[{"data":1,"prerenderedAt":411},["ShallowReactive",2],{"practiceExams":3,"practice-exam-\u002Fpractice-exams\u002Faws-certified-machine-learning-engineer-associate":153,"practice-exams-\u002Fpractice-exams\u002Faws-certified-machine-learning-engineer-associate":319,"practice-exam-concepts-\u002Fpractice-exams\u002Faws-certified-machine-learning-engineer-associate":362,"courses-\u002Fpractice-exams\u002Faws-certified-machine-learning-engineer-associate":363},[4,17,28,39,49,59,70,80,90,101,111,122,134,145],{"id":5,"slug":6,"description":7,"questionCount":8,"isActive":9,"datePublicModified":10,"difficulty":11,"examVersion":16},1,"aws-certified-solutions-architect-associate-saa-c03","Unlock your potential with the AWS Certified Solutions Architect - Associate Practice Exam Simulator. 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An ML team needs an initial copy of the records in Amazon S3 and an ongoing feed of committed inserts, updates, and deletes for feature preparation. After the initial load, the feed must preserve successive changes to the same record, even when those changes occur between extraction runs. The application permanently deletes some records and cannot be modified to produce change events. Which solution meets these requirements with the least custom development?","Consider whether periodic queries can recover changes that are no longer visible in the current table contents.","single",[178,183,187,191],{"id":179,"questionId":173,"content":180,"hint":181,"isCorrect":182,"position":152,"matchAnswerId":152},261575,"Schedule an AWS Glue JDBC job that uses the transaction ID as a job bookmark and appends newly extracted rows to Amazon S3.","A bookmark on a monotonically increasing transaction ID can support extracting new rows, but it does not capture updates to previously extracted rows or permanent deletions. It also cannot preserve successive changes between runs.",false,{"id":184,"questionId":173,"content":185,"hint":186,"isCorrect":9,"position":152,"matchAnswerId":152},261576,"Enable the required row-based binary logging and configure sufficient log retention on the database. Use an AWS DMS full-load-and-CDC task with the database as the source and Amazon S3 as the target.","AWS DMS can perform the initial table load and then capture committed inserts, updates, and deletes from MySQL binary logs. Its S3 target can store ongoing change records, including operation indicators. This avoids building custom change-capture logic or modifying the application.",{"id":188,"questionId":173,"content":189,"hint":190,"isCorrect":182,"position":152,"matchAnswerId":152},261577,"Create an RDS read replica and periodically query rows whose last_modified timestamp exceeds the previous extraction watermark. Append the query results to Amazon S3.","A read replica offloads queries, but timestamp-based polling only observes row versions that exist when the query runs. It cannot recover permanently deleted rows or every intermediate update to a row between queries.",{"id":192,"questionId":173,"content":193,"hint":194,"isCorrect":182,"position":152,"matchAnswerId":152},261578,"Take recurring RDS snapshots, export each snapshot to Amazon S3, and use an AWS Glue job to compare consecutive exports and generate change records.","Snapshot comparisons can identify differences between captured states, but they cannot recover every intervening change. Multiple updates, or an insert followed by a delete between snapshots, can be absent from the resulting change feed.",{"id":196,"title":197,"subtitle":152,"description":152,"questions":198},539,"Perform data transformation, feature engineering, and pre-processing",[],{"id":200,"title":201,"subtitle":152,"description":152,"questions":202},540,"Validate data quality and manage bias",[],{"id":204,"title":205,"subtitle":152,"description":152,"content":152,"coverImageUrl":152,"weight":206,"subcategories":207},147,"ML Model and Foundation Model (FM) Development",24,[208,212,216],{"id":209,"title":210,"subtitle":152,"description":152,"questions":211},541,"Choose appropriate modeling approaches for ML and AI solutions",[],{"id":213,"title":214,"subtitle":152,"description":152,"questions":215},542,"Train, fine-tune, and customize models for ML and AI solutions",[],{"id":217,"title":218,"subtitle":152,"description":152,"questions":219},543,"Analyze and evaluate the performance of ML and AI systems",[220],{"id":221,"subcategoryId":217,"content":222,"hint":223,"type":176,"isPublic":9,"answers":224},58175,"A machine learning engineer runs a TensorFlow training job on an Amazon EC2 GPU instance using an AWS Deep Learning Container. Each epoch contains 200 batches, and the optimizer performs one update