MLA-C01 Practice Test 43 — 15 Questions

Question 1

A machine learning engineer is preparing a dataset for training a SageMaker model. The dataset contains missing values in several numerical features. The engineer wants to handle these missing values during the training pipeline. Which two methods are valid ways to handle missing values in SageMaker? (Choose two.)

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

Accepted Answer

Use SageMaker Data Wrangler to impute missing values with the mean or median.. SageMaker Data Wrangler offers built-in transformations to impute missing values, and the built-in XGBoost algorithm natively handles missing values by learning default directions. Both are valid methods for dealing with missing data in a SageMaker training pipeline. The other services are not designed for data preprocessing.

Answer

Use SageMaker Clarify to replace missing values with the mode of each column.

Answer

Use SageMaker Debugger to automatically fill missing values during training.

Answer

Configure SageMaker Automatic Model Tuning to impute missing values via hyperparameter search.

Question 2

A company is training a deep learning model with SageMaker and wants to reduce training time by using pipe mode instead of file mode for a large dataset stored as TFRecord files in Amazon S3. After switching the estimator's input mode to Pipe, the training job fails immediately with a dataset format error. The data scientist confirms the files are valid TFRecords and that the same script works with File mode. What is the most likely cause?

Accepted Answer

The training script is still trying to read files from the local filesystem path instead of consuming the named pipe provided by SageMaker.. Pipe mode exposes training data as a named pipe rather than as files on disk. A script written for File mode typically lists files and opens them by path, which fails when SM_CHANNEL_TRAIN points to a FIFO. To use Pipe mode, the script must consume the stream sequentially, for example with a framework reader designed for pipes. Format conversion and Region settings are unrelated to this error.

Answer

The S3 bucket and the training job are in different AWS Regions, so Pipe mode cannot stream the objects across Regions.

Answer

Pipe mode only supports RecordIO-encoded data for built-in algorithms, so TFRecord files must be converted to RecordIO before use.

Answer

The estimator is missing the enable_network_isolation parameter, which is required for Pipe mode to establish the streaming connection to S3.

Question 3

A data engineer is building an AWS Glue ETL job that reads raw JSON clickstream events from Amazon S3, flattens nested structures, and writes Parquet to a curated S3 prefix for SageMaker training. The job must run daily on only the newly arrived files and must keep the Glue Data Catalog table current so Athena and SageMaker can query it. Which TWO configurations should the engineer apply to meet these requirements? (Choose two.)

Accepted Answer

Enable job bookmarks on the Glue ETL job.. Job bookmarks give Glue the persistent state needed to process only newly arrived S3 objects, which is exactly the incremental requirement. Keeping the Data Catalog current, either through the job's catalog update option or a crawler over the curated prefix, ensures Athena and SageMaker see the latest schema and partitions. Together these two settings make the daily pipeline both efficient and queryable.

Answer

Convert the input JSON to CSV before processing to improve Glue read performance.

Answer

Set the job's maximum capacity to the smallest possible value to reduce cost.

Answer

Use a Development endpoint to run the daily job interactively.

Question 4

A machine learning engineer is using a SageMaker training job with a custom training script. They need to save the trained model artifacts to Amazon S3 so that the model can be deployed later. Which parameter in the SageMaker estimator should they configure to specify the S3 location for model artifacts?

Accepted Answer

output_path. The output_path parameter in the SageMaker estimator defines the S3 URI where training job artifacts, including the final model, are stored. It allows control over the bucket and prefix, which is essential for governance and deployment. Other parameters like model_dir, code_location, and dependencies serve different purposes in the training lifecycle.

Answer

code_location

Answer

model_dir

Answer

dependencies

Question 5

A machine learning engineer is training a deep learning model on SageMaker using the PyTorch estimator. The training job fails with an error indicating that the GPU memory is exhausted. The engineer wants to reduce memory usage without changing the model architecture. Which SageMaker feature should the engineer use?

Accepted Answer

SageMaker model parallelism. SageMaker model parallelism partitions a large model across multiple GPUs, reducing the memory required on each GPU. This directly addresses GPU memory exhaustion without changing the model architecture. Data parallelism replicates the model, which does not help with memory constraints, and the other services are for monitoring or tuning, not memory reduction.

Answer

SageMaker data parallelism

Answer

SageMaker Debugger

Answer

SageMaker Automatic Model Tuning

Question 6

A machine learning engineer is preparing a dataset for training a SageMaker model. The dataset contains missing values in several numerical features. The engineer wants to handle these missing values during the training pipeline. Which two methods are valid ways to handle missing values in SageMaker? (Choose two.)

