MLS-C01 : AWS Certified Machine Learning Specialty 2025 Exam

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MLS-C01
AWS Certified Machine Learning Specialty (MLS-C01)
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SAMPLE QUESTIONS
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Question: 894
You build an RNN with two stacked LSTM layers (64 units each) in SageMaker to forecast hourly energy usage from a 24-hour sequence. You use tanh activation, a batch size of 32, and a learning rate of
0.01. After 50 epochs, the model predicts flat values across all hours. What’s the most likely cause, and how should you fix it?
nishing gradients; switch to ReLU activation rning rate too high; reduce it to 0.001 ufficient capacity; add a third LSTM layer
Data not normalized; scale inputs to [0, 1] er: B
nation: Flat predictions in RNNs often result from a learning rate too high (0.01), causing un that prevent the model from learning temporal patterns. Reducing it to 0.001 stabilizes trai
ng the LSTM to capture dependencies. Vanishing gradients are mitigated by LSTMs, and lization or capacity isn’t indicated as the primary issue.
ion: 895
eloper writes an R script in SageMaker to train a logistic regression model on a 20 GB datas th columns "age", "income", and "target". The script uses glm() and must handle missing va ale features. Which snippet is correct?
ary(aws.s3)df <- s3read_using(read.csv, bucket="bucket", object="data.csv")df[is.na(df)] <- ", "income")] <- scale(df[, c("age", "income")])model <- glm(target ~ age + income, data=d
=binomial) ary(boto3)
Va
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Answ
Expla stable
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allowi norma
Quest
A dev et in
S3, wi lues
and sc
1. libr 0df[,
c("age f,
family
2. libr
df <- read.csv("s3://bucket/data.csv") df <- na.omit(df)
df$age <- (df$age - mean(df$age)) / sd(df$age) df$income <- scale(df$income)
model <- glm(target ~ ., data=df, family="binomial")
3. library(data.table)
df <- fread("s3://bucket/data.csv") df[is.na(df)] <- median(df, na.rm=TRUE)
df[, c("age", "income")] <- lapply(df[, c("age", "income")], scale) model <- glm(target ~ age + income, family=binomial(link="logit"))
4. library(aws.s3)
df <- s3get_object(bucket="bucket", key="data.csv") df <- impute(df, method="mean")
df[, c("age", "income")] <- normalize(df[, c("age", "income")]) model <- logist(target ~ age + income, data=df)
Answer: A
ion: 896
ncial institution is building a fraud detection system using machine learning and has decided on S3 as the primary storage medium for its datasets, which include transactional records an mer profiles. The data engineering team needs to ensure that the S3 bucket can handle a grow of data—currently 50 TB and expected to double annually—while supporting concurrent
rite operations from multiple SageMaker training jobs. Which configuration optimizes the S for this ML use case?
able S3 versioning and configure lifecycle policies to transition older data to S3 Glacier, usi S3 storage class
ate an S3 bucket with Requester Pays enabled and use S3 Standard-Infrequent Access for al
up S3 bucket with Transfer Acceleration and multipart upload enabled, using S3 Intelligent for cost optimization
nfigure S3 bucket with cross-region replication to an EFS file system and enable strong tency
er: C
nation: For a fraud detection ML system with large, growing datasets and concurrent SageM S3 must be optimized for performance and cost. Transfer Acceleration and multipart uploa
Explanation: The correct R script uses aws.s3’s s3read_using to read from S3, replaces NAs with 0, scales features with scale(), and fits a binomial GLM. Option B uses Python’s boto3, Option C misuses median and GLM syntax, and Option D has invalid functions (impute, normalize, logist).
Quest
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Amaz d
custo ing
volume
read/w 3
bucket
1. En ng
default
2. Cre l
objects
3. Set -
Tiering
4. Co
consis Answ
Expla aker
access, d
enhance upload speed and handle large files efficiently, while Intelligent-Tiering automatically adjusts storage costs based on access patterns. Versioning with Glacier is less optimal for frequent access, Requester Pays shifts costs inappropriately for internal use, and cross-region replication to EFS is impractical as EFS is a separate service, not an S3 feature.
Question: 897
In a SageMaker training job, you optimize a neural network with a custom loss function combining L1 and L2 penalties. The dataset has 5 million rows, and you use mini-batch gradient descent with a batch
size of 128 and learning rate of 0.005. After 40 epochs, the loss converges to 0.4 on training data but fluctuates between 0.7 and 0.9 on validation data. What’s the most likely cause, and how should you address it?