per batch. The engineer intends to halve the learning rate every 5 epochs during a 20-epoch run. The training script uses tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=0.001, decay_steps=5, decay_rate=0.5, staircase=False). Training loss improves during the first few batches but then stops improving. Logs show that the learning rate becomes nearly zero during the first epoch, while gradients remain finite and nonzero. Which change best addresses the convergence issue while implementing the intended decay interval?","Check which unit of training progress the TensorFlow schedule uses, and relate that unit to the number of batches in an epoch.",[225,229,233,237],{"id":226,"questionId":221,"content":227,"hint":228,"isCorrect":182,"position":152,"matchAnswerId":152},261450,"Configure the optimizer with global_clipnorm=1.0 and retain the existing learning rate schedule.","Gradient clipping can limit excessively large gradients, but the observed problem is an incorrectly configured learning rate schedule. Clipping would not prevent the learning rate from becoming nearly zero or restore meaningful parameter updates.",{"id":230,"questionId":221,"content":231,"hint":232,"isCorrect":9,"position":152,"matchAnswerId":152},261451,"Set decay_steps=1000 and retain the initial learning rate and decay rate.","TensorFlow ExponentialDecay measures decay_steps in optimizer updates, not epochs. With 200 updates per epoch, 5 epochs correspond to 1,000 updates. This change makes the learning rate halve over each intended 5-epoch interval instead of decaying almost to zero during the first epoch.",{"id":234,"questionId":221,"content":235,"hint":236,"isCorrect":182,"position":152,"matchAnswerId":152},261452,"Keep decay_steps=5 and set staircase=True so that the learning rate changes only at discrete intervals.","This would halve the learning rate every 5 optimizer updates rather than every 5 epochs. Discrete decay would not correct the mismatch between the configured interval and the intended interval.",{"id":238,"questionId":221,"content":239,"hint":240,"isCorrect":182,"position":152,"matchAnswerId":152},261453,"Multiply initial_learning_rate by 200 and keep decay_steps=5 to compensate for the number of batches per epoch.","Increasing the initial learning rate does not correct the decay interval. The learning rate would still halve every 5 updates and rapidly become negligible. The much larger initial rate could also destabilize the earliest updates.",{"id":242,"title":243,"subtitle":152,"description":152,"content":152,"coverImageUrl":152,"weight":206,"subcategories":244},148,"Deployment and Orchestration of ML and AI Workflows",[245,270,274],{"id":246,"title":247,"subtitle":152,"description":152,"questions":248},544,"Manage deployment infrastructure for ML and AI model types",[249],{"id":250,"subcategoryId":246,"content":251,"hint":252,"type":176,"isPublic":9,"answers":253},58023,"A media company must run a trained video-classification model against 1,200 independent input shards in Amazon S3 each weekend. Each shard requires one NVIDIA GPU and approximately 75 minutes of processing. Results are needed the next day, not interactively. The existing container runs a command-line inference program that reads a shard and writes predictions to Amazon S3; it does not implement an HTTP inference server. The company cannot modify the container and wants AWS to provision and scale the compute capacity, with no running compute instances between workloads. Which solution meets these requirements?","Consider both the container's execution interface and which compute environment can automatically provision GPU capacity for queued work.",[254,258,262,266],{"id":255,"questionId":250,"content":256,"hint":257,"isCorrect":182,"position":152,"matchAnswerId":152},260789,"Create an AWS Batch unmanaged EC2 compute environment with GPU-capable instance types. Associate it with a job queue and rely on the job queue to launch instances when array-job children are waiting and terminate them when processing finishes.","An unmanaged compute environment requires the company to provision and manage its compute resources. The job queue does not automatically launch or terminate EC2 instances in an unmanaged environment, so this configuration does not satisfy the infrastructure-management requirement.",{"id":259,"questionId":250,"content":260,"hint":261,"isCorrect":182,"position":152,"matchAnswerId":152},260790,"Register the existing container as an Amazon SageMaker AI model and launch a GPU-backed Batch Transform job against the S3 input shards, using the container unchanged.","Batch Transform supports offline inference and temporary compute resources, but a custom