How Many Questions Of MLS-C01 Exam Engine

NEW QUESTION 1
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset
Which tool should be used to improve the validation accuracy?

• A. Amazon Comprehend syntax analysts and entity detection
• B. Amazon SageMaker BlazingText allow mode
• C. Natural Language Toolkit (NLTK) stemming and stop word removal
• D. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers

Answer: D

NEW QUESTION 2
A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%
What should the Specialist consider to fix this issue1?

• A. A longer training time
• B. Making the network larger
• C. Using a different optimizer
• D. Using some form of regularization

Answer: D

NEW QUESTION 3
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

• A. Receiver operating characteristic (ROC) curve
• B. Misclassification rate
• C. Root Mean Square Error (RM&)
• D. L1 norm

Answer: A

NEW QUESTION 4
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
• Real-time analytics
• Interactive analytics of historical data
• Clickstream analytics
• Product recommendations
Which services should the Specialist use?

• A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
• B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
• C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
• D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: A

NEW QUESTION 5
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning
The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:
• Solution simplicity
• Fast development time
• Low cost
• High flexibility
What technologies meet the company's requirements?

• A. Amazon S3 and Amazon Athena
• B. Amazon Redshift and AWS Glue
• C. Amazon DynamoDB and DynamoDB Accelerator (DAX)
• D. Amazon RDS and Amazon ES

Answer: B

NEW QUESTION 6
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

• A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
• C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
• D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Answer: B

Explanation:
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

NEW QUESTION 7
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist
How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

• A. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group
• B. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset
• C. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC toaccess the S3 bucket
• D. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enable
• E. Generate an S3 pre-signed URL for access to data in the bucket

Answer: B

NEW QUESTION 8
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data
Which of the following services can feed data to the MapReduce jobs? (Select TWO )

• A. AWSDMS
• B. Amazon Kinesis
• C. AWS Data Pipeline
• D. Amazon Athena
• E. Amazon ES

Answer: BD

NEW QUESTION 9
The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

• A. The model predicts both the trend and the seasonality well.
• B. The model predicts the trend well, but not the seasonality.
• C. The model predicts the seasonality well, but not the trend.
• D. The model does not predict the trend or the seasonality well.

Answer: D

NEW QUESTION 10
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not
Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

• A. Seq2seq
• B. XGBoost
• C. K-means
• D. Random Cut Forest (RCF)

Answer: C

NEW QUESTION 11
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )

• A. An AWS KMS key policy that allows access to the customer master key (CMK)
• B. A SageMaker notebook security group that allows access to Amazon S3
• C. An 1AM role that allows access to the specific S3 bucket
• D. A permissive S3 bucket policy
• E. An S3 bucket owner that matches the notebook owner
• F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

Answer: ACF

NEW QUESTION 12
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes
* Start the workflow as soon as data is uploaded to Amazon S3
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3
* Store the results of joining datasets in Amazon S3
* If one of the jobs fails, send a notification to the Administrator Which configuration will meet these requirements?

• A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
• B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
• C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
• D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure

Answer: A

NEW QUESTION 13
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
• Profiles for all past and existing customers
• Profiles for all past and existing insured pets
• Policy-level information
• Premiums received
• Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• B. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segment
• D. Find similar profiles on social media
• E. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segment
• F. Find similar profiles on social media

Answer: C

NEW QUESTION 14
A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker
When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization?
Choose the maximum number of hyperparameters supported by

• A. Amazon SageMaker to search the largest number of combinations possible
• B. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value.
• C. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
• D. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments

Answer: C

NEW QUESTION 15
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

• A. Poisson distribution ,
• B. Uniform distribution
• C. Normal distribution
• D. Binomial distribution

Answer: D

NEW QUESTION 16
A Machine Learning Specialist wants to determine the appropriate SageMakerVariant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5
Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMakervariantinvocationsPerinstance setting?

• A. 10
• B. 30
• C. 600
• D. 2,400

Answer: C

NEW QUESTION 17
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
• Two dense layers one output neuron
• 100 neurons in each layer
• 100 epochs
• Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

• A. Early stopping
• B. Random initialization of weights with appropriate seed
• C. Increasing the number of epochs
• D. Adding another layer with the 100 neurons

Answer: D

NEW QUESTION 18
A Machine Learning Specialist needs to be able to ingest streaming data and store it in Apache Parquet files for exploration and analysis. Which of the following services would both ingest and store this data in the correct format?

• A. AWSDMS
• B. Amazon Kinesis Data Streams
• C. Amazon Kinesis Data Firehose
• D. Amazon Kinesis Data Analytics

Answer: C

NEW QUESTION 19
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata. Which approach meets trfese requirements?

• A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
• B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
• C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
• D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Answer: B

NEW QUESTION 20
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?

• A. Create a schema in the AWS Glue Data Catalog of the incoming data forma
• B. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
• C. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
• D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL databas
• E. Have the Analysts query and run dashboards from the RDS database.
• F. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.

Answer: A

NEW QUESTION 21
A Machine Learning Specialist is working with a large cybersecurily company that manages security events in real time for companies around the world The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested The company also wants be able to save the results in its data lake for later processing and analysis
What is the MOST efficient way to accomplish these tasks'?

• A. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection Then use Kinesis Data Firehose to stream the results to Amazon S3
• B. Ingest the data into Apache Spark Streaming using Amazon EM
• C. and use Spark MLlib with k-means to perform anomaly detection Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake
• D. Ingest the data and store it in Amazon S3 Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.
• E. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data

Answer: B

NEW QUESTION 22
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A. Increase the training data by adding variation in rotation for training images.
• B. Increase the number of epochs for model training.
• C. Increase the number of layers for the neural network.
• D. Increase the dropout rate for the second-to-last layer.

Answer: B

NEW QUESTION 23
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

• A. Load a smaller subset of the data into the SageMaker notebook and train locall
• B. Confirm that the training code is executing and the model parameters seem reasonabl
• C. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
• D. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstanc
• E. Train on a small amount of the data to verify the training code and hyperparameter
• F. Go back toAmazon SageMaker and train using the full dataset
• G. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatiblewith Amazon SageMake
• H. Initiate a SageMaker training job using the full dataset from the S3 bucket usingPipe input mode.
• I. Load a smaller subset of the data into the SageMaker notebook and train locall
• J. Confirm that the training code is executing and the model parameters seem reasonabl
• K. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Answer: A

NEW QUESTION 24
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