MLS-C01 Mock Exam 87 — 25 Questions

Question 1

During training of a SageMaker built-in object detection algorithm, the loss is not decreasing after several epochs. Which troubleshooting step should be taken first?

Accepted Answer

Check whether the learning rate is appropriate. When the loss is not decreasing during training of a SageMaker built-in object detection algorithm, the most common cause is an inappropriate learning rate. A learning rate that is too high can cause the loss to oscillate or diverge, while one that is too low can cause the loss to plateau. Checking and adjusting the learning rate is the first troubleshooting step because it directly controls the step size of gradient updates and is a fundamental hyperparameter in optimization.

Answer

Increase the mini-batch size

Answer

Add more classes to the dataset

Answer

Increase the number of epochs

Question 2

Refer to the exhibit. A data scientist is configuring SageMaker Model Monitor for data quality checks. The configuration above is used. What is the purpose of the `ProbabilityThresholdAttribute` set to "0.5"?

Accepted Answer

It defines the probability threshold used to convert model output to binary predictions for monitoring. In SageMaker Model Monitor, the `ProbabilityThresholdAttribute` parameter is used for binary classification models to define the probability threshold for converting model output probabilities (e.g., 0.7) to binary predictions (0 or 1). This threshold is used to monitor drift in the distribution of predictions over time, not to set the endpoint inference threshold. Option D correctly identifies this purpose. Option A is incorrect because it does not filter input data; it only defines the threshold for converting probabilities to labels for monitoring. Option B is incorrect as it does not specify a sampling threshold; sampling is configured separately. Option C is incorrect because it does not set the accuracy metric threshold; accuracy is a separate metric.

Answer

It filters the input data to only include predictions above the threshold

Answer

It specifies the threshold for sampling data for monitoring

Answer

It sets the threshold for the accuracy metric

Question 3

A data engineering team is designing a data lake on Amazon S3. They need to enforce encryption at rest for all data stored in the bucket. The security policy requires that the encryption keys be managed by the organization using AWS Key Management Service (KMS), and that the bucket must deny uploads of unencrypted objects. Which bucket policy should be applied?

Accepted Answer

A bucket policy that denies PutObject unless the request includes the 'x-amz-server-side-encryption-aws-kms-key-id' header matching the desired KMS key ID. The security policy requires that encryption keys be managed by the organization using AWS KMS, and that unencrypted uploads be denied. A bucket policy that denies PutObject unless the request includes the 'x-amz-server-side-encryption-aws-kms-key-id' header matching the desired KMS key ID enforces both conditions: it ensures server-side encryption with a customer-managed KMS key (SSE-KMS) and blocks any upload that does not specify the exact key ID, thereby preventing unencrypted objects or objects encrypted with other keys.

Answer

A bucket policy that denies PutObject unless the request includes the 'x-amz-server-side-encryption' header with value 'AES256'

Answer

A bucket policy that denies PutObject if the 'x-amz-server-side-encryption' header is not present

Answer

Enable default encryption on the bucket with AWS-KMS

Question 4

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model is a large ensemble that requires 4 GB of memory. The engineer wants to minimize cost while ensuring the endpoint can handle up to 100 concurrent requests with a latency under 200 ms. Which instance configuration is most appropriate?

Accepted Answer

One ml.c5.xlarge instance with auto-scaling up to 2 instances.. The ml.c5.xlarge instance provides sufficient compute (4 vCPUs, 8 GB memory) for the 4 GB model, and auto-scaling up to 2 instances allows handling 100 concurrent requests with low latency while minimizing cost during low traffic. The ml.c5 family is optimized for compute-intensive inference, and auto-scaling ensures the endpoint scales out only when needed, avoiding over-provisioning.

Answer

Two ml.t3.medium instances behind a load balancer.

Answer

One ml.m5.2xlarge instance.

Answer

One ml.p3.2xlarge instance.

Question 5

A company needs to build a data lake on AWS for analytics. The data includes structured, semi-structured, and unstructured data. The solution must support schema-on-read, provide fine-grained access control, and be cost-effective for storing rarely accessed data. Which THREE services should be used? (Choose THREE)

Accepted Answer

AWS Glue Data Catalog for schema-on-read.. AWS Glue Data Catalog is correct because it provides a centralized metadata repository that enables schema-on-read for data stored in Amazon S3. It allows you to define table schemas and partitions without transforming the underlying data, so analytics tools like Amazon Athena and Amazon EMR can query the data with the schema applied at read time.

