MLA-C01 Practice Test 23 — 15 Questions

Question 1

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. They need to identify potential bias in the data before training. Which SageMaker feature should they use?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify is the correct feature because it is specifically designed to detect bias in datasets and machine learning models. It provides built-in bias metrics (e.g., pre-training bias) and can generate bias reports during data preparation, directly addressing the need to identify potential bias before training.

Answer

Amazon SageMaker Model Monitor

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Amazon SageMaker Debugger

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Amazon SageMaker Pipelines

Question 2

A data engineer needs to convert a JSON dataset to Parquet format for efficient querying with Amazon Athena. The JSON files are in an S3 bucket. Which service can perform this conversion with minimal coding?

Accepted Answer

AWS Glue Studio with a visual job. AWS Glue Studio with a visual job is the correct choice because it provides a no-code, drag-and-drop interface to create ETL jobs that can read JSON from S3 and write it as Parquet, with built-in schema inference and transformation capabilities. This minimizes coding effort while leveraging Glue's serverless Spark engine for efficient conversion, making it ideal for preparing data for Athena queries.

Answer

Amazon SageMaker Processing

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Amazon EMR

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AWS Lambda

Question 3

A team is using SageMaker Pipelines to automate retraining and deployment. They want to trigger the pipeline automatically when new training data is available in an S3 bucket. Which approach should they use?

Accepted Answer

Create an Amazon EventBridge rule that triggers the pipeline execution on S3 PutObject events. Amazon EventBridge can directly capture S3 PutObject events and invoke a SageMaker Pipeline execution as a target. This provides a fully event-driven, serverless integration without polling or manual intervention, aligning with best practices for automating ML workflows when new data arrives.

Answer

Register the pipeline as a model package in SageMaker Model Registry

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Configure a cron job to run the pipeline every hour

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Use AWS Step Functions to poll the S3 bucket and start the pipeline when a new object appears

Question 4

A machine learning engineer is using Lasso regression for feature selection. After training, many coefficients become zero. The engineer notices that some features with high mutual information with the target also have zero coefficients. What is the most likely reason?

Accepted Answer

The features are highly correlated with each other. Lasso regression with L1 regularization tends to select a subset of features by shrinking some coefficients to zero. When features are highly correlated, Lasso arbitrarily picks one and zeros out the others, even if they have high mutual information with the target. This is a known property of L1 regularization. Option A is correct. Option B (features not normalized) is less likely because normalization affects coefficient magnitude but does not cause zeroing due to correlation. Option C (regularization parameter too low) would result in fewer zero coefficients, not more. Option D (high variance) does not directly cause zero coefficients.

Answer

The features are not normalized

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The regularization parameter lambda is too low

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The features have high variance

Question 5

A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively exploring the hyperparameter space. Which approach should they use?

Accepted Answer

Bayesian optimization. Bayesian optimization is the correct approach because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next, balancing exploration and exploitation. This method converges to optimal hyperparameters in fewer iterations than random or grid search, significantly reducing training time for expensive neural network models.

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Random search

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Grid search

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Manual tuning

Question 6

A machine learning engineer is using Amazon SageMaker Data Wrangler to create a data preparation pipeline. The pipeline includes multiple transforms such as handling missing values, scaling, and encoding. The engineer wants to export the prepared data directly to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should the engineer choose?

Accepted Answer

Export to a feature group in SageMaker Feature Store.. Amazon SageMaker Data Wrangler provides a built-in export destination for SageMaker Feature Store, allowing you to directly write the transformed data to a feature group without additional code. This enables seamless reuse of the prepared features for both training and real-time inference, leveraging the Feature Store's low-latency retrieval and versioning capabilities.

Answer

Export to Amazon S3 as a CSV file.

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Export to a Jupyter notebook for further processing.

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Export to Amazon Redshift for analysis.

Question 7

A company wants to deploy a trained XGBoost model for batch inference on a large dataset stored in S3. The inference job should be cost-effective and does not require real-time responses. Which SageMaker inference option should they use?