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

Accepted Answer

Use a framework-provided dataset or iterator that reads from the path in SM_CHANNEL_TRAIN and yields batches lazily during training.. SageMaker downloads the contents of each training channel to a local path and exposes that path through the SM_CHANNEL_TRAIN environment variable. Using a framework-native dataset or iterator that reads lazily from that path streams batches on demand, so memory stays bounded regardless of dataset size. Direct S3 reads, EFS mounts, and volume resizing do not address in-memory loading.

Answer

Set the estimator's volume_size parameter large enough to hold the dataset and enable cacheutils to copy the data into the container's memory before training starts.

Answer

Read the dataset directly from the Amazon S3 URI passed in the SM_CHANNEL_TRAIN environment variable using the AWS SDK for Python (Boto3) inside the training loop.

Answer

Configure the estimator with an Amazon EFS file system and mount the dataset into the container, then use memory-mapped file access for all training samples.

Question 7

A machine learning engineer is using SageMaker Pipelines to orchestrate a training workflow. The pipeline includes a processing step that outputs a dataset, which is then used by a training step. The engineer notices that the processing step runs every time the pipeline executes, even when the input data has not changed. The engineer wants to avoid re-running the processing step if the input data and code are unchanged, while ensuring that downstream steps still execute if the processing step is skipped. Which approach should the engineer take?

Accepted Answer

Enable caching on the processing step by setting the cache policy to CacheConfig with a time-to-live (TTL) and a cache key that includes the input data S3 URI and the processing script's S3 URI.. SageMaker Pipelines supports step caching, where the cache key is derived from the step's input artifacts and parameters. By configuring a cache policy with a TTL on the processing step and ensuring the cache key includes the input data URI and script URI, the step is skipped when these inputs are unchanged. The cached outputs are then used by downstream steps, maintaining pipeline integrity.

Answer

Use a condition step to check if the input data has changed, and if not, skip the processing step and directly pass the previous output to the training step.

Answer

Configure the processing step to use a spot instance, which will reduce cost but not affect whether the step runs.

Answer

Set the pipeline's execution mode to 'Reprocess' and manually skip the processing step by editing the pipeline definition before each run.

Question 8

A data scientist has a 40 GB CSV dataset in Amazon S3 that will be used to train a SageMaker model. The training script reads the data with pandas, and the scientist wants to reduce both storage cost and training-time I/O without changing the logical schema. Which data preparation action should be taken?

Accepted Answer

Convert the dataset to Parquet with Snappy compression and keep the same columns.. Parquet stores data by column and compresses each column efficiently, so a training script that needs a subset of columns reads far fewer bytes than it would from CSV. Snappy compression further reduces the S3 object size, lowering storage cost. Because the column names and types stay the same, the logical schema is unchanged and the pandas-based script can read the Parquet files with minimal modification.

Answer

Move the dataset to Amazon EFS and mount it to the training container.

Answer

Convert the dataset to JSON Lines and enable S3 Transfer Acceleration.

Answer

Split the CSV into many smaller CSV files and keep the same column layout.

Question 9

A data engineer needs to prepare a dataset for a fraud detection model. The dataset contains a highly skewed numerical feature with extreme outliers. The engineer decides to apply a logarithmic transformation to this feature before training. Which SageMaker Data Wrangler transform should be used to apply the logarithmic transformation?

Accepted Answer

Use the 'Log Transform' transform in Data Wrangler.. The logarithmic transformation is a common technique to reduce right skewness and stabilize variance in numerical data. In SageMaker Data Wrangler, the 'Log Transform' transform applies a natural logarithm to the selected column, effectively compressing the scale of large values and making the distribution more symmetric. This helps models that are sensitive to feature distributions, such as linear models, to perform better. Other transforms like scaling or encoding do not change the distribution shape.

Answer

Use the 'Standard Scaler' transform in Data Wrangler.

Answer

Use the 'One-Hot Encoding' transform in Data Wrangler.

Answer

Use the 'Min-Max Scaler' transform in Data Wrangler.

Question 10

A machine learning engineer is using SageMaker to train a model and wants to automatically stop a training job when the validation loss has not improved for 10 consecutive epochs, while still saving the best model artifacts. The engineer is using the SageMaker training toolkit in a custom container. Which combination of actions should the engineer take?

Accepted Answer

Implement early stopping in the training script by tracking validation loss and calling the SageMaker training toolkit's save_model or exiting the loop when the patience threshold is reached.. Early stopping based on validation loss patience is logic that belongs in the training script when using a custom container. The script should evaluate validation loss each epoch, track the best value, and stop after 10 epochs without improvement, saving the best model artifacts. Estimator time limits, tuning strategies, and Debugger rules do not implement this per-epoch behavior.

Answer

Configure the estimator's stopping condition with the MaxRuntimeInSeconds parameter and set the checkpoint_s3_uri to save intermediate models.