Answer

Amazon Redshift for data warehousing.

Answer

Amazon EMR for data processing.

Question 6

Refer to the exhibit. A data scientist is reviewing CloudWatch logs for a SageMaker real-time endpoint. The log shows that a prediction took 15 ms. The endpoint is configured with an ml.c5.large instance and the model is a small scikit-learn model. The latency requirement is under 10 ms. Which action would most likely reduce the latency?

Accepted Answer

Use a larger instance type. The latency of 15 ms exceeds the 10 ms requirement, indicating that the current ml.c5.large instance lacks sufficient compute resources (CPU) to process predictions quickly enough. Upgrading to a larger instance type (e.g., ml.c5.xlarge or ml.c5.2xlarge) provides more CPU capacity, reducing inference time by allowing the model to compute predictions faster. This directly addresses the bottleneck for a small scikit-learn model, which is CPU-bound and benefits from increased compute power.

Answer

Add more instances to the endpoint

Answer

Change the model to a TensorFlow model

Answer

Enable SageMaker Batch Transform

Answer

Increase the batch size for inference

Question 7

Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads data from the S3 bucket 'my-bucket' and writes the model artifact to the same bucket. The training job fails with an access denied error. What is the likely cause?

Accepted Answer

The IAM role does not have permission to write to the S3 bucket for the model artifact. The training job fails with an access denied error because the IAM role used by SageMaker lacks the s3:PutObject permission (or equivalent write access) for the S3 bucket 'my-bucket'. While the script reads data from the bucket, writing the model artifact requires explicit write permissions on the same bucket. Without this, SageMaker cannot upload the model artifact, causing the job to fail.

Answer

The IAM role does not have sagemaker:CreateModel permission

Answer

The IAM role does not have s3:ListBucket permission

Answer

The IAM role does not have ec2:DescribeInstances permission

Question 8

A company wants to store semi-structured data from IoT sensors in a cost-effective manner for occasional querying. The data is not updated once written. Which Amazon S3 storage class is the most cost-effective for this use case?

Accepted Answer

S3 Glacier Deep Archive. S3 Glacier Deep Archive is the most cost-effective storage class for semi-structured IoT sensor data that is written once and only occasionally queried. It offers the lowest storage cost among S3 classes (approximately $0.00099/GB/month), making it ideal for long-term archival of immutable data where retrieval times of 12–48 hours are acceptable.

Answer

S3 Standard

Answer

S3 One Zone-Infrequent Access

Answer

S3 Intelligent-Tiering

Question 9

A company runs a real-time fraud detection system using Amazon Kinesis Data Streams with 100 shards. Data is consumed by a custom Java application running on Amazon EC2 instances in an Auto Scaling group. The application processes records and writes results to a DynamoDB table. Over the past month, the application has experienced intermittent slowdowns and the DynamoDB write capacity has been fully utilized during peak hours. The team wants to improve throughput without losing the ability to reprocess failed records. The application currently uses the Kinesis Client Library (KCL) with DynamoDB as the lease table. The team is considering the following changes: A. Increase the number of EC2 instances to match the number of shards. B. Switch to using AWS Lambda as the consumer to handle scaling automatically. C. Increase the write capacity of the DynamoDB lease table to handle more workers. D. Use enhanced fan-out to have each consumer receive its own 2 MB/second shard throughput. Which change should the team implement first to address the issue?

Accepted Answer

Use enhanced fan-out to have each consumer receive its own 2 MB/second shard throughput.. The primary bottleneck is DynamoDB write capacity being fully utilized during peak hours. Enhanced fan-out (option B) provides each consumer with a dedicated 2 MB/second read throughput per shard, eliminating the need for consumers to contend for the shared 2 MB/second per shard. This reduces the load on the DynamoDB lease table because workers no longer need to poll for records, which in turn lowers the write operations to the lease table and alleviates the DynamoDB write capacity issue.

Answer

Increase the write capacity of the DynamoDB lease table to handle more workers.

Answer

Switch to using AWS Lambda as the consumer to handle scaling automatically.

Answer

Increase the number of EC2 instances to match the number of shards.

Question 10

A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (5% churn). Which TWO techniques can help improve the model's ability to detect churn?