Accepted Answer

SageMaker Batch Transform. SageMaker Batch Transform is designed for offline, high-throughput inference on large datasets stored in S3, with no persistent endpoint and no real-time requirement. It is the most cost-effective option for this scenario because you pay only for the duration of the batch job.

Answer

SageMaker real-time endpoint

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SageMaker Asynchronous Inference

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SageMaker Serverless Inference

Question 8

A machine learning engineer is using SageMaker Autopilot for AutoML. Which TWO outputs does Autopilot produce?

Accepted Answer

An ensemble of candidate models. SageMaker Autopilot produces an ensemble of candidate models (B), because it automatically explores multiple algorithms and hyperparameter configurations and then combines the best-performing candidates into an ensemble for deployment. It also produces an explainability report (E), which provides feature importance and model insights so users can understand how the model makes predictions. Autopilot does not output a hyperparameter tuning job summary (A); while it performs hyperparameter optimization internally, the deliverable is not a tuning job summary. It does not create a data labeling pipeline (C), since Autopilot assumes labeled tabular data and does not manage annotation workflows. It also does not produce only a single optimal model (D), because its output includes multiple candidates and an ensemble rather than just one model.

Answer

A hyperparameter tuning job summary

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A data labeling pipeline

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A single optimal model

Question 9

A data scientist is preparing text data for a sentiment analysis model using Amazon SageMaker. Which two data preprocessing techniques are commonly used when working with text data for natural language processing? (Choose two.)

Accepted Answer

Tokenization. Tokenization (C) is correct because NLP models require raw text to be split into individual tokens (words, subwords, or characters) so they can be mapped to numerical vectors before being fed into a sentiment analysis model in SageMaker. Stop word removal (E) is correct because eliminating high-frequency, low-information words such as 'the', 'is', and 'and' reduces noise and dimensionality, helping the model focus on sentiment-bearing terms. One-hot encoding of all words (A) is not a common standalone preprocessing technique for text at scale, since it produces extremely sparse, high-dimensional vectors and ignores word order and semantics; embeddings are typically preferred. Image resizing (B) applies to computer vision data, not text. Principal component analysis (D) is a dimensionality reduction technique for numerical feature matrices, not a standard text preprocessing step for NLP.

Answer

One-hot encoding of all words

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Image resizing

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Principal component analysis (PCA)

Question 10

A company is deploying a machine learning model using SageMaker hosting. They need to support multiple versions of the model for A/B testing. Which TWO actions are required to set up the A/B test? (Choose two.)

Accepted Answer

Configure the endpoint to route a percentage of traffic to each variant using initial variant weight. Option E is correct because a SageMaker A/B test requires a single real-time endpoint that hosts two (or more) production variants, each pointing to a different model version, so live traffic can be split between them. Option C is correct because the traffic split is controlled by assigning each production variant an initial variant weight (a percentage of invocations), which the endpoint uses to route requests proportionally between the variants. Together, creating the multi-variant endpoint and configuring the initial variant weights are the required steps to run the A/B test. Option A is not required because shadow variants only mirror traffic to a variant without returning its response to users, which is for testing without impacting users rather than A/B comparison. Option B is not required because a batch transform job performs offline inference and does not route live endpoint traffic. Option D is not required because registering models in the Model Registry is a governance/versioning practice, not a prerequisite for configuring endpoint traffic splitting.

Answer

Enable shadow variants to capture traffic for the new model without affecting users

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Set up a batch transform job to compare performance offline

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Register both models in SageMaker Model Registry

Question 11

A company needs to secure a SageMaker notebook instance that contains sensitive data. Which THREE of the following are effective security measures? (Select THREE.)