1. Loss mismatch; switch to pure L2 loss
2. Learning rate too low; increase it to 0.01
3. Batch size too small; increase it to 512
4. Overfitting; add dropout with rate=0.3 to hidden layers Answer: D
nation: Fluctuating validation loss with converged training loss indicates overfitting, where t memorizes training data. Adding dropout (rate=0.3) regularizes the network, reducing overfi abilizing validation performance without altering the optimization process.
ion: 898
ufacturing firm is preparing a 26 TB dataset of production logs in S3 (Parquet format) for a to predict quality. The dataset includes defect rates, pressures, and timestamps over 5 years. ust create a histogram of defect rates to assess distribution, interpret the p-value from a t-te ring pressures across shifts, and perform cluster analysis with an elbow plot to optimize clus argeting 2-4 clusters). Which approach best analyzes and visualizes this data?
up an AWS Lambda function: plot a histogram with matplotlib.hist(), compute p-value with ded formula, and approximate clustering with a static size
AWS Glue with PySpark: create a histogram with pyplot.hist(), calculate p-value with a cu nd perform hierarchical clustering with linkage() and an elbow plot from scipy
nfigure Amazon QuickSight: build a histogram visual, estimate p-value manually, and skip ing due to limited functionality
ploy Amazon SageMaker with Jupyter Notebook: generate a histogram with seaborn.histplot te p-value with scipy.stats.ttest_ind(), and use KMeans with elbow_plot() from sklearn
er: D
Expla he
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and st
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team m st
compa ter
size (t
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Explanation: Amazon SageMaker with Jupyter Notebook handles a 26 TB dataset efficiently: seaborn.histplot() visualizes defect rate distribution, ttest_ind() computes a precise p-value, and KMeans with an elbow plot optimizes cluster size. Glue lacks native statistical tools, QuickSight skips clustering, and Lambda is unsuitable for complex analysis.
Question: 899
You deploy a SageMaker model for real-time fraud detection using a gradient boosting classifier trained
on 5 million transactions. The model runs on an ml.m5.xlarge instance, and you need to update it every 15 minutes with 10,000 new transactions. Which online retraining strategy would minimize downtime?
1. Use a shadow endpoint with incremental updates via xgboost.train()
2. Retrain in batch mode every 15 minutes on an ml.p3.2xlarge instance
3. Implement a SageMaker endpoint with online SGD updates
4. Use a Lambda function to trigger full retraining Answer: A
without downtime, testing the new model in parallel before promotion. This ensures real-ti bility while incorporating new data efficiently.
ion: 900
pany deploys a SageMaker endpoint with a PyTorch model on an ml.m5.large instance, han quests/minute. They need to add A/B testing for a new model version with 10% traffic. How they configure this?
ploy a second endpoint, use Application Load Balancer to split 10% traffic, and monitor wit Watch
ate a new endpoint variant with the new model, set its weight to 0.1, and update the existing nt
SageMaker Shadow Mode, deploy the new model as a shadow variant, and allocate 10% tr nfigure SageMaker Multi-Model Endpoint, add the new model, and route 10% requests via nce logic
er: B
nation: SageMaker endpoint variants allow A/B testing by assigning weights (e.g., 0.1 for 10 to the new model within the same endpoint, simplifying management. ALB requires separa nts, Shadow Mode is for testing without live traffic, and Multi-Model Endpoints don’t supp
Explanation: A shadow endpoint with incremental updates via xgboost.train() allows seamless model updates me
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Question: 901
A manufacturing firm is processing a 18 TB dataset of sensor logs in S3 (CSV format), including temperatures, pressures, and failure flags, to minimize equipment downtime. The goal is to predict failure probability per machine with 92% accuracy, currently at 65% with manual checks. The ML team must decide if ML is appropriate, choose supervised vs. unsupervised learning, and select a model type,
considering labeled failure data. Which solution best frames this business problem?
