AWS Certified Machine Learning Specialty MLS-C01 (MLS-C01) — Questions 826–900

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Option B is wrong because adding more instances introduces network communication overhead (e.g., gradient synchronization) that can actually slow down training if the dataset is small or the model is not embarrassingly parallel; it does not address the data ingestion bottleneck. Option C is wrong because increasing batch size may improve GPU utilization but does not fix the underlying slow data loading from S3; it can even cause out-of-memory errors or require learning rate tuning. Option D is wrong because switching from Pipe mode to File mode would download the entire dataset to the local EBS volume before training, increasing startup time and disk I/O, which would worsen throughput.

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Option A is wrong because a restrictive S3 bucket policy would cause consistent access denied errors (HTTP 403), not intermittent timeouts. Option C is wrong because Redshift maintenance mode is a planned event that blocks all queries and connections, leading to immediate, persistent failures, not intermittent timeouts. Option D is wrong because uncompressed source data increases data volume and network transfer time, which can contribute to slower performance but does not directly cause intermittent timeouts; AWS Glue can handle uncompressed data, and compression is an optimization, not a requirement.

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Option B is wrong because S3 bucket limits are based on total bucket size (unlimited) and object size (up to 5 TB per object), not model size; a model exceeding these limits would cause a different error (e.g., upload failure), not a silent missing artifact. Option C is wrong because using spot instances does not affect where the model is saved; spot instances can be preempted, but if the training completes successfully, artifacts are still saved to `/opt/ml/model` and uploaded. Option D is wrong because SageMaker can write to S3 buckets in any region as long as the bucket policy and IAM role grant cross-region access; a region mismatch would cause a permission or access error, not a silent failure to save artifacts.

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Option A is wrong because Amazon Forecast does not accept JSON files for target time series data; it requires CSV format for dataset import. Option C is wrong because a text file with one value per line lacks the necessary metadata (timestamp and item_id) to define multiple time series and their temporal alignment. Option D is wrong while Parquet is a supported format for related time series (RTS) or item metadata, the target time series dataset must be in CSV format as per Forecast's documentation.

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Option A is wrong because reducing early stopping tolerance would cause training to stop even sooner, which does not address the fundamental issue of model capacity and would likely worsen the high loss. Option B is wrong because increasing maximum runtime does not help if the model has already converged; the algorithm has reached a plateau and more time will not reduce the loss further. Option D is wrong because increasing the number of training instances does not increase model complexity — it only provides more data for the same linear model, which may actually increase the irreducible error if the data is inherently non-linear.

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Option A is wrong because out-of-memory errors usually manifest as 'ResourceExhaustedError' or container OOM kill signals, not a script-level failure reason. Option B is wrong because inaccessible S3 buckets cause a 'ClientError' with a specific 'AccessDenied' or 'NoSuchBucket' message, not a generic script failure. Option C is wrong because exceeding service limits results in a 'LimitExceededException' during job submission, not during training execution.

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Option A is wrong because mean squared error (MSE) is a regression metric that measures average squared differences between predicted and actual values, not suitable for binary classification evaluation. Option B is wrong because accuracy is misleading in imbalanced datasets; a model predicting all negatives achieves 99% accuracy but fails to identify positives, as seen here. Option D is wrong because precision measures the proportion of positive predictions that are correct, which could be high if the model makes very few positive predictions, but it does not capture the low detection rate of actual positives (recall).

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Option A is wrong because creating an interaction term between two nearly perfectly correlated features would introduce even more severe multicollinearity (the interaction term will be highly correlated with the original features), worsening model stability. Option C is wrong because increasing the regularization parameter (e.g., lambda in L2 regularization) can shrink coefficients but does not eliminate the underlying multicollinearity; the model remains sensitive to small data changes and coefficient interpretation is still problematic. Option D is wrong because mean-centering only shifts the features' means to zero and does not change the correlation coefficient between them; it has no effect on multicollinearity.

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Option A is wrong because reducing the number of epochs does not affect peak GPU memory usage during training; it only changes the total training time, not the memory footprint per batch. Option B is wrong because increasing the number of GPUs via distributed training (e.g., ml.p3.16xlarge) does not reduce per-GPU memory consumption; it distributes the model across GPUs but each GPU still needs to hold its portion of the data and gradients, and the error is on a single GPU instance. Option D is wrong because using a smaller instance type would reduce available GPU memory (e.g., ml.p3.xlarge has only 8 GB), making the out-of-memory error worse, not better.

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Option A is wrong because R-squared is a regression metric that measures the proportion of variance explained by the model, and it is not applicable to binary classification problems. Option B is wrong because Mean Squared Error (MSE) is a regression loss function that penalizes large errors quadratically and does not provide meaningful evaluation for classification tasks, especially with imbalanced data. Option D is wrong because accuracy would be misleadingly high (95%) by simply predicting the majority class for all instances, failing to capture the model's performance on the rare positive class.

