MLA-C01 Practice Test 4 — 10 Questions

Question 1

A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?

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

Accepted Answer

Read the Parquet files directly using SparkSession.read.parquet. SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.

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Read the Parquet files as text using SparkContext.textFile

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Split the dataset into many small Parquet files (e.g., 1 MB each)

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Convert the Parquet files to CSV before processing

Question 2

A financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting model (XGBoost) trained on historical transaction data. The inference endpoint uses an ml.m5.2xlarge instance with a single variant. Recently, the company has experienced a 3x increase in transaction volume during peak hours, causing inference latency to exceed the 200ms SLA. The data science team has already optimized the model by reducing the number of trees and feature set, but the latency remains high during spikes. The team considers using SageMaker's built-in scaling policies. They currently have a single endpoint with one production variant. The team wants to maintain low latency without over-provisioning resources. They have ruled out model changes. Which approach should the team take?

Accepted Answer

Configure an Application Auto Scaling target tracking scaling policy for the variant based on the 'SageMakerVariantInvocationsPerInstance' metric, with a target value that keeps the inference latency within the SLA.. SageMaker's built-in target tracking scaling policy using the 'SageMakerVariantInvocationsPerInstance' metric allows the endpoint to automatically adjust the instance count based on real-time invocation load. By setting a target value that correlates with the 200ms SLA, the policy dynamically scales out during traffic spikes and scales in during lulls, preventing over-provisioning while maintaining low latency. This approach directly addresses the 3x peak-hour volume increase without requiring manual intervention or model changes.

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Deploy the model on multiple endpoints behind an Application Load Balancer.

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Use scheduled scaling to increase the instance count during known peak hours.

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Manually increase the instance count during peak hours.

Question 3

A data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing model performance?

Accepted Answer

AUC. AUC (Area Under the ROC Curve) is robust to class imbalance as it evaluates the model's ability to rank positive vs negative examples. Precision, recall, and F1 can be misleading if not threshold-optimized.

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Precision

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Accuracy

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Recall

Question 4

A company wants to track the lineage of their ML models, including the training dataset, hyperparameters, and training job used to produce each model version. Which AWS service should they use?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the correct choice because it is purpose-built to record and query the provenance of ML models, capturing relationships between datasets, training jobs, hyperparameters, and model versions. It creates a directed acyclic graph (DAG) of entities (e.g., artifacts, actions, contexts) that allows you to trace how a specific model version was produced, which directly meets the requirement for lineage tracking.

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Amazon DynamoDB

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AWS Glue Data Catalog

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Amazon S3 object tagging

Question 5

A financial services company deploys a fraud detection model on a SageMaker real-time endpoint. The inference logic includes a pre-processing step that requires access to a DynamoDB table for user metadata. The model container is a custom Docker image. How should the team grant the endpoint access to DynamoDB?

Accepted Answer

Create an IAM role with DynamoDB read access and assign it to the SageMaker endpoint as the execution role. SageMaker endpoints require an IAM execution role to be assigned at creation time. This role defines the permissions the endpoint's container has when making AWS API calls, such as reading from DynamoDB. By attaching a policy with DynamoDB read access to this execution role, the endpoint securely obtains temporary credentials via the AWS STS service, eliminating the need to hardcode or manage long-term credentials.

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Store IAM credentials in the container image as environment variables

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Attach an IAM instance profile to the underlying EC2 instance

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Retrieve temporary credentials from AWS Secrets Manager within the container code

Question 6

A company wants to use a pre-trained NLP model from SageMaker JumpStart for sentiment analysis. Which step is required to make predictions?

Accepted Answer

Deploy the model to an endpoint. D is correct because SageMaker JumpStart provides pre-trained models that are ready for inference without additional training. To make predictions, you must deploy the model to a SageMaker endpoint, which creates a hosted inference endpoint that can accept input data and return sentiment analysis results.