1. Avoid ML: implement an AWS Glue job to flag machines above temperature thresholds, as ML is too complex
2. Frame as an unsupervised recommendation problem: use SageMaker with Factorization Machines to suggest maintenance schedules without failure predictions
3. Frame as a supervised classification problem: use SageMaker with XGBoost to predict failure probability, training on failure flags
4. Frame as a supervised regression problem: use SageMaker with Linear Learner to predict failure times as continuous values
er: C
nation: Predicting failure probability with 92% accuracy justifies ML over 65% manual chec vised learning leverages labeled failure flags, and classification (XGBoost) suits the probabili me. Recommendation lacks failure focus, rule-based flagging underperforms, and regression gns with the binary prediction needed.
ion: 902
ing company is processing a 32 TB dataset of player logs in S3 (JSON format), including s mes, and churn flags, to reduce churn by 20%. The business aims to predict churn probabilit with 85% accuracy, currently at 55% with heuristic rules. The ML team must evaluate ML ability, select supervised vs. unsupervised learning, and choose a model type, considering lab
ata. Which solution best frames this business problem?
me as a supervised classification problem: use SageMaker with XGBoost to predict churn bility, training on churn flags
me as an unsupervised clustering problem: use SageMaker with K-Means to group players b mes, then analyze churn patterns
oid ML: implement an AWS Lambda function with playtime-based churn thresholds, as ML es excessive tuning
me as a supervised regression problem: use SageMaker with Linear Learner to predict churn tinuous values
Answ
Expla ks.
Super stic
outco misali
Quest
A gam cores,
playti y per
player
applic eled
churn d
1. Fra proba
2. Fra y
playti
3. Av
requir
4. Fra times
as con Answer: A
Explanation: Predicting churn probability with 85% accuracy warrants ML over 55% heuristic rules. Supervised learning fits the labeled churn flags, and classification (XGBoost) addresses the probabilistic outcome. Clustering lacks predictive precision, rule-based thresholds underperform, and regression misaligns with the binary prediction needed.
Question: 903
You’re training an RNN with 30 GB of time-series data using AWS Batch and Spot Instances on 5 p3.2xlarge instances. The job fails after 3 hours. How do you fix it?
1. Use On-Demand p3.2xlarge with no checkpointing and a 10-hour timeout
2. Add checkpointing to S3 every 10 epochs, set retries to 3, and use a 15-hour timeout
3. Switch to g4dn.xlarge Spot Instances with no retries
nation: Checkpointing to S3 every 10 epochs and retries handle Spot interruptions, ensuring etion on p3.2xlarge within 15 hours. On-Demand is costlier, and g4dn lacks GPU capacity.
ion: 904
rt city initiative is implementing an ML model for traffic optimization and needs to transfor ing traffic camera data (200 MB/second) from Kinesis Data Streams. The transformation mu n transit, aggregating vehicle counts by lane every 15 seconds, filtering out invalid frames, as ORC in S3 partitioned by date (yyyy/MM/dd). Which solution best implements this data ormation in transit?
up Amazon Kinesis Data Firehose with a Lambda function to aggregate and filter data, writ hout partitioning
ploy Amazon EMR with Apache Spark Streaming, a 15-second micro-batch, and a custom jo ate, filter, and write ORC to S3
nfigure AWS Batch with a Docker container running Apache Spark to process Kinesis data i batches and save ORC to S3
AWS Glue with a streaming ETL job, a PySpark script to aggregate by lane and filter inval and output to S3 with dynamic partitioning
er: D
Run on SageMaker with Spot Instances and EBS storage Answer: B
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Explanation: AWS Glue’s streaming ETL with PySpark transforms Kinesis data in transit, aggregating by lane, filtering invalid frames, and partitioning ORC output to S3. EMR with Spark Streaming is complex, AWS Batch with Spark lacks streaming support, and Firehose with Lambda doesn’t support advanced partitioning.
Question: 905
Which compute resource would be the most suitable for training a large-scale deep learning model that requires high computational power and parallel processing?
1. Standard CPU
2. High Memory Instance
3. GPU Instance
4. Low-Cost T2 Instance Answer: C
ion: 906
ergy analytics firm is implementing an ML model for demand forecasting and needs to orche ine that ingests real-time meter data (200 MB/second) into S3 and processes monthly batch
TB, Parquet) from S3 with trend analysis. The streaming pipeline requires a 2-second latency, ch pipeline must run on the 1st of each month. Which services best orchestrate this hybrid ne?
nfigure Amazon Kinesis Data Streams with 40 shards and Amazon Data Firehose for batch sing, orchestrated by Lambda
ploy Amazon Managed Service for Apache Flink for streaming with a 2-second window and on EMR for batch processing, managed by Step Functions
Amazon Kinesis Data Firehose for streaming to S3 with a 2-second buffer and AWS Glue TL with trend analysis, triggered by CloudWatch Events
up Amazon EMR with Spark Streaming for real-time ingestion and AWS Glue for batch sing, triggered by Data Pipeline
er: C
nation: Kinesis Data Firehose ingests streaming meter data (200 MB/s) into S3 with a 2-seco while AWS Glue processes batch Parquet data monthly with trend analysis, orchestrated by Watch Events. Managed Flink with EMR is complex, Kinesis Streams with Firehose misalig nd EMR with Glue lacks streaming efficiency.