container must implement the SageMaker inference serving contract. The existing command-line-only container does not provide that interface, and the company cannot modify it.",{"id":263,"questionId":250,"content":264,"hint":265,"isCorrect":182,"position":152,"matchAnswerId":152},260791,"Create an AWS Batch managed Fargate compute environment. Define a job that requests one GPU and submit an array job whose children each process one shard.","Fargate does not support GPU resource requests for AWS Batch jobs. Although it removes instance management and supports containerized batch processing, it cannot run this GPU-dependent workload.",{"id":267,"questionId":250,"content":268,"hint":269,"isCorrect":9,"position":152,"matchAnswerId":152},260792,"Create an AWS Batch managed EC2 compute environment with GPU-capable instance types and minimum vCPUs set to zero. Associate it with a job queue, define a job that requests one GPU, and submit an array job whose children each process one shard.","AWS Batch can run the existing command-line container without an inference server. A managed EC2 compute environment provisions GPU-capable capacity for queued jobs and can scale down to zero instances when idle. Each array child can use its array index to select an independent input shard and write predictions to S3.",{"id":271,"title":272,"subtitle":152,"description":152,"questions":273},545,"Provision and configure resources for ML and AI workloads based on existing architecture and requirements",[],{"id":275,"title":276,"subtitle":152,"description":152,"questions":277},546,"Implement automated orchestration and continuous integration and continuous delivery (CI\u002FCD) pipelines for MLOps and AI workloads",[],{"id":279,"title":280,"subtitle":152,"description":152,"content":152,"coverImageUrl":152,"weight":206,"subcategories":281},149,"Operating, Monitoring, and Securing ML and AI Solutions",[282,286,311],{"id":283,"title":284,"subtitle":152,"description":152,"questions":285},547,"Monitor ML and AI model inference and performance",[],{"id":287,"title":288,"subtitle":152,"description":152,"questions":289},548,"Optimize and manage ML and AI infrastructure costs and performance",[290],{"id":291,"subcategoryId":287,"content":292,"hint":293,"type":176,"isPublic":9,"answers":294},58419,"A company is selecting an Amazon EC2 instance configuration for a synchronous image-classification API. Traffic alternates between sparse requests and sustained bursts. The API must return a complete response within 100 milliseconds for at least 99% of requests during both traffic patterns.\n\nAn ML engineer tests four configurations using representative requests. Each configuration meets the required throughput and model accuracy. The client-observed latency includes network transport, preprocessing, batch waiting, model execution, and response delivery. The table shows the higher p99 latency observed across the sparse-traffic and burst-traffic tests. Prices are illustrative.\n\n| Configuration | Hourly cost per instance | p99 model execution latency | p99 client-observed latency |\n|---|---:|---:|---:|\n| Compute-optimized CPU, no batching | $0.60 | 85 ms | 125 ms |\n| Inference accelerator, batches of up to 32 requests | $0.85 | 18 ms | 145 ms |\n| Small GPU instance, no batching | $1.20 | 42 ms | 78 ms |\n| Large GPU instance, no batching | $2.40 | 24 ms | 58 ms |\n\nThe company will use the same fixed instance count for every configuration. It must select one of the tested configurations without further serving changes. Which choice meets the response-time requirement at the lowest instance cost?","Identify which latency measurement matches the application's deadline before comparing prices.",[295,299,303,307],{"id":296,"questionId":291,"content":297,"hint":298,"isCorrect":182,"position":152,"matchAnswerId":152},262507,"Select the compute-optimized CPU configuration because its model execution p99 is below 100 milliseconds and it has the lowest hourly price.","The requirement applies to the complete API response, not just model execution. Although model execution takes 85 milliseconds at p99, client-observed p99 latency reaches 125 milliseconds and fails the requirement.",{"id":300,"questionId":291,"content":301,"hint":302,"isCorrect":9,"position":152,"matchAnswerId":152},262508,"Select the small GPU configuration because its client-observed p99 latency meets the deadline and its hourly cost is the lowest among compliant configurations.","The small GPU configuration has a client-observed p99 latency of 78 milliseconds across both traffic patterns, meeting the 100-millisecond requirement. Only the two GPU configurations meet that requirement, and the small GPU