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Label the dataset for fine-tuning

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Train the model from scratch on the company's data

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Convert the model to ONNX format

Question 7

Refer to the exhibit. A data scientist ran a training job using a custom algorithm container. The job failed with the error shown. What is the most likely cause?

Accepted Answer

The algorithm script references an undefined variable or metric named 'loss'. The error 'Cannot evaluate expression: loss' indicates that the training script attempted to compute or log a variable named 'loss' that is not defined in the code. The training image access, S3 output path, and instance type are not related to this specific error.

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The S3 output path is incorrect

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The training image is not accessible

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The instance type is insufficient

Question 8

A financial services company is building a fraud detection model using transactional data stored in Amazon S3. The data includes transaction_id, timestamp, amount, merchant_category, and fraud_label (0/1). The data is collected from multiple sources and has inconsistencies: timestamps are in different timezones (UTC and EST), merchant categories are sometimes misspelled (e.g., 'RESTAURANT', 'Restaurant', 'restaurant'), and the fraud_label is missing for about 5% of records. The data science team uses AWS Glue for ETL. They need to prepare a clean dataset for training. The final dataset must have consistent timestamps in UTC, standardized merchant categories, and no missing fraud labels. The team also wants to minimize data loss. Which set of actions should the team take?

Accepted Answer

Use AWS Glue to convert timestamps to UTC, use a mapping table to group similar merchant categories (e.g., all restaurant variants to 'Restaurant'), and impute missing fraud_label using mode (most frequent value).. It preserves data by imputing missing fraud labels using the mode (most frequent value), which is appropriate for a binary classification label where the majority class is likely 0. It also standardizes timestamps to UTC and uses a mapping table to group merchant category variants, ensuring consistency without data loss. Dropping records (as in A and C) would reduce the dataset size, and imputing with the mean (as in B) is invalid for a categorical label.

Answer

Use AWS Glue to convert all timestamps to UTC, apply a mapping function to correct merchant category misspellings to a standard list, and drop records with missing fraud_label.

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Use AWS Glue to convert timestamps to UTC, use a fuzzy matching algorithm to standardize merchant categories, and replace missing fraud_label with the mean value (0.05).

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Use AWS Glue to convert timestamps to UTC, correct merchant categories by mapping known misspellings to correct names, and drop records with missing fraud_label.

Question 9

A company has a SageMaker endpoint running a model that provides real-time recommendations. Recently, the model's accuracy has degraded due to data drift. The team wants to automatically retrain the model when a drift metric exceeds a threshold and deploy the new model without downtime. Which architecture should the team implement?

Accepted Answer

Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant. It uses SageMaker Model Monitor to detect data drift and emit an EventBridge event, which triggers a SageMaker Pipeline to retrain the model, register it in the Model Registry, and then update the existing endpoint with a new production variant. This architecture enables automatic retraining and zero-downtime deployment by leveraging the endpoint's production variants for a blue/green deployment.

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Use SageMaker Model Monitor to collect drift metrics, and have a data scientist manually analyze the metrics and trigger retraining via the SageMaker console

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Schedule a daily SageMaker Pipeline that retrains the model and deploys it using a new endpoint, then updates the application to point to the new endpoint

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Use SageMaker Model Monitor to publish drift metrics to Amazon CloudWatch, and create a CloudWatch alarm that triggers an AWS Lambda function to retrain and deploy the model

Question 10

A data scientist is performing text preprocessing for a sentiment analysis model. The dataset contains many stop words and rare words. Which combination of preprocessing steps will reduce dimensionality and improve model performance?

Accepted Answer

Tokenization, stop-word removal, and TF-IDF. Tokenization, stop-word removal, and TF-IDF vectorization reduce dimensionality by filtering common words and weighting important terms. Lemmatization can further reduce sparsity.

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Remove all words shorter than 3 characters and apply label encoding

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Only tokenization without any removal

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Tokenization and one-hot encoding of words