Explanation: GPU instances are specifically optimized for high computational power and parallel processing, making them ideal for training large-scale deep learning models.
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Question: 907
You deploy a k-means model in SageMaker to cluster IoT sensor data with 20 features, setting k=8 and using Euclidean distance. After clustering, you notice that one cluster contains 80% of the data points. What is the most likely issue, and how should you resolve it?
1. Uneven cluster sizes; switch to DBSCAN with eps=0.5
2. Wrong k; use the elbow method to find optimal k
3. Features on different scales; normalize data to [0, 1]
4. Outliers; remove points beyond 2 standard deviations Answer: C
ion: 908
e-commerce company is designing a system to ingest real-time customer clickstream data ns of users across multiple regions. The data, which includes user IDs, timestamps, product I ssion durations, must be collected at scale and stored in a data lake on Amazon S3 for down ne learning tasks. The ingestion pipeline must handle bursts of up to 10 GB/s, ensure low lat ovide fault tolerance. Which combination of AWS services and configurations would best m equirements while minimizing operational overhead?
up Amazon SQS with a FIFO queue, process messages with an Auto Scaling group of EC2 ces, and upload data to S3 in Parquet format using the AWS SDK
ploy an Amazon MSK (Managed Streaming for Kafka) cluster with 10 partitions, configure a consumer to batch data, and use AWS Lambda to write to S3 every 5 minutes
Amazon Kinesis Data Streams with 50 shards, enable enhanced fan-out, and write data dire ng Kinesis Data Firehose with a buffer interval of 60 seconds
Amazon API Gateway with a WebSocket connection to ingest data, process it with AWS ync, and store it in S3 via a GraphQL mutation every 10 seconds
er: C
nation: For high-throughput, real-time ingestion at 10 GB/s with low latency and fault tolera on Kinesis Data Streams is ideal due to its scalability and ability to handle massive data stre 0 shards (each supporting 1 MB/s ingress), it can manage the load, and enhanced fan-out en
Explanation: K-means uses Euclidean distance, which is sensitive to feature scales. Unnormalized features can dominate the distance metric, causing imbalanced clusters. Normalizing data to [0, 1] ensures equal contribution from all features, improving cluster balance. Adjusting k or switching algorithms may help but doesn’t address the scaling issue directly.
Quest
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low-latency delivery to consumers. Kinesis Data Firehose seamlessly integrates with S3, buffering data (e.g., 60 seconds) to optimize writes, reducing operational complexity compared to custom solutions. MSK is powerful but requires more management for consumers, SQS isn’t suited for such high throughput, and API Gateway with WebSocket is impractical for this scale of raw data ingestion.
Question: 909
A pharmaceutical company is building an ML model for drug discovery and needs to ingest streaming
sensor data (90 MB/second) from lab equipment into an S3 data lake. The ingestion must aggregate data by experiment ID every 30 seconds, partition by date and equipment ID (yyyy/MM/dd/equipID), and handle late-arriving events up to 2 minutes. Which streaming ingestion solution is most appropriate?
1. Use Amazon Managed Service for Apache Flink with a 30-second tumbling window, late event handling (2 minutes), and a partitioned S3 sink
2. Configure Amazon Kinesis Data Firehose with a 30-second buffer and a Lambda function for aggregation and partitioning, with no late event support
3. Deploy Amazon EMR with Apache Spark Streaming, a 30-second micro-batch, and a custom script to aggregate and partition to S3
on data to S3 every 30 seconds er: A
nation: Managed Service for Apache Flink excels at streaming with a 30-second tumbling wi ent handling (2 minutes), and custom S3 sinks with partitioning (date/equipID). Firehose lac upport, EMR with Spark is batch-heavy, and Kinesis Streams with Lambda requires more c
ion: 910
ia company trains a SageMaker model to classify video content as "viral" or "non-viral" usi samples (20% viral). The confusion matrix on a test set is: TP = 1,500, FP = 500, TN = 7,
00. What is the recall, and what does it imply for the model’s performance?
0, showing moderate success in predicting viral videos
3, suggesting high reliability in detecting non-viral content 5, indicating 75% of viral videos are correctly identified
0, reflecting strong overall classification performance er: C
nation: Recall = TP / (TP + FN) = 1,500 / (1,500 + 500) = 0.75. This means 75% of actual viral
Set up Amazon Kinesis Data Streams with 18 shards and a consumer Lambda to aggregate and partiti
Answ
Expla ndow,
late ev ks late
event s ustom
logic.
