MLA-C01 Practice Test 4 — 25 Questions

Question 1

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. Option B is correct because 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.

Answer

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

Answer

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

Answer

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 2

Which TWO of the following are best practices for deploying machine learning models on SageMaker? (Select TWO.)

Accepted Answer

Use separate production and staging endpoints to test new models before full rollout. Option B and Option D are correct. Option A is wrong because model should be in S3, not EBS. Option C is wrong because you should use the SageMaker Model Registry for versioning. Option E is wrong because CloudWatch Logs are enabled by default, not disabled.

Answer

Store model artifacts in Amazon EBS volumes attached to the endpoint instances

Answer

Manually track model versions using tags because SageMaker Model Registry is not available for deployment

Answer

Disable CloudWatch Logs to reduce costs during inference

Question 3

A financial services company uses a custom container on Amazon SageMaker to serve a fraud detection model. The model's inference latency has recently increased, causing timeouts for some requests. The team reviews the SageMaker logs and finds that the container is consuming more memory than allocated. What should the team do to maintain service quality while ensuring cost-effectiveness?

Accepted Answer

Change the instance type to a memory-optimized instance, such as r5.large. The correct answer is D because the root cause is that the container is consuming more memory than allocated, leading to increased latency and timeouts. Switching to a memory-optimized instance like r5.large directly addresses the memory constraint by providing more memory per vCPU, which resolves the performance issue without over-provisioning compute resources. This approach is cost-effective because it targets the specific bottleneck (memory) rather than scaling out or changing unrelated parameters.

Answer

Decrease the model's batch size to reduce memory usage

Answer

Increase the number of instances in the endpoint to distribute the load

Answer

Implement an auto-scaling policy based on memory utilization

Question 4

A healthcare company deploys a model to predict patient readmission risk. The model was trained on historical data and is now showing signs of concept drift. The team needs to implement a monitoring solution that can detect drift and automatically retrain the model when drift is detected. Which THREE steps should the team take to build this solution? (Choose THREE.)

Accepted Answer

Deploy SageMaker Model Monitor to track prediction quality over time. A is correct because Amazon SageMaker Model Monitor can continuously track prediction quality metrics (e.g., accuracy, precision) over time by analyzing data captured from the endpoint. This allows the team to detect concept drift by comparing live predictions against a baseline, triggering alerts when performance degrades. It provides a managed, automated way to monitor model quality without manual intervention.

Answer

Disable the existing endpoint to prevent stale predictions during retraining

Answer

Manually compare the model's predictions against a holdout validation set each week

Question 5

A machine learning engineer is troubleshooting a model that is producing unexpectedly low accuracy in production. The engineer examines the model's training data and finds that the distribution of the target variable in production is significantly different from the training set. What type of drift is the model experiencing?

Accepted Answer

Concept drift. Option B is correct because a change in the target variable distribution is concept drift. Option A is wrong because covariate shift is input distribution change. Option C is wrong because prior probability shift is a type of concept drift, but not the best answer here. Option D is wrong because data drift is a general term.

Answer

Prior probability shift

Answer

Data drift

Answer

Covariate shift

Question 6

A team deploys a machine learning model using a SageMaker endpoint with an ML.T4 instance. After a week, they notice that the endpoint's CPU utilization is consistently below 10% and latency is low. However, the endpoint is incurring high costs. Which action should the team take to reduce costs while maintaining the ability to serve traffic?

Accepted Answer

Migrate to a SageMaker Serverless Inference endpoint. Option C is correct because the endpoint's CPU utilization is consistently below 10% with low latency, indicating that traffic is sparse and the instance is severely underutilized. SageMaker Serverless Inference automatically scales compute resources based on request volume and charges only for the compute time consumed per inference, eliminating idle costs. This makes it the most cost-effective choice for low-utilization workloads while still maintaining the ability to serve traffic on demand.