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Option A is wrong because SSE-S3 uses AWS-managed keys, not the company's own HSM-managed keys, violating the requirement for customer-controlled key management. Option B is wrong because client-side encryption encrypts data before it reaches S3, but the requirement specifies 'encrypted at rest in S3' and server-side encryption; client-side encryption also places the burden of key management on the client, but the key must be stored in the HSM, not used for client-side encryption. Option D is wrong because SSE-KMS with a customer managed key still relies on AWS KMS to manage the key, not the company's own HSM, and the key is not stored or managed directly in the company's HSM.

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Option A is wrong because Amazon Athena is an interactive query service that can query CSV and Parquet files directly in S3, but it does not perform ETL transformations or convert file formats; it is for ad-hoc analysis, not data transformation. Option B is wrong because Amazon EMR with Spark requires provisioning and managing clusters, which is not serverless; it incurs costs for running EC2 instances even when idle, making it less cost-effective for occasional transformations. Option D is wrong because AWS Data Pipeline is a workflow orchestration service that can move and transform data, but it is not serverless (it relies on EC2 instances for task runners) and is primarily designed for scheduled data movement, not optimized for converting CSV to Parquet with built-in Spark capabilities.

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Option B is wrong because multi-model endpoints share a single instance across multiple models, which can lead to contention and increased latency for a large 5 GB model under bursty traffic, and the ml.c5.4xlarge instances lack GPU acceleration, making them unsuitable for deep learning inference at high throughput. Option C is wrong because SageMaker Serverless Inference has a cold start latency that can exceed 100 ms, especially for a 5 GB model, and its maximum concurrency is limited, making it unable to handle 1000 requests per second with strict latency requirements. Option D is wrong because a single ml.p3.16xlarge instance, while powerful, cannot handle bursty traffic of 1000 requests per second without scaling; a single instance will be overwhelmed, causing latency to spike above 100 ms, and it lacks the elasticity needed for bursty workloads.

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Option A is wrong because multi-model endpoints do not support GPU instances; they are designed for CPU-based hosting where multiple models share the same endpoint to reduce costs, not for minimizing cold start latency. Option B is wrong because serverless inference endpoints automatically scale to zero when idle, incurring cold start latency when a request arrives, which contradicts the goal of minimizing cold start latency. Option C is wrong because multiple production variants are used for A/B testing or gradual rollout, not for reducing cold start latency; they do not change the underlying cold start behavior of the endpoint.

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Option A is wrong because using a smaller instance type reduces CPU and memory resources, which would likely increase training time, not decrease it. Option B is wrong because reducing the number of trees in XGBoost directly lowers model capacity, which typically sacrifices accuracy (increases bias) and is not a recommended way to reduce time without impacting performance. Option C is wrong because while a larger instance type with more vCPUs can speed up training through parallelism, it does not address the root cause of high dimensionality (500 features), and the improvement may be marginal compared to feature reduction; it also increases cost without guaranteeing accuracy preservation.

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Option A is wrong because plotting the autocorrelation function (ACF) is a visual diagnostic for identifying autocorrelation patterns and model order (e.g., AR or MA terms), but it does not provide a formal statistical test for stationarity. Option B is wrong because time-series cross-validation is a model evaluation technique used to assess predictive performance, not a method for testing stationarity. Option D is wrong because a scatter plot of the series against its lag can reveal linear relationships and autocorrelation, but it lacks a formal hypothesis test and cannot definitively confirm or reject stationarity.

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Option A is wrong because using a different split criterion (e.g., Gini impurity vs. entropy) changes how splits are selected but does not inherently reduce model complexity or overfitting; it may still produce a deep, overfitted tree. Option C is wrong because L1 regularization is a technique for linear models (e.g., Lasso regression) and is not directly applicable to decision trees, which do not have coefficients to penalize. Option D is wrong because increasing tree depth makes the model more complex, exacerbating overfitting rather than fixing it.

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Option A is wrong because Glue triggers schedule job execution but do not track which records have been processed, so they cannot ensure only new/updated records are handled. Option B is wrong because partition pruning filters data based on partition columns in S3, not on record-level changes from a relational database like RDS. Option C is wrong because Glue crawlers update the Data Catalog schema and detect new partitions, but they do not track incremental record changes or support stateful processing of updated rows.

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Option B is wrong because if the training job were trying to write to a different S3 bucket, the error would still be an access denied error unless the role had permissions for that bucket, but the question states the job fails when trying to write to the specified bucket, not a different one. Option C is wrong because the error occurs during the write of model artifacts, not during reading training data; a read permission issue would cause a different error (e.g., during data loading). Option D is wrong because the training job is already running, meaning the role has permission to create training jobs; the error is specifically about writing to S3, not about job creation.

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Option B is wrong because increasing batch size typically reduces gradient noise and can lead to sharper minima, which often worsens generalization and overfitting, not reduces it. Option C is wrong because using a larger learning rate can cause the optimizer to overshoot minima, leading to unstable training and potentially higher validation loss, but it does not specifically target the overfitting problem when training loss is already near zero. Option D is wrong because adding more convolutional layers increases model capacity, which exacerbates overfitting when the model already has enough capacity to memorize the training data.