Quest
A med ng
10,000 500,
FN = 5
1. 0.6
2. 0.8
3. 0.7
4. 0.9
Answ Expla
videos are correctly classified, implying the model is reasonably effective at identifying viral content but misses 25% of viral cases, which could be critical depending on the business use case.
Question: 911
A data science team trains an ML model on SageMaker with a 1 TB dataset, requiring persistent block storage with snapshots for rollback (e.g., volume size 1024 GiB, IOPS 3000). The storage must attach to ml.c5.xlarge instances and encrypt data at rest. What should they use?
1. Deploy Amazon EFS with SageMaker integration
2. Use Amazon EBS with gp3 volumes and encryption
3. Configure Amazon FSx with block storage
4. Set up Amazon S3 with lifecycle policies Answer: B
ion: 912
re tasked with deploying a new model version using Amazon SageMaker and need to ensure al disruption to your users while switching from the old model. What deployment strategy s nsider?
nary deployment ling update
e/Green deployment at-once deployment
er: C
nation: Blue/Green deployment allows for seamless switching between the old and new mode ns, minimizing user disruption and allowing for easy rollback if issues arise.
ion: 913
com provider is analyzing a 17 TB dataset of signal logs in S3 (JSON format) for an ML m quality. The dataset includes strengths, latencies, and timestamps over 4 years. The team ne
Explanation: Amazon EBS gp3 volumes provide persistent block storage (e.g., 1024 GiB, 3000 IOPS) with snapshots and encryption, attaching to SageMaker instances like ml.c5.xlarge. EFS is file-based, FSx is for specific file protocols, and S3 is object storage, none of which offer block-level persistence.
Quest
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2. Rol
3. Blu
4. All- Answ
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Quest
A tele odel to
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create a scatter plot of strength vs. latency, calculate the Pearson correlation between these variables, and perform hierarchical clustering with a dendrogram to diagnose network segments. Which solution best accomplishes this visualization and analysis?
1. Configure Amazon QuickSight: build a scatter plot visual, estimate correlation manually, and skip clustering due to lack of support
2. Deploy Amazon SageMaker with Jupyter Notebook: create a scatter plot with seaborn.scatterplot(), calculate correlation with pandas.corr(), and use KMeans with a static cluster size
3. Use AWS Glue with PySpark: generate a scatter plot with pyplot.scatter(), compute correlation with corr(), and perform hierarchical clustering with scipy.cluster.hierarchy.dendrogram()
4. Set up an AWS Lambda function: plot a scatter with matplotlib.scatter(), compute correlation with a custom formula, and approximate clustering without visualization
Answer: C
ion: 914
ontext of model evaluation, what does a high AUC-ROC value signify regarding the model to classify positive and negative instances?
model has poor classification accuracy. model's predictions are unreliable. model is not overfitting.
model performs well, effectively distinguishing between positive and negative instances. er: D
nation: A high AUC-ROC value indicates that the model performs well in distinguishing bet positive and negative instances, reflecting strong classification capabilities.
ion: 915
ical imaging dataset has inconsistent lighting across X-rays. Which preprocessing step stand ages?
AWS Rekognition to auto-tag images nvert images to grayscale and crop edges
cale pixel values to [0, 1] and apply histogram equalization ete underexposed/overexposed images
Explanation: AWS Glue with PySpark scales for a 17 TB dataset: pyplot.scatter() visualizes strength vs. latency, corr() computes Pearson correlation, and dendrogram() from scipy diagnoses hierarchical clustering. SageMaker lacks hierarchical clustering, QuickSight skips clustering, and Lambda is impractical for large-scale visualization.
Quest
In the c 's
ability
1. The
2. The
3. The
4. The Answ
Expla ween
Quest
A med ardizes
the im
1. Use
2. Co
3. Res
4. Del Answer: C
Explanation: Histogram equalization normalizes contrast, and rescaling ensures consistent input ranges. Grayscale conversion alone doesn’t fix lighting, and deleting images reduces dataset size unnecessarily.
Question: 916
What is the most common reason for a model to converge to a local minimum instead of a global minimum during training?
1. The choice of optimization algorithm.
2. The complexity of the dataset.
3. The size of the training data.
4. The presence of non-convex loss functions. Answer: D
hm to converge to a local minimum rather than the global minimum.
Explanation: Non-convex loss functions can lead to multiple local minima, causing the optimization algorit

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