Answer

Switch to a multi-model endpoint to share instances across models

Answer

Reduce the number of instances to one

Answer

Implement an asynchronous inference endpoint

Question 7

A company has a SageMaker endpoint that uses a trained model to classify images. The endpoint is experiencing high latency and the team suspects it is due to the model size. Which action can the team take to reduce latency without significantly impacting accuracy?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles trained models into an optimized binary for the target hardware, applying techniques like operator fusion, memory layout optimization, and quantization. This reduces model size and inference latency while preserving accuracy, making it the correct choice for addressing high latency caused by model size.

Answer

Switch to a compute-optimized instance type

Answer

Reduce the batch size of inference requests

Answer

Convert the model to ONNX format

Question 8

A company uses Amazon Rekognition to moderate user-generated images. They want to set up a monitoring system that alerts the team if the number of inappropriate images flagged by the model exceeds a threshold. Which combination of AWS services should they use?

Accepted Answer

Amazon CloudWatch to publish custom metrics and create an alarm, and AWS Lambda to process images and publish metrics.. Option B is correct because Amazon Rekognition can be integrated with AWS Lambda to process images and publish custom metrics to Amazon CloudWatch. CloudWatch can then create an alarm based on a threshold for the number of inappropriate images flagged, and trigger an SNS notification to alert the team. This combination provides a complete monitoring and alerting pipeline without relying on inference logs or resource configuration changes.

Answer

Amazon CloudWatch Logs to store inference logs and create a metric filter.

Answer

AWS Config to track resource changes and trigger an SNS notification.

Answer

Amazon Simple Notification Service (SNS) to send alerts when threshold is exceeded.

Question 9

A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?

Accepted Answer

Parquet. Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.

Answer

XML

Answer

ORC

Answer

JSON Lines

Question 10

In SageMaker Data Wrangler, you have a flow that imports data from Amazon S3 and needs to join it with a table from Amazon Redshift. The data volumes are large (hundreds of GB). Which approach is most efficient within Data Wrangler?

Accepted Answer

Import the Redshift table directly using a Data Wrangler source step and apply a join transform. Option D is correct because SageMaker Data Wrangler natively supports Amazon Redshift as a source via a direct connection, allowing you to import the Redshift table as a source step and then apply a join transform within the same visual flow. This approach avoids unnecessary data movement or intermediate exports, which is critical for hundreds of GB of data, as it leverages Data Wrangler's optimized in-memory and Spark-based processing to perform the join efficiently.

Answer

Use Amazon Athena federated query to join in place and import the result

Answer

Export the Redshift table to S3 as Parquet, then import both datasets into Data Wrangler and join

Answer

Use AWS Glue to join the datasets and output to S3, then import the joined result into Data Wrangler

Question 11

A data scientist creates a feature group as shown in the exhibit. When ingesting data with an 'age' column of integer values, the ingestion fails. What is the most likely cause?

Accepted Answer

The `age` feature type should be `Integral`, not `String`.. Option B is correct because the feature group definition specifies the 'age' column as a `String` type, but the ingested data contains integer values. Amazon SageMaker Feature Store requires that the data types of ingested records match the schema defined in the feature group. When a mismatch occurs, such as providing an integer for a string field, the ingestion fails with a type conversion error.

Answer

The role does not have permissions to write to the feature store.

Answer

The `OnlineStoreConfig` must include a `SecurityConfig`.

Answer

The `EventTimeFeatureName` is incorrectly spelled.

Question 12

A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?

Accepted Answer

Random oversampling of the minority class. Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.

Answer

Principal Component Analysis (PCA) dimensionality reduction

Answer

Standard scaling of numerical features

Answer

One-hot encoding of categorical variables

Question 13

A company is building a real-time inference pipeline for an ML model. The raw data arrives in JSON format via Amazon Kinesis Data Streams. Before invoking the SageMaker endpoint, the data must be preprocessed to match the training data format. Which THREE steps should be included in the preprocessing function? (Select THREE)

Accepted Answer

Ensure that missing values are handled consistently with the training phase. Option A is correct because the preprocessing function must handle missing values identically to how they were handled during training to maintain data consistency. If the training phase used mean imputation for a numeric feature, the inference pipeline must apply the same mean value; otherwise, the model will receive unexpected input distributions, degrading prediction accuracy.