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Option A is wrong because increasing the number of rounds would continue training further, exacerbating overfitting and making validation loss worse. Option C is wrong because increasing the learning rate makes the model converge faster but does not address overfitting; it can actually cause the model to overshoot optimal minima and worsen validation loss. Option D is wrong because increasing the maximum tree depth allows trees to grow deeper, capturing more complex patterns and increasing the risk of overfitting, which is the opposite of what is needed.

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Option A is wrong because registering the model in SageMaker Model Registry is optional for deployment; it is used for model versioning and governance, not a required step to deploy an endpoint. Option B is wrong because creating a training job to re-train the model is unnecessary if the model artifacts already exist; deployment only requires the artifacts and a serving container. Option C is wrong because while TensorFlow models are often saved as SavedModel format, the question states the artifacts are already stored in S3, and the required step is to create the Model object with the serving image, not to save the model again.

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Option A is wrong because incorrect S3 bucket permissions would result in an AccessDenied error, not a timeout. Option B is wrong because a syntax error in the inference script would cause a Python runtime error during invocation, not a timeout during model loading. Option C is wrong because insufficient memory would cause an OutOfMemory error or container crash, not a timeout; the instance type's memory is checked at container start, not via a timeout.

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Option B is wrong because the ml.g4dn.xlarge uses a T4 GPU with less memory and lower compute performance compared to the V100 in P3 instances, which may not meet the latency requirements for a high-memory-footprint model. Option C is wrong because ml.c5.xlarge has no GPU, making it incapable of GPU acceleration required by the model. Option D is wrong because ml.p3.16xlarge with 8 GPUs is over-provisioned for a single model endpoint, leading to unnecessary cost without latency benefit for a single inference request.

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Option A is wrong because compressing JSON with gzip reduces storage size but does not change the row-based nature of JSON; Athena still must decompress and scan entire rows, limiting performance gains and cost reduction compared to columnar formats. Option B is wrong because partitioning by date improves query performance only if queries filter on that partition key, but it does not address the fundamental inefficiency of scanning entire JSON rows; partitioning alone is less effective than combining it with a columnar format. Option D is wrong because increasing the number of Athena workers (i.e., concurrency or DML query slots) does not reduce the amount of data scanned per query; it only allows more queries to run in parallel, which does not fix the root cause of slow and expensive queries.

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Option A is wrong because AWS KMS key encryption would cause a different error (e.g., KMS access denied) rather than a generic 'access denied' error, and the question does not mention KMS key permissions. Option B is wrong because a bucket name mismatch would produce a 'NoSuchBucket' error, not an 'access denied' error. Option D is wrong because if the bucket is publicly accessible, the training job would not need IAM role permissions for read access; the error would be a different type (e.g., 403 Forbidden) if the role lacked permissions, but the scenario points to an encryption mismatch.

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Option A is wrong because increasing both timeout and instance type is an over-engineered solution that introduces unnecessary cost and complexity, and it does not address the underlying inefficiency in processing the dataset sequentially. Option B is wrong because simply increasing the timeout to 1800 seconds is a temporary band-aid that does not fix the performance bottleneck; as the dataset continues to grow, the timeout will need to be increased again, leading to an unsustainable pattern. Option D is wrong because switching to a larger instance type (ml.m5.4xlarge) only provides more memory and compute but does not change the sequential processing logic; the script will still take the same amount of time (or only marginally less) and may still hit the timeout if the dataset is large enough.

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Option A is wrong because the error is about a service limit of 0, not about spot instance availability; switching to on-demand instances would still fail because the limit applies to both spot and on-demand training jobs. Option B is wrong because the error message does not indicate regional unavailability; it specifically cites a limit of 0, meaning the instance type exists in the region but the account has no quota. Option D is wrong because the error is not about instance size or resource exhaustion; using a smaller instance type like ml.m5.large would avoid the GPU limit but does not address the root cause of the ml.p3.8xlarge limit being 0.

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Option A is wrong because DynamoDB auto scaling adjusts write capacity based on load, but the latency issue originates upstream in the stream processing, not in the write destination; the pipeline is already writing to DynamoDB, and auto scaling would not reduce the time data spends waiting in the stream or being processed. Option B is wrong because decreasing parallelism in Kinesis Data Analytics reduces the number of concurrent processing tasks, which would increase latency by slowing down the sliding window aggregations, not reduce it. Option D is wrong because increasing the sliding window size reduces the frequency of computations but does not address the underlying throughput limitation; it may even increase latency by requiring more data to be buffered before results are emitted.

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Option B is wrong because increasing the learning rate can cause the optimizer to overshoot minima, leading to unstable training and potentially worse generalization, not reduced overfitting. Option C is wrong because removing regularization terms (e.g., L1/L2 penalties) would reduce the model's resistance to overfitting, exacerbating the validation loss increase. Option D is wrong because increasing the number of epochs would continue training on the same data, allowing the model to memorize noise further and worsen the overfitting, as validation loss would continue to rise.

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