MLA-C01 Practice Test 28 — 15 Questions

Question 1

A team is preparing text data for a natural language processing (NLP) model. They have a corpus of customer reviews. Which THREE preprocessing steps are essential to reduce noise and improve model performance?

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

Accepted Answer

Remove punctuation and special characters. Option B is correct because removing punctuation and special characters eliminates non-linguistic symbols that add noise and are typically not useful features for NLP models, helping normalize the token stream. Option D is correct because stemming or lemmatization reduces inflected words to their base or dictionary form (e.g., 'running' to 'run'), decreasing vocabulary size and helping the model generalize across morphological variants. Option E is correct because converting all text to lowercase ensures that words like 'Review' and 'review' are treated as the same token, preventing spurious vocabulary duplication and improving consistency. Option A is not a noise-reduction preprocessing step; one-hot encoding is a feature representation technique that actually increases dimensionality and does not clean the text. Option C is also not a preprocessing cleaning step; TF-IDF is a numerical vectorization/weighting method applied after text has already been normalized and tokenized.

Answer

Apply one-hot encoding to each word

Answer

Compute TF-IDF vectors

Question 2

A data scientist needs to version and manage multiple models for a team of five. The team frequently experiments with different algorithms and hyperparameters. They need a centralized registry to store, deploy, and compare model versions. Which AWS service should the data scientist use?

Accepted Answer

Use Amazon SageMaker Model Registry.. Amazon SageMaker Model Registry is the correct choice because it provides a centralized repository specifically designed for cataloging, versioning, approving, and deploying machine learning models. It integrates natively with SageMaker pipelines and endpoints, enabling the team to compare model versions, manage metadata (e.g., hyperparameters, metrics), and promote models through stages (e.g., from staging to production) with approval workflows.

Answer

Store each model artifact in Amazon S3 with manual versioning in the key name.

Answer

Use AWS Config to track model version changes.

Answer

Use AWS CodeArtifact to store model packages.

Question 3

A company wants to deploy a new model using a canary deployment strategy on SageMaker. Which two actions should they take? (Select TWO.)

Accepted Answer

Create a new endpoint with two production variants. Option C is correct because a SageMaker canary deployment is implemented by creating an endpoint configuration with two production variants (the existing/stable model and the new model) and deploying them behind a single endpoint, which is the foundational mechanism for splitting traffic between versions. Option E is correct because the canary pattern requires assigning initial traffic weights to those variants—typically a small percentage (e.g., 5%) to the new model and the remainder (e.g., 95%) to the stable model—so the new model receives limited live traffic before being promoted. Option A is not required for the deployment itself; Model Registry 'Approved' status is a governance/approval step, not a technical prerequisite for creating canary variants. Option B is not required because Model Monitor is used for detecting data/quality drift and bias, not for comparing model performance during a canary rollout. Option D is not required because data capture is an optional monitoring feature that logs request/response data, not a necessary action to perform a canary deployment.

Answer

Register both models in the Model Registry with 'Approved' status

Answer

Use SageMaker Model Monitor to compare model performance

Answer

Enable data capture on the endpoint

Question 4

A data engineer is ingesting streaming clickstream data from a website into Amazon S3 for ML training. The data arrives at a rate of 10,000 events per second, and the team needs near-real-time availability with minimal transformation. Which AWS service should the engineer use to ingest the data into S3 with the LEAST operational overhead?

Accepted Answer

Amazon Kinesis Data Firehose with S3 as the destination. Amazon Kinesis Data Firehose is a fully managed service that ingests streaming data and delivers it directly to destinations like Amazon S3 with built-in buffering, compression, and optional transformation. It requires no consumer application to write or manage, so operational overhead is minimal. It easily handles 10,000 events per second and provides near-real-time delivery to S3.

Answer

Amazon Kinesis Data Streams with a custom consumer application writing to S3

Answer

AWS Glue ETL job running on a schedule to pull data from a message queue

Answer

AWS Database Migration Service (DMS) with S3 as target

Question 5

A data science team has trained a model using SageMaker and wants to deploy it to a production endpoint with automatic scaling based on request volume. Which SageMaker feature should they use to configure scaling?

Accepted Answer

SageMaker Endpoint Autoscaling. SageMaker Endpoint Autoscaling is the correct feature because it automatically adjusts the number of instances behind a SageMaker hosted endpoint based on a target metric (e.g., requests per minute, CPU utilization) using Application Auto Scaling. This allows the endpoint to handle varying request volumes without manual intervention, ensuring cost efficiency and performance.

