MLA-C01 Practice Test 44 — 15 Questions

Question 1

Refer to the exhibit. A data scientist creates a SageMaker Pipeline definition using the JSON shown. The pipeline runs successfully, but the scientist notices that the training step did not use the parameter 'TrainingInstanceCount' defined in Parameters. Why did this happen?

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

Accepted Answer

The steps do not reference the Parameters; the values are hardcoded in the step definitions.. The SageMaker Pipeline definition shows that the training step's `InstanceCount` field is hardcoded to `1` in the step definition, rather than referencing the `TrainingInstanceCount` parameter using the `Parameters` object (e.g., `Parameters.TrainingInstanceCount`). In SageMaker Pipelines, parameters defined in the `Parameters` section must be explicitly referenced within the step definitions using the `Parameters` object; otherwise, the pipeline uses the hardcoded values and ignores the parameters entirely.

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The pipeline encountered a runtime error and fell back to default values.

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The parameter name has a typo; it should be 'TrainingInstanceCount' not 'TrainingInstanceCount'.

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The training image is not compatible with the specified instance type.

Question 2

A data science team has trained a model using SageMaker and wants to deploy it for real-time inference with automatic scaling based on request latency. The deployment must handle unpredictable traffic spikes without manual intervention. Which combination of SageMaker features should the team use?

Accepted Answer

Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric. It uses a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric. While this metric measures invocations per instance rather than latency directly, it serves as a proxy: high invocations per instance often lead to increased latency, so scaling on this metric helps maintain low latency by distributing load. This approach handles unpredictable traffic spikes automatically, meeting the requirement for latency-aware scaling better than any other option, which lack automatic or latency-based scaling.

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Deploy the model on a multi-model endpoint and manually adjust the number of instances via the AWS Management Console

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Deploy the model on an Elastic Inference accelerator and use AWS Auto Scaling with a scheduled policy

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Create a batch transform job with a scheduled Lambda function to trigger scaling

Question 3

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.

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SageMaker Neo

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SageMaker Pipelines

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Amazon Elastic Inference

Question 4

A company uses SageMaker Pipelines to orchestrate their ML workflow. They notice that if a pipeline step fails due to a transient error (e.g., a brief network issue), the entire pipeline fails and they must manually rerun from the beginning. They want to automatically retry failed steps a few times before failing. What should they do?

Accepted Answer

Configure a RetryPolicy in the pipeline step definition to specify the number of retry attempts and backoff. SageMaker Pipelines supports retry policies for steps. By setting a RetryPolicy in the step definition with a maximum number of retry attempts, the pipeline will automatically retry the step on failure. The other options do not achieve automatic retry within the pipeline: Step Functions would require rebuilding the pipeline, Lambda cannot retry pipeline steps, and CreatePipelineExecution does not handle retries.

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Use a Lambda function to catch step failures and re-invoke the step

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Use the CreatePipelineExecution API with a flag to ignore failures

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Use AWS Step Functions to orchestrate the workflow instead of SageMaker Pipelines

Question 5

A team is building a recommendation system and wants to store and serve features for online and offline models. The features include user statistics (updated daily) and movie metadata (static). The team needs low-latency inference for real-time recommendations and wants to reuse features across multiple models. Which AWS service should the team use to store, manage, and serve these features?

Accepted Answer

SageMaker Feature Store.. Amazon SageMaker Feature Store is purpose-built for storing, managing, and serving ML features with low-latency retrieval for online inference and batch serving for offline training. It supports feature reuse across multiple models by providing a centralized feature registry, consistent feature definitions, and both online (low-latency) and offline (S3-based) stores, which directly matches the team's requirements for real-time recommendations and cross-model reuse.

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Amazon DynamoDB with TTL.

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AWS Glue Data Catalog.

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Amazon S3 with AWS Lambda for serving.

Question 6

A company wants to enable cross-account access to a SageMaker model endpoint. The model is in Account A, and Account B needs to invoke it. Which TWO steps are required? (Select TWO)

Accepted Answer

Attach a resource-based policy to the SageMaker model in Account A allowing access from Account B's IAM role. SageMaker endpoints support resource-based policies that allow cross-account access. By attaching a resource-based policy to the model endpoint in Account A, you can grant the IAM role from Account B explicit permission to invoke the endpoint. This is the standard AWS mechanism for cross-account SageMaker endpoint invocation without needing to duplicate the model.

Answer

Export the model from Account A and re-deploy in Account B

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Configure VPC peering between the two accounts

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Use a SageMaker notebook instance cross-account sharing

Question 7

Refer to the exhibit. A Glue job runs successfully the first time but on subsequent runs with new data (added to the same input location), the job does not process the new data. What is the most likely cause?

Accepted Answer

The job bookmark is enabled, causing the job to skip already processed data. When a Glue job bookmark is enabled, the job tracks previously processed data using a persistent state stored in a DynamoDB table. On subsequent runs, the bookmark mechanism skips files that have already been processed, so new data added to the same input location is ignored unless the bookmark is reset or the job is configured to process new partitions. This explains why the first run succeeds but later runs do not process new data.

