 7

A company is using SageMaker to train a model for image classification. They have a dataset of 10,000 images. They use SageMaker's built-in image classification algorithm with transfer learning. During training, they notice that the training job completes successfully but the model accuracy on the validation set is very low (~30%). They suspect the model is underfitting. Which action is most likely to improve accuracy?

Accepted Answer

Increase the number of training epochs.. Underfitting occurs when the model has not learned enough from the training data, often because training was stopped too early. Increasing the number of training epochs allows the model more iterations to converge to a better solution, which directly addresses underfitting by giving the optimizer more time to minimize the loss function.

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Use a different algorithm.

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Add more layers to the model architecture.

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Use a smaller batch size.

Question 8

A machine learning engineer needs to handle missing values in a dataset containing numerical features. The missingness is completely at random (MCAR). Which imputation strategy is most robust for downstream model performance?

Accepted Answer

Impute with median of each feature. When missingness is completely at random (MCAR), imputing with the median is robust because it preserves the central tendency of the distribution without introducing bias or distorting variance. Unlike mean imputation, the median is resistant to outliers, making it a safe default for numerical features in downstream models that assume normally distributed inputs or are sensitive to skewed data.

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Impute with a constant like -1

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Use a model to predict missing values

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Remove all rows with missing values

Question 9

A data scientist is training a deep learning model on SageMaker and notices that the training loss oscillates and does not converge. They want to debug this issue. Which SageMaker feature can they use to monitor and analyze the training process?

Accepted Answer

SageMaker Debugger. SageMaker Debugger is the correct feature because it provides real-time monitoring and analysis of training metrics, including loss values, gradients, and weights. It can automatically detect issues like oscillating or non-converging loss by setting rules (e.g., loss not decreasing) and emit alerts or capture tensors for later analysis, directly addressing the data scientist's need to debug training instability.

Answer

SageMaker Profiler

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SageMaker Gradient Descent optimization

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SageMaker Automatic Model Tuning

Question 10

A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The data is stored in Amazon S3 in Parquet format. A data engineer notices that the Glue job is running slowly and consuming a lot of resources. What is the MOST cost-effective way to improve the performance of the Glue job?

Accepted Answer

Use the G.1X worker type, which provides more memory per worker compared to the Standard worker type.. Increasing the number of DPUs (Data Processing Units) in AWS Glue can improve parallelism and reduce job runtime, but it increases cost. Using G.1X worker type with more memory per worker can improve performance without increasing DPU count, offering better resource utilization. Switching to CSV may degrade performance. Using partition pruning on the source data can reduce data scanned but may not address resource consumption.

Answer

Use partition pruning on the source data to reduce the amount of data processed.

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Switch the output format from Parquet to CSV to reduce processing overhead.

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Use a larger instance type for the Glue job by increasing the number of DPUs.

Question 11

A data scientist notices that a production model's accuracy has degraded over the past week. The training data distribution remains unchanged, but the relationship between features and the target has shifted. Which type of drift is occurring, and which monitoring approach should be used?

Accepted Answer

Concept drift; use SageMaker Model Monitor model quality monitoring with ground truth labels. Concept drift occurs when the underlying relationship between features and target changes. Model quality monitoring (comparing predictions against ground truth) detects this. Data drift monitors feature distribution changes, which are not present here.

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Bias drift; use SageMaker Clarify post-deployment bias monitoring

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Data drift; use SageMaker Model Monitor data quality monitoring

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Feature attribution drift; use SageMaker Clarify

Question 12

A company deploys a model using SageMaker real-time endpoint with auto scaling. They observe that during a traffic spike, the endpoint quickly scales up to 10 instances, but after the spike, it takes a long time to scale down, leading to high costs. The scaling policy is based on a simple average CPU utilization threshold. Which adjustment would optimize the scaling down behavior?

Accepted Answer

Decrease the scale-in cooldown period to allow the endpoint to scale down faster when utilization drops.. Decreasing the scale-in cooldown period allows the endpoint to respond more quickly to sustained drops in CPU utilization. By default, SageMaker auto scaling uses cooldown periods to prevent rapid fluctuations; a long scale-in cooldown delays the termination of instances after utilization falls, keeping costs high. Reducing this cooldown lets the endpoint scale down faster when the spike subsides, directly addressing the problem.

Answer

Increase the scale-in cooldown period to prevent premature scale-down.

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Use a step scaling policy with a larger step adjustment for scale-in.

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Change the scaling policy to use memory utilization instead of CPU.

Question 13

A company wants to deploy a foundation model from SageMaker JumpStart with the lowest possible inference cost, given that latency requirements are flexible. They have a mix of traffic volumes. Which approach should they take?

Accepted Answer

Select the smallest instance type that meets throughput requirements and enable automatic scaling. SageMaker JumpStart provides pre-built models; for cost optimization, choosing the smallest suitable instance type and enabling auto-scaling based on demand reduces cost while handling varying traffic.

Answer

Use SageMaker Savings Plans to get a discount on on-demand instances

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Deploy the model on the largest GPU instance to handle peak load

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Deploy the model on a serverless inference endpoint

Question 14

A data scientist is using SageMaker built-in linear learner algorithm for a regression problem. The dataset has 10 features, some have missing values, and the target variable is right-skewed. The data scientist wants to handle missing values and transform the target variable to improve model performance. Which data preparation steps should the data scientist take?

Accepted Answer

Impute missing values with the median of each feature and apply a log transformation to the target variable.. Imputing missing values with the median is robust to outliers and preserves the distribution of each feature, which is important when the target is right-skewed. Applying a log transformation to the right-skewed target variable helps normalize its distribution, which aligns with the linear learner algorithm's assumption of normally distributed errors and improves convergence and prediction accuracy.

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Apply one-hot encoding to all features and remove missing values by dropping rows.

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Standardize all features to have zero mean and unit variance, then apply a box-cox transformation to the target.

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Remove rows with missing values and normalize the target to range [0,1].

Question 15

A company is training a deep learning model on Amazon SageMaker. The training job started but has been stuck in 'InProgress' state for an unusually long time with low CPU utilization. The data scientist suspects a bottleneck. What should be the first troubleshooting step?

Accepted Answer

Review CloudWatch Logs for the training container to identify errors or warnings.. When a SageMaker training job is stuck in 'InProgress' with low CPU utilization, the most common cause is a bottleneck in data loading or preprocessing within the training container. Reviewing CloudWatch Logs for the training container is the first troubleshooting step because it provides direct visibility into container-level errors, warnings, or stalls (e.g., hanging on a file read, waiting for a dependency, or a misconfigured data channel) that would not be visible from instance-level metrics alone.

Answer

Switch the training job to use Spot instances to reduce cost and potentially improve throughput.

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Increase the number of training instances to parallelize data loading.

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Stop and restart the training job with a different instance type.