configuration costs less. With the same instance count, it also provides the lowest compliant fleet cost.",{"id":304,"questionId":291,"content":305,"hint":306,"isCorrect":182,"position":152,"matchAnswerId":152},262509,"Select the large GPU configuration because its lower client-observed p99 latency makes it the lowest-cost configuration that satisfies the response-time requirement.","The large GPU configuration meets the latency requirement, but the small GPU configuration also meets it at half the hourly instance price. The additional latency reduction is not required and does not justify calling this the lowest-cost compliant choice.",{"id":308,"questionId":291,"content":309,"hint":310,"isCorrect":182,"position":152,"matchAnswerId":152},262510,"Select the inference-accelerator configuration because it has the lowest model execution latency and costs less than either GPU configuration.","Its 18-millisecond model execution latency excludes other delays, including batch waiting. The measured client-observed p99 latency is 145 milliseconds, which violates the 100-millisecond response-time requirement.",{"id":312,"title":313,"subtitle":152,"description":152,"questions":314},549,"Secure ML and AI workloads and model endpoints",[],[],{"id":317,"code":77,"slug":72,"dateLastDay":152,"whatChanged":152,"pageSlug":78},23,[],[320,323,326,329,332,335,338,341,344,347,350,353,356,359],{"id":5,"slug":6,"description":7,"questionCount":8,"isActive":9,"datePublicModified":10,"difficulty":321,"examVersion":322},{"title":12,"subtitle":13,"slug":14,"coverImageUrl":15},{"slug":6,"pageSlug":14},{"id":18,"slug":19,"description":20,"questionCount":21,"isActive":9,"datePublicModified":10,"difficulty":324,"examVersion":325},{"title":23,"subtitle":24,"slug":25,"coverImageUrl":26},{"slug":19,"pageSlug":25},{"id":29,"slug":30,"description":31,"questionCount":32,"isActive":9,"datePublicModified":10,"difficulty":327,"examVersion":328},{"title":34,"subtitle":35,"slug":36,"coverImageUrl":37},{"slug":30,"pageSlug":36},{"id":40,"slug":41,"description":42,"questionCount":43,"isActive":9,"datePublicModified":10,"difficulty":330,"examVersion":331},{"title":45,"subtitle":46,"slug":47,"coverImageUrl":37},{"slug":41,"pageSlug":47},{"id":50,"slug":51,"description":52,"questionCount":53,"isActive":9,"datePublicModified":10,"difficulty":333,"examVersion":334},{"title":55,"subtitle":56,"slug":57,"coverImageUrl":37},{"slug":51,"pageSlug":57},{"id":60,"slug":61,"description":62,"questionCount":63,"isActive":9,"datePublicModified":64,"difficulty":336,"examVersion":337},{"title":66,"subtitle":67,"slug":68,"coverImageUrl":37},{"slug":61,"pageSlug":68},{"id":71,"slug":72,"description":73,"questionCount":74,"isActive":9,"datePublicModified":64,"difficulty":339,"examVersion":340},{"title":76,"subtitle":77,"slug":78,"coverImageUrl":37},{"slug":72,"pageSlug":78},{"id":81,"slug":82,"description":83,"questionCount":84,"isActive":9,"datePublicModified":64,"difficulty":342,"examVersion":343},{"title":86,"subtitle":87,"slug":88,"coverImageUrl":37},{"slug":82,"pageSlug":88},{"id":91,"slug":92,"description":93,"questionCount":94,"isActive":9,"datePublicModified":64,"difficulty":345,"examVersion":346},{"title":96,"subtitle":97,"slug":98,"coverImageUrl":99},{"slug":92,"pageSlug":98},{"id":102,"slug":103,"description":104,"questionCount":105,"isActive":9,"datePublicModified":64,"difficulty":348,"examVersion":349},{"title":107,"subtitle":108,"slug":109,"coverImageUrl":37},{"slug":103,"pageSlug":109},{"id":112,"slug":113,"description":114,"questionCount":115,"isActive":9,"datePublicModified":116,"difficulty":351,"examVersion":352},{"title":118,"subtitle":119,"slug":120,"coverImageUrl":37},{"slug":113,"pageSlug":120},{"id":123,"slug":124,"description":125,"questionCount":126,"isActive":9,"datePublicModified":127,"difficulty":354,"examVersion":355},{"title":129,"subtitle":130,"slug":131,"coverImageUrl":132},{"slug":124,"pageSlug":131},{"id":135,"slug":136,"description":137,"questionCount":138,"isActive":9,"datePublicModified":127,"difficulty":357,"examVersion":358},{"title":140,"subtitle":141,"slug":142,"coverImageUrl":143},{"slug":136,"pageSlug":142},{"id":146,"slug":147,"description":148,"questionCount":149,"isActive":9,"datePublicModified":127,"difficulty":360,"examVersion":361},{"title":96,"subtitle":97,"slug":98,"coverImageUrl":99},{"slug":147,"pageSlug":152},[],[364,370,376,381,386,390,394,398,402,406],{"id":5,"slug":19,"examVersionId":365,"description":366,"difficulty":367,"examVersion":368,"lessonsCount":369},3,"Unlock your cloud computing potential with our interactive AWS course designed for the AWS Certified Cloud Practitioner exam. 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