MLS-C01 NEW PRACTICE MATERIALS, CERTIFICATION MLS-C01 EXAM

MLS-C01 New Practice Materials, Certification MLS-C01 Exam

MLS-C01 New Practice Materials, Certification MLS-C01 Exam

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To qualify for the AWS Certified Machine Learning - Specialty exam, candidates must have at least one year of experience in developing machine learning models on AWS and must possess a deep understanding of AWS services for data analytics, data warehousing, and data processing. MLS-C01 Exam consists of 65 multiple-choice and multiple-response questions that must be completed within 180 minutes. To pass the exam, candidates must score at least 72% on the exam. Upon passing the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification, which is valid for three years. AWS Certified Machine Learning - Specialty certification is recognized globally and demonstrates an individual’s expertise in the field of machine learning on the AWS platform.

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The AWS-Certified-Machine-Learning-Specialty certification is ideal for professionals who want to advance their careers in the field of machine learning. AWS Certified Machine Learning - Specialty certification is recognized globally and is valued by employers who are looking for skilled machine learning professionals. AWS Certified Machine Learning - Specialty certification is also a great way to demonstrate your expertise in machine learning to potential clients and customers.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q105-Q110):

NEW QUESTION # 105
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

  • A. Use Amazon Translate with the transcribed files to train and build a model for the key topics
  • B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
  • C. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
  • D. Use Amazon Comprehend with the transcribed files to build the key topics

Answer: A


NEW QUESTION # 106
A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:
* True positive rate (TPR): 0.700
* False negative rate (FNR): 0.300
* True negative rate (TNR): 0.977
* False positive rate (FPR): 0.023
* Overall accuracy: 0.949
Which solution should the data scientist use to improve the performance of the model?

  • A. Undersample the minority class.
  • B. Oversample the majority class.
  • C. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.
  • D. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.

Answer: C

Explanation:
The solution that the data scientist should use to improve the performance of the model is to apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset, and retrain the model with the updated training data. This solution can address the problem of class imbalance in the dataset, which can affect the model's ability to learn from the rare but important positive class (fraud).
Class imbalance is a common issue in machine learning, especially for classification tasks. It occurs when one class (usually the positive or target class) is significantly underrepresented in the dataset compared to the other class (usually the negative or non-target class). For example, in the credit card fraud detection problem, the positive class (fraud) is much less frequent than the negative class (fair transactions). This can cause the model to be biased towards the majority class, and fail to capture the characteristics and patterns of the minority class. As a result, the model may have a high overall accuracy, but a low recall or true positive rate for the minority class, which means it misses many fraudulent transactions.
SMOTE is a technique that can help mitigate the class imbalance problem by generating synthetic samples for the minority class. SMOTE works by finding the k-nearest neighbors of each minority class instance, and randomly creating new instances along the line segments connecting them. This way, SMOTE can increase the number and diversity of the minority class instances, without duplicating or losing any information. By applying SMOTE on the minority class in the training dataset, the data scientist can balance the classes and improve the model's performance on the positive class1.
The other options are either ineffective or counterproductive. Applying SMOTE on the majority class would not balance the classes, but increase the imbalance and the size of the dataset. Undersampling the minority class would reduce the number of instances available for the model to learn from, and potentially lose some important information. Oversampling the majority class would also increase the imbalance and the size of the dataset, and introduce redundancy and overfitting.
References:
1: SMOTE for Imbalanced Classification with Python - Machine Learning Mastery


NEW QUESTION # 107
A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet these requirements?

  • A. Stop and then restart the SageMaker notebook instance
  • B. Call the UpdateNotebookInstanceLifecycleConfig API operation
  • C. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
  • D. Call the CreateNotebookInstanceLifecycleConfig API operation

Answer: A

Explanation:
The correct solution for updating the software on a SageMaker notebook instance is to stop and then restart the notebook instance. This will automatically apply the latest security and software updates provided by SageMaker1 The other options are incorrect because they either do not update the software or require unnecessary steps.
For example:
* Option A calls the CreateNotebookInstanceLifecycleConfig API operation. This operation creates a lifecycle configuration, which is a set of shell scripts that run when a notebook instance is created or started. A lifecycle configuration can be used to customize the notebook instance, such as installing additional libraries or packages. However, it does not update the software on the notebook instance2
* Option B creates a new SageMaker notebook instance and mounts the Amazon Elastic Block Store (Amazon EBS) volume from the original instance. This option will create a new notebook instance with the latest software, but it will also incur additional costs and require manual steps to transfer the data and settings from the original instance3
* Option D calls the UpdateNotebookInstanceLifecycleConfig API operation. This operation updates an existing lifecycle configuration. As explained in option A, a lifecycle configuration does not update the software on the notebook instance4
1: Amazon SageMaker Notebook Instances - Amazon SageMaker
2: CreateNotebookInstanceLifecycleConfig - Amazon SageMaker
3: Create a Notebook Instance - Amazon SageMaker
4: UpdateNotebookInstanceLifecycleConfig - Amazon SageMaker


NEW QUESTION # 108
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?

  • A. A scatter plot showing the performance of the objective metric over each training iteration.
  • B. A histogram showing whether the most important input feature is Gaussian.
  • C. A scatter plot showing the correlation between maximum tree depth and the objective metric.
  • D. A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.

Answer: D


NEW QUESTION # 109
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 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
  • B. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
  • 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 to access the S3 bucket
  • D. 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

Answer: C

Explanation:
The best way to maintain the privacy and integrity of the data stored in Amazon S3 is to use a combination of VPC endpoints and S3 bucket policies. A VPC endpoint allows the SageMaker notebook instance to access the S3 bucket without going through the public internet. A bucket policy allows the S3 bucket owner to specify which VPCs or VPC endpoints can access the bucket. This way, the data is protected from unauthorized access and tampering. The other options are either insecure (A and D) or inefficient (B).
References: Using Amazon S3 VPC Endpoints, Using Bucket Policies and User Policies


NEW QUESTION # 110
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