Answer

Convert the data to a CSV string for model input

Answer

Re-train the model periodically using new data

Question 14

A company is preparing a dataset with a categorical feature that has over 1000 unique values. They need to create features for a random forest model. Which feature engineering approach is most scalable and effective in AWS for high-cardinality categories?

Accepted Answer

Target encoding with smoothing using SageMaker Data Wrangler. Target encoding with smoothing in SageMaker Data Wrangler is the most scalable and effective approach because it replaces each high-cardinality category with the mean of the target variable, smoothed by a global prior to prevent overfitting. SageMaker Data Wrangler handles datasets with over 1000 unique values efficiently without exploding feature dimensions, unlike one-hot encoding, and avoids the ordinal bias of label encoding.

Answer

Hash encoding using Apache Spark on Amazon EMR

Answer

One-hot encoding using SageMaker Processing with scikit-learn

Answer

Label encoding using Pandas in a SageMaker notebook

Question 15

A data scientist is exploring data stored in an Amazon Redshift cluster. The data includes timestamp columns with different formats. The scientist wants to create a new column that standardizes the timestamp format to UTC. Which approach is MOST efficient?

Accepted Answer

Use a SELECT with CONVERT_TIMEZONE in Redshift and export to S3. Option B is correct because `CONVERT_TIMEZONE` in Amazon Redshift is a native SQL function that directly converts timestamps to UTC without moving data outside the cluster. This approach avoids the overhead of external services, leverages Redshift's massively parallel processing (MPP) engine, and is the most efficient for in-database transformations.

Answer

Use AWS Glue to read the Redshift table and apply a custom transform

Answer

Use a SageMaker notebook to query Redshift and transform

Answer

Use Amazon QuickSight to transform the timestamp

Question 16

An organization stores raw data in Amazon S3 as CSV files. They need to perform serverless data transformation and convert the data to Parquet format for efficient ML training. Which AWS service is most appropriate?

Accepted Answer

AWS Glue. AWS Glue is the most appropriate service because it is a fully managed, serverless ETL service designed specifically for data transformation tasks like converting CSV to Parquet. It automatically handles schema inference, data partitioning, and optimization for ML training workloads without requiring infrastructure management.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

Amazon Redshift

Question 17

A company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?

Accepted Answer

Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.. Option B is correct because AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.

Answer

Use S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.

Answer

Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.

Answer

Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.

Question 18

You are preparing a time-series dataset for a forecasting model. Which three steps are critical to prevent data leakage during preprocessing? (Choose three.)

Accepted Answer

Standardize features using parameters computed only from the training set. Standardizing features using parameters computed only from the training set is critical because it prevents information from the test set from influencing the training data. If you compute the mean and standard deviation from the entire dataset before splitting, the test set's distribution leaks into the training process, causing the model to see future data during training. This violates the temporal order and leads to overly optimistic performance estimates.

Answer

Impute missing values using the mean of the entire dataset

Answer

Shuffle the data randomly before splitting

Question 19

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.

Answer

Impute with a constant like -1

Answer

Use a model to predict missing values

Answer

Remove all rows with missing values

Question 20

An ML engineer needs to convert a raw dataset from CSV to Parquet format in a serverless manner for cost efficiency. Which AWS service can be used to perform this conversion without managing servers?

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a serverless ETL service that can automatically convert CSV to Parquet using its built-in transform capabilities, such as the `ChangeSchema` or `ConvertToParquet` transforms in a Glue ETL job. This eliminates the need to provision or manage any servers, aligning with the cost-efficiency requirement.

Answer

Amazon S3 Select

Answer

Amazon EMR

Answer

AWS Lambda