Answer

Use SageMaker Automatic Model Tuning with the Bayesian strategy and set the max_parallel_jobs parameter to 1 to enable early stopping across trials.

Answer

Enable SageMaker Debugger with the vanishing_gradient rule and configure a stop condition on the rule to halt training when validation loss plateaus.

Question 11

A data scientist is using SageMaker to train a linear regression model on a dataset with a large number of features. They notice that the model's training time is long and want to speed it up by using a more efficient algorithm. They decide to use the SageMaker built-in Linear Learner algorithm. Which of the following is a key advantage of using the Linear Learner algorithm in SageMaker for this scenario?

Accepted Answer

It supports both regression and classification and can handle large-scale datasets efficiently using distributed training.. SageMaker Linear Learner is optimized for large-scale linear models and supports both regression and classification. It can be trained in distributed mode across multiple instances, which significantly reduces training time for datasets with many features. This makes it a suitable choice for the data scientist's scenario.

Answer

It uses a built-in automatic model tuning feature that always finds the optimal hyperparameters without user intervention.

Answer

It automatically performs feature engineering and selection, reducing the need for manual preprocessing.

Answer

It is specifically designed for deep learning models and can leverage GPUs for faster training.

Question 12

A data scientist wants to track the training and validation accuracy of a SageMaker training job over time. They need to visualize these metrics in Amazon CloudWatch. Which action should they take?

Accepted Answer

Use the SageMaker estimator's metric_definitions parameter to specify regex patterns that extract metrics from the training logs.. The metric_definitions parameter in the SageMaker estimator allows you to define regex patterns that extract metric values from the training logs. SageMaker then automatically publishes these as CloudWatch metrics, enabling visualization. This is the recommended and simplest approach for tracking standard training metrics.

Answer

Configure the training job to write metrics directly to Amazon CloudWatch using the AWS SDK for Python (Boto3) in the training script.

Answer

Write the metrics to a file in the output S3 bucket and create a CloudWatch dashboard from the file.

Answer

Enable SageMaker Debugger and configure rules to emit metrics to CloudWatch.

Question 13

A data scientist is preparing a dataset stored in Amazon S3 for a SageMaker training job. The dataset contains missing values in several columns. The scientist wants to impute missing values with the mean of each column. Which SageMaker built-in algorithm or processing method should be used to perform this imputation efficiently?

Accepted Answer

Use the SageMaker 'Impute' transform in Data Wrangler.. Mean imputation is a common preprocessing step for numerical features. In SageMaker Data Wrangler, the 'Impute' transform provides a user-friendly way to replace missing values with the column mean, median, or mode. This transform can be applied to multiple columns and is part of the data preparation flow. It ensures that the dataset is complete before feeding it into a training algorithm, which is essential for algorithms that cannot handle missing values natively.

Answer

Use the SageMaker 'PCA' algorithm to fill missing values.

Answer

Use the SageMaker 'Linear Learner' algorithm to predict missing values.

Answer

Use the SageMaker 'XGBoost' algorithm to predict missing values.

Question 14

A machine learning engineer is using SageMaker Automatic Model Tuning (AMT) to optimize a model. They want to ensure the tuning job explores the hyperparameter search space efficiently and stops poorly performing trials early. Which two strategies should they use? (Choose two.)

Accepted Answer

Enable early stopping with the Hyperband strategy to terminate underperforming trials.. Bayesian optimization efficiently models the objective function to select promising hyperparameters, while Hyperband early stopping terminates underperforming trials to save resources. Together, they maximize tuning efficiency. The other options either increase cost, use less efficient search, or do not address early stopping.

Answer

Define hyperparameters as categorical with a large number of discrete values to increase granularity.

Answer

Set the maximum number of training jobs to a very high value to ensure thorough exploration.

Answer

Use random search instead of Bayesian optimization to cover the search space uniformly.

Question 15

A data scientist is training a model with SageMaker and needs to reduce the cost of a long-running training job that can tolerate interruptions. The job uses a custom training script and reads data from Amazon S3. The data scientist wants the job to resume from the last saved state if the underlying compute is reclaimed. (Choose two.)

Accepted Answer

Configure the estimator to use managed spot training and set max_wait time greater than max_run time.. Managed spot training lowers cost by using Spot Instances, and checkpointing to Amazon S3 lets a restarted job resume from the last saved state. Setting max_wait above max_run gives the job time to wait for capacity and to recover from interruptions. Network isolation, larger volumes, and restarting from scratch do not support the resume requirement and can increase cost or break access.

Answer

Set the estimator's enable_spot_training parameter to true and rely on SageMaker to automatically restart the job from the beginning after an interruption.

Answer

Increase the volume_size parameter so that the container's local disk can hold the entire dataset and all checkpoints during training.

Answer

Enable network isolation on the estimator to prevent the training container from accessing the internet during spot interruptions.