Answer

SageMaker Debugger

Answer

SageMaker Model Registry

Answer

SageMaker Pipelines

Question 6

A data scientist wants to version and manage trained models, require approval before deployment, and enable cross-account deployment. Which SageMaker feature provides these capabilities?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it provides a centralized catalog for versioning trained models, supports approval workflows (e.g., pending, approved, rejected) to gate deployment, and enables cross-account deployment by sharing model package ARNs across AWS accounts via AWS Resource Access Manager (RAM) or cross-account IAM roles. This directly satisfies all three requirements: versioning, approval before deployment, and cross-account deployment.

Answer

SageMaker Neo

Answer

SageMaker Pipelines

Answer

Amazon Elastic Inference

Question 7

A machine learning team needs to ensure that all model training and inference jobs within SageMaker Studio run in a private network without internet access. The team also requires that inter-container traffic within the same training job be encrypted. Which configurations should they combine?

Accepted Answer

Enable network isolation mode and inter-container traffic encryption. To run SageMaker jobs in a private network without internet access, you enable network isolation mode, which prevents containers from accessing the internet. To encrypt inter-container traffic within the same training job, you enable inter-container traffic encryption. These two configurations together meet both requirements.

Answer

Configure SageMaker Studio in VPC-only mode and use KMS encryption

Answer

Use a VPC with a NAT gateway and enable network isolation

Answer

Enable inter-container traffic encryption and use a VPC with VPC endpoints

Question 8

A data engineer needs to ingest streaming data from IoT devices into Amazon S3 for machine learning. The data arrives continuously and must be available for querying within minutes. Which service should be used to collect and deliver the streaming data to S3?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to ingest streaming data in real time and automatically deliver it to destinations like Amazon S3 with near-real-time latency (typically 60 seconds minimum buffering). It handles data transformation, compression, and partitioning without requiring custom code, making it ideal for continuously arriving IoT data that must be queryable within minutes.

Answer

AWS Database Migration Service (DMS)

Answer

AWS Glue ETL job triggered by an event

Answer

Amazon S3 Transfer Acceleration

Question 9

A team deploys a machine learning model using an Amazon SageMaker endpoint. They need to monitor for data drift and model quality issues. Which AWS services or features should they use? (Choose THREE.)

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Model Monitor (E) is the purpose-built feature for continuously monitoring deployed SageMaker endpoints, detecting data drift, model quality degradation, bias drift, and feature attribution drift by comparing live traffic against a baseline. Amazon SageMaker Clarify (B) provides bias detection and explainability (SHAP-based feature attributions), and its bias/explainability metrics are integrated with Model Monitor to surface those drift and fairness issues. Amazon CloudWatch Logs and Metrics (D) capture the endpoint's invocation logs and metrics that Model Monitor analyzes and that trigger CloudWatch alarms when violations are detected. AWS Glue DataBrew (A) is a visual data preparation tool for cleaning and transforming datasets, not for monitoring live endpoints, and SageMaker Ground Truth (C) is a data labeling service for building training datasets, so neither addresses drift or model quality monitoring.

Answer

AWS Glue DataBrew

Answer

Amazon SageMaker Ground Truth

Question 10

A company ingests daily log data into an S3 bucket. They need to update the existing ML training dataset with new data without reprocessing the entire history. Which two strategies should they adopt? (Choose two.)

Accepted Answer

Use AWS Glue to incrementally process new partitions. Option B is correct because AWS Glue can perform incremental processing by reading only newly added partitions (using job bookmarks and partition predicates) rather than reprocessing the full history, which directly satisfies the requirement to update the dataset without re-running the entire pipeline. Option C is correct because partitioning the S3 data by a key such as date lets new daily log data land in a new partition, so downstream training jobs or Glue ETL can scan only the new partition and append it to the existing dataset. Option A is incorrect because S3 objects are immutable and cannot be appended to in place; a single large file would require rewriting the whole object. Option D is incorrect because manually copying files into the same bucket does not provide incremental processing logic or partition awareness, and is error-prone. Option E is incorrect because overwriting the entire dataset forces full reprocessing, which is exactly what the scenario wants to avoid.

Answer

Store all data in a single large file and use append operations

Answer

Manually copy new files to the same S3 bucket

Answer

Overwrite the entire existing dataset with the new data

Question 11

A company runs an online retail business and wants to build a product recommendation system. They have a dataset of customer purchases stored in Amazon S3 as CSV files. The dataset includes columns: 'customer_id', 'product_id', 'purchase_date', 'quantity', 'price', and 'category'. The data science team plans to use Amazon SageMaker to train a factorization machines model. During data exploration, they discover that the 'category' column has 1,200 unique values, and many categories appear only a few times. The 'product_id' column has 50,000 unique values. They want to include both features in the model. The team is concerned about the high cardinality of these features. Which approach should they take to prepare these features for the factorization machines model?