Accepted Answer

Use Synthetic Minority Over-sampling Technique (SMOTE). SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, effectively balancing the dataset and providing the model with more diverse churn examples to learn from. This directly addresses the class imbalance problem without losing information from the majority class.

Answer

Downsample the majority class to balance the dataset

Answer

Use accuracy as the evaluation metric

Answer

Increase the model complexity by adding more layers

Question 11

A data scientist is building a regression model to predict house prices. The dataset contains features like 'number_of_rooms' (integer), 'sqft' (float), 'location' (categorical with 1000 unique values). Which feature engineering approach is BEST for the 'location' feature?

Accepted Answer

Target encoding. Target encoding is the best approach for the 'location' feature because it has 1,000 unique categories, making one-hot encoding infeasible (would create 1,000 dummy columns) and label encoding inappropriate (imposes arbitrary ordinal relationships). Target encoding replaces each category with the mean of the target variable (house price) for that category, capturing the predictive signal of location while keeping the feature as a single numeric column. This balances model performance with dimensionality and avoids overfitting when regularized (e.g., with smoothing or cross-validation).

Answer

Remove the feature

Answer

One-hot encoding

Answer

Label encoding

Question 12

A company uses AWS Glue to run ETL jobs on a daily basis. The jobs read from Amazon RDS and write to Amazon S3. The data volume has grown, and the jobs are taking longer to complete. The team wants to optimize the jobs for cost and performance. Which combination of techniques should the team implement? (Choose THREE.)

Accepted Answer

Use a larger Glue worker type, such as G.2X, for more memory per worker.. (larger worker type) provides more memory and CPU per worker, improving performance for heavy workloads. Option B (job bookmarks) enables incremental processing, reducing the amount of data read on subsequent runs. Option E (pushdown predicates) filters data at the source in the JDBC connection, reducing data transferred across the network. Option C is incorrect because increasing partitions in the output S3 data does not affect the processing speed of the current job. Option D is incorrect because increasing the maximum number of DPUs increases cost linearly and may not be as effective as using larger workers or other optimizations.

Answer

Increase the number of partitions in the output S3 data to improve parallelism.

Answer

Increase the maximum number of DPUs for the job to 100.

Question 13

Refer to the exhibit. A data engineer examines the output of 'aws glue get-job-run' for a failed job. The job run state is FAILED, but ErrorMessage is empty. The job ran for 3600 seconds (1 hour) before failing. What is the MOST likely cause of the failure?

Accepted Answer

The job ran out of memory due to insufficient DPU allocation.. The job ran for 3600 seconds (the default Glue job timeout) before failing with a FAILED state and an empty ErrorMessage. This pattern is characteristic of an out-of-memory (OOM) error in AWS Glue, which occurs when the allocated DPUs (Data Processing Units) are insufficient for the data volume or transformation complexity. Glue kills the job at the timeout boundary without a detailed error message because the JVM or Python process is killed by the OS (OOM killer), not by a Glue service exception.

Answer

The JDBC connection to the source database timed out.

Answer

The IAM role does not have sufficient permissions to access S3.

Answer

The Python script has a syntax error.

Question 14

A company is using Amazon SageMaker to train a model on data stored in S3. The training job needs to access data from an S3 bucket in a different AWS account. The data owner has granted cross-account access via a bucket policy. However, the training job fails with an AccessDenied error. What is the MOST likely cause?

Accepted Answer

The SageMaker execution role does not have the necessary permissions to access the S3 bucket.. Even with a bucket policy granting cross-account access, the SageMaker execution role must have an IAM policy that allows s3:GetObject (and any other required actions) on the S3 bucket. Without these permissions, the training job will fail with AccessDenied. Option A is incorrect because SSE-KMS encryption would require KMS permissions, but the issue is specifically about access permissions, not encryption. Option C is incorrect because the data does not need to be public; cross-account access via bucket policy is sufficient. Option D is incorrect because cross-account access does not require a VPC endpoint.

Answer

The data is encrypted with SSE-KMS and the SageMaker role lacks KMS permissions.

Answer

The S3 bucket is not configured with public access.

Answer

The S3 bucket is in a different region and requires a VPC endpoint.

Question 15

A data scientist is training a model using Amazon SageMaker and wants to automatically stop training when the model stops improving. Which feature should be used?