Answer

The script location is incorrect

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The MaxRetries is set to 0, so the job does not retry on failure

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The WorkerType is Standard, which does not support incremental processing

Question 8

A social media company is processing a real-time stream of user activity data from Amazon Kinesis Data Streams to train a machine learning model for content recommendation. The raw data includes user ID, timestamp, content ID, interaction type (like, share, comment), and device type. The data scientists need to aggregate features per user over a sliding window of 7 days, including counts of interaction types, unique content IDs engaged, and a moving average of interaction timestamps. The aggregated data will be used to update a user embedding model. The streaming data volume is approximately 500 records per second, and the company uses an AWS Glue streaming ETL job for transformation. However, the Glue job is failing frequently with high latency and checkpoint errors. The team needs a more robust solution to prepare the streaming data features. Which approach should the team take?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to perform the sliding window aggregations with built-in state management and exactly-once processing, then write the features to S3 and DynamoDB.. Amazon Kinesis Data Analytics for Apache Flink provides native support for sliding window aggregations with managed state and exactly-once processing semantics, which directly addresses the high latency and checkpoint errors seen in the Glue streaming ETL job. Flink's checkpointing mechanism ensures fault-tolerant state management for the 7-day sliding window, while Glue's Spark Streaming engine struggles with long-running stateful operations at 500 records/sec due to its micro-batch architecture and checkpoint overhead.

Answer

Increase the DPU count on the Glue streaming ETL job and reduce the checkpoint interval to improve performance.

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Use AWS Lambda functions to process records from Kinesis, store intermediate aggregation results in Amazon DynamoDB, and read them back to compute windowed features.

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Use Amazon SageMaker Processing jobs that run periodically every hour to read data from S3 (landing from Kinesis Firehose) and perform the aggregations batch-wise.

Question 9

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

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Shuffle the data randomly before splitting

Question 10

A team is using Amazon SageMaker Data Wrangler to prepare a large dataset. They need to detect potential bias in the data before training. Which capability of Data Wrangler should they use?

Accepted Answer

Integration with Amazon SageMaker Clarify for bias reports. Amazon SageMaker Data Wrangler integrates directly with Amazon SageMaker Clarify to detect bias in datasets. This integration allows you to run bias analysis on your data before training, generating reports that highlight potential imbalances or unfairness in features and target variables. It is the correct capability for the team's stated need.

Answer

Built-in transform for SMOTE oversampling

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Use of Amazon Athena to query data for bias patterns

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Export to Amazon SageMaker Feature Store

Question 11

A data scientist is using SageMaker to train a model and wants to reduce training costs without sacrificing performance. Which TWO actions should the scientist take? (Select TWO.)

Accepted Answer

Use SageMaker managed spot training. Using spot instances can reduce costs up to 90%. SageMaker managed spot training handles interruptions automatically. Using distributed training across multiple smaller instances can be cost-effective compared to a single large instance. Using Provisioned Concurrency is for inference, not training. Debugger hooks do not reduce cost.

Answer

Use a larger instance type to finish faster

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Enable SageMaker Debugger hooks to monitor training

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Enable SageMaker Model Monitor for the training job

Question 12

A machine learning engineer is deploying a model using SageMaker and needs to ensure that the endpoint can automatically scale based on traffic patterns. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Define a scaling policy using Application Auto Scaling for the SageMaker endpoint variant.. SageMaker endpoints use Application Auto Scaling to automatically adjust the number of instances based on traffic. You define a scaling policy (e.g., target tracking, step scaling) that references a CloudWatch metric. Option B is correct because the InvocationsPerInstance metric is a standard SageMaker endpoint metric that reflects the load per instance, and a CloudWatch alarm on this metric can trigger the scaling policy to add or remove instances as traffic changes.

Answer

Enable SageMaker Model Monitor to detect data drift.

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Configure a multi-model endpoint to serve multiple models.

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Use SageMaker batch transform to handle variable traffic.

Question 13

A company plans to deploy a large foundation model using SageMaker JumpStart. They are concerned about costs because the model will be used intermittently. Which deployment option is MOST cost-effective for intermittent traffic?

Accepted Answer

Deploy as a serverless endpoint. Serverless endpoints in SageMaker automatically scale to zero when not in use, so you pay only for the compute time consumed during inference requests. This makes them the most cost-effective option for intermittent traffic, as you avoid paying for idle compute capacity.

Answer

Purchase SageMaker Savings Plans for the endpoint

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Use a batch transform job for each request

Answer

Deploy as a real-time endpoint with a multi-model endpoint

Question 14

An ML engineer needs to create a feature store that supports both low-latency online inference and large-scale offline training. The features are updated hourly from a streaming source. Which Amazon SageMaker Feature Store configuration should the engineer use?

Accepted Answer

Create a feature group with both online and offline stores enabled.. SageMaker Feature Store supports both an online store (for low-latency retrieval) and an offline store (for large-scale analytics and training). Enabling both meets the dual requirement.

Answer

Create a feature group with only an online store enabled.

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Create two separate feature groups: one for online and one for offline.

Answer

Create a feature group with only an offline store enabled.

Question 15

An ML team is running multiple SageMaker endpoints for various models. The monthly cost is higher than expected. Which TWO actions would help reduce costs without negatively impacting performance?

Accepted Answer

Consolidate multiple small models into a single Multi-Model Endpoint on a larger instance.. SageMaker Multi-Model Endpoints allow you to host multiple small models on a single endpoint behind a common serving container, sharing the underlying instance resources. This reduces the number of endpoints and instances needed, lowering costs without degrading performance, as models are loaded and unloaded dynamically based on traffic.

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Increase the number of minimum instances to handle traffic spikes without scaling.

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Limit the maximum number of concurrent invocations per endpoint.

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Use a scheduled scaling to turn off endpoints during non-business hours.