Accepted Answer

Encode both columns as integer indices and feed them directly to the factorization machines algorithm as categorical features.. Amazon SageMaker's factorization machines algorithm natively supports categorical features encoded as integer indices (0-based). This avoids the explosion of features from one-hot encoding (which would create 51,200 columns) and leverages the algorithm's ability to learn interactions between high-cardinality features via factorized parameters, making it both memory-efficient and effective for sparse data.

Answer

Apply one-hot encoding to both 'product_id' and 'category' columns.

Answer

Drop the 'category' column and only use 'product_id' since it has more granularity.

Answer

Apply principal component analysis (PCA) to reduce the dimensionality of the categorical features.

Question 12

A company has a SageMaker real-time endpoint that serves predictions. They want to set up automated monitoring and remediation for when the number of 5XX errors exceeds a threshold. Which TWO steps should they take? (Choose TWO.)

Accepted Answer

Configure the CloudWatch Alarm to publish to an SNS topic. A CloudWatch Alarm on the 5XXError metric can be configured to publish to an SNS topic, enabling automated notifications or remediation actions (e.g., via Lambda) when the alarm state is triggered. This is the standard AWS approach for alerting on endpoint errors without custom polling.

Answer

Use SageMaker Model Monitor to detect 5XX errors

Answer

Set up a scheduled EventBridge rule to check 5XXError every minute

Answer

Write a custom script on EC2 to poll the endpoint and check for errors

Question 13

A data scientist is evaluating a binary classification model. They have the confusion matrix and want to assess the model's performance comprehensively. Which THREE metrics should they consider? (Select THREE.)

Accepted Answer

Precision. Precision (A) is correct because it measures the proportion of positive predictions that are actually correct (TP / (TP + FP)), which is essential for evaluating a binary classifier's reliability on predicted positives. Recall (C) is correct because it measures the proportion of actual positives that were correctly identified (TP / (TP + FN)), capturing the model's ability to find all relevant cases. F1 score (D) is correct because it is the harmonic mean of precision and recall (2 × (Precision × Recall) / (Precision + Recall)), giving a balanced single metric when both false positives and false negatives matter. RMSE (B) is not appropriate here because it is a regression error metric measuring the square root of the average squared difference between predicted and actual continuous values, not classification outcomes. R² (E) is also a regression metric that quantifies the proportion of variance explained in a continuous target, so it does not apply to a binary classification confusion matrix.

Answer

RMSE

Answer

R²

Question 14

A company is using SageMaker to train a model for image classification. The training dataset contains 100,000 labeled images. The team wants to use a pre-trained model to reduce training time. Which SageMaker feature should they use?

Accepted Answer

SageMaker JumpStart. SageMaker JumpStart provides pre-trained, publicly available foundation models and task-specific models (including image classification) that can be fine-tuned on your dataset, dramatically reducing training time and data requirements. For a team wanting to leverage a pre-trained model for image classification on 100,000 labeled images, JumpStart is the purpose-built feature that offers one-click deployment and fine-tuning of pre-trained models.

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker built-in Image Classification algorithm

Question 15

A machine learning team needs to monitor a deployed model for both data drift and concept drift. Which TWO approaches should they implement? (Select TWO.)

Accepted Answer

Set up SageMaker Model Monitor for data quality monitoring. Option A is correct because SageMaker Model Monitor's data quality monitoring detects data drift by comparing the statistical properties of incoming inference requests against the baseline statistics captured from the training dataset, alerting when feature distributions shift. Option D is correct because Model Monitor's model quality monitoring evaluates concept drift by comparing predicted values against actual ground-truth labels, tracking metrics such as accuracy, precision, and recall over time to detect degradation in the model's real-world performance. Option B is not correct because SageMaker Clarify is used for bias detection and explainability (e.g., SHAP values), not for detecting data or concept drift. Option C is not correct because CloudWatch Logs Insights merely queries and analyzes log data; it does not provide built-in drift detection statistics or baseline comparisons. Option E is not correct because SageMaker Debugger is designed to debug training jobs by capturing tensors and monitoring training metrics, not to monitor deployed models for drift.

Answer

Use SageMaker Clarify for bias monitoring

Answer

Configure CloudWatch Logs Insights to query inference logs

Answer

Enable SageMaker Debugger during inference