Accepted Answer

Enable early stopping in the training job configuration.. SageMaker's built-in early stopping feature automatically halts a training job when the model's objective metric (e.g., loss or accuracy) ceases to improve over a specified number of steps or epochs. This is configured directly in the training job's `StoppingCondition` parameter, which monitors the metric defined in the `MetricDefinitions` and stops training if no improvement is detected, saving compute time and avoiding overfitting.

Answer

Use SageMaker Debugger to monitor the loss metric.

Answer

Configure a CloudWatch alarm on the training job's CPU utilization.

Answer

Use SageMaker Hyperparameter Tuning with random search.

Question 16

Which TWO options are valid ways to reduce inference latency for a model deployed on a SageMaker real-time endpoint? (Select TWO.)

Accepted Answer

Enable SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles the trained model into an optimized binary for the specific target instance type, using hardware-specific instructions (e.g., Intel MKL-DNN, NVIDIA TensorRT) to reduce inference latency without sacrificing accuracy. This compilation optimizes the model graph and fuses operations, leading to faster execution on the deployed endpoint.

Answer

Use SageMaker batch transform instead of real-time endpoint

Answer

Deploy the model to multiple instances behind a load balancer

Answer

Increase the endpoint's invocation timeout

Question 17

A data scientist is training a deep learning model for object detection using Amazon SageMaker. The training job is using a single GPU instance and is taking too long. Which THREE actions can reduce training time? (Choose THREE.)

Accepted Answer

Enable mixed precision training with FP16. Enabling mixed precision training with FP16 reduces memory usage and accelerates computation by using half-precision floating-point numbers where possible, which is particularly effective on NVIDIA GPUs with Tensor Cores (e.g., V100, A100). This can nearly double throughput for deep learning models without sacrificing model accuracy, as critical operations still use FP32 precision.

Answer

Use a CPU instance instead of GPU

Answer

Reduce the batch size

Question 18

A data scientist is deploying a regression model in Amazon SageMaker that predicts housing prices. The model shows high bias (underfitting). Which action is most likely to reduce bias?

Accepted Answer

Add more features or increase model complexity. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features or increasing model complexity (e.g., using polynomial features, deeper trees, or a more flexible algorithm) directly addresses underfitting by giving the model greater capacity to learn from the data. In Amazon SageMaker, this could involve using a more complex built-in algorithm like XGBoost with deeper trees or adding feature engineering transformations in a processing job.

Answer

Reduce the amount of training data

Answer

Increase regularization strength

Answer

Use a simpler model

Question 19

A data scientist is working on a predictive maintenance project for a manufacturing company. Sensor data is collected every second from 100 machines and stored in an Amazon S3 bucket as Parquet files, partitioned by machine_id and date. The dataset is massive (10 TB) and contains over 2000 features per machine. The data scientist needs to perform exploratory data analysis to identify which features are most predictive of machine failure. They have access to Amazon SageMaker Studio with a SageMaker Data Wrangler flow. The initial data exploration is taking too long due to the volume of data. The data scientist wants to speed up the analysis without losing accuracy in feature selection. Which course of action is most appropriate?

Accepted Answer

Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample. SageMaker Data Wrangler supports stratified sampling, which preserves the distribution of machine failure across machine_id and date, allowing for faster exploratory data analysis while maintaining representativeness for feature selection. Option A is incorrect because distributed processing with EMR on the full dataset may still be slow and is unnecessary when sampling can capture the signal. Option B is incorrect because using only one machine's data loses cross-machine variability and may bias feature selection. Option D is incorrect because random sampling does not guarantee preservation of time series order or failure distribution, potentially compromising analysis accuracy.

Answer

Switch to using Amazon EMR with Spark to perform distributed feature selection on the full dataset

Answer

Reduce the data to a single partition by concatenating all files and use only one machine's data

Answer

Use Amazon Athena to query a random sample of rows from the dataset

Question 20

A company is using AWS Glue Data Catalog as the metadata store for their data lake. They have multiple AWS accounts and want to share the catalog across accounts. Which feature should they use?

Accepted Answer

AWS Resource Access Manager (RAM). AWS Resource Access Manager (RAM) enables you to share AWS Glue Data Catalog databases and tables across multiple AWS accounts without needing to copy metadata. This allows a centralized catalog to be consumed by different accounts for querying and ETL operations, maintaining a single source of truth for the data lake.

Answer

Amazon Athena Federated Query

Answer

AWS Lake Formation

Answer

Amazon S3 Cross-Region Replication