Accepted Answer

Use IAM policies to restrict who can access the notebook instance.. Option A is correct because IAM policies are the primary mechanism for controlling authentication and authorization to SageMaker notebook instances, allowing you to restrict which principals can create, access, or manage the instance via fine-grained permissions. Option B is correct because disabling direct internet access forces all outbound traffic through your VPC, and a NAT gateway provides controlled, monitored egress while preventing the notebook from being directly reachable from the internet. Option E is correct because encrypting the notebook instance's EBS volume with AWS KMS protects data at rest, ensuring that sensitive data stored on the volume is unreadable if the underlying storage is compromised. Option C is not a security measure because downloading data from a public S3 bucket introduces unnecessary exposure and does not secure the notebook. Option D, while useful for auditing, is a detective control rather than a preventive security measure and does not by itself secure the sensitive data on the instance.

Answer

Attach a lifecycle configuration that runs a script to download data from a public S3 bucket.

Answer

Enable AWS CloudTrail to log all notebook API calls.

Question 12

A team is preparing text data for sentiment analysis. They have a large corpus of customer reviews. They want to convert the text into numerical features using a technique that captures word importance relative to the whole corpus. Which feature extraction method should they use?

Accepted Answer

TF-IDF. TF-IDF (Term Frequency–Inverse Document Frequency) weights each word by how often it appears in a document relative to how often it appears across the entire corpus, so common words like 'the' are down-weighted while distinctive words are up-weighted. This directly captures word importance relative to the whole corpus, which is exactly what the team needs for sentiment analysis feature extraction.

Answer

Word2Vec embeddings

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One-hot encoding

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Bag-of-words (CountVectorizer)

Question 13

A company requires that all SageMaker notebook instances be created within a private VPC without internet access. Which configuration step is mandatory?

Accepted Answer

Configure VPC settings when creating the notebook instance, choosing a private subnet.. When creating a SageMaker notebook instance, you must explicitly configure the VPC settings and select a private subnet to ensure the instance is launched within a private VPC without internet access. This is mandatory because SageMaker notebook instances, by default, are created with internet access enabled unless you specify a VPC with no direct internet access. Selecting a private subnet ensures the instance uses only VPC endpoints or NAT gateways for outbound traffic, meeting the no-internet requirement.

Answer

Use a SageMaker Studio notebook instead.

Answer

Enable SageMaker direct internet access.

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Assign a public IP to the notebook instance.

Question 14

A company is deploying a SageMaker real-time endpoint and needs to monitor inference latency. Which THREE metrics are available from SageMaker for this purpose? (Choose THREE.)

Accepted Answer

OverheadLatency. Option A, OverheadLatency, is correct because SageMaker publishes it as the time spent on overhead outside the model itself (for example, request routing and response processing) in the endpoint's invocation path, so it directly contributes to observed inference latency. Option C, ModelLatency, is correct because it measures the interval the container spends processing the request, which is the core inference latency component reported by SageMaker. Option E, Latency, is correct because SageMaker reports the total end-to-end time from when the request is received to when the response is returned, which is the primary inference latency metric for a real-time endpoint. Option B, Invocations, is not a latency metric; it counts the number of requests sent to the endpoint. Option D, MemoryUtilization, is a resource-utilization metric for the instance/container, not an inference latency measurement.

Answer

Invocations

Answer

MemoryUtilization

Question 15

A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)

Accepted Answer

Store training data in Amazon S3 in a shuffled and compressed format. Option A is correct because storing training data in Amazon S3 in a shuffled and compressed format reduces storage footprint and download time, and shuffling prevents order-dependent bias while improving I/O throughput during training. Option D is correct because SageMaker Managed Spot Training with checkpointing lets the team use discounted spare EC2 capacity and resume from the last checkpoint after an interruption, cutting cost without losing training progress. Option E is correct because Pipe mode streams data directly from S3 to the training container, avoiding the latency and disk overhead of downloading the full dataset as File mode does, which speeds up training for large datasets. Option B is not necessarily right because the largest instance type may be cost-inefficient or mismatched to the workload, and performance depends on proper resource sizing rather than simply maximizing instance size. Option C is not right because adding layers increases model complexity and training time and does not by itself optimize training performance.

Answer

Use the largest instance type available

Answer

Increase the number of layers in the model to improve accuracy