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NEW QUESTION # 28
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?
- A. Confusion matrix
- B. Root Mean Square Error (RMSE)
- C. Residual plots
- D. Area under the curve
Answer: D
NEW QUESTION # 29
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?
- A. Use a TPU with tf.distribute.TPUStrategy.
- B. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
- C. Increase the batch size.
- D. Create a custom training loop.
Answer: A
NEW QUESTION # 30
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?
- A. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
- B. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
- C. Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText
- D. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
Answer: B
NEW QUESTION # 31
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- B. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- C. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
Answer: A
Explanation:
CI/CD for Kubeflow pipelines. At the heart of this architecture is Cloud Build, infrastructure. Cloud Build can import source from Cloud Source Repositories, GitHub, or Bitbucket, and then execute a build to your specifications, and produce artifacts such as Docker containers or Python tar files.
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#cicd_architecture
NEW QUESTION # 32
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance? (Choose One Correct Answer)
- A. Average time players wait before being assigned to a team
- B. User engagement as measured by the number of battles played daily per user
- C. Rate of return as measured by additional revenue generated minus the cost of developing a new model
- D. Precision and recall of assigning players to teams based on their predicted versus actual ability
Answer: B
NEW QUESTION # 33
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
- A. Change the search algorithm from Bayesian search to random search.
- B. Decrease the number of parallel trials
- C. Decrease the maximum number of trials during subsequent training phases.
- D. Set the early stopping parameter to TRUE
- E. Decrease the range of floating-point values
Answer: A,E
NEW QUESTION # 34
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?
- A. AutoML Vision model
- B. AutoML Vision Edge mobile-versatile-1 model
- C. AutoML Vision Edge mobile-high-accuracy-1 model
- D. AutoML Vision Edge mobile-low-latency-1 model
Answer: A
NEW QUESTION # 35
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?
- A. Change the learning rate
- B. Change the optimizer
- C. Reduce the batch size
- D. Reduce the image shape
Answer: C
Explanation:
Reference:
https://stackoverflow.com/questions/59394947/how-to-fix-resourceexhaustederror-oom-when-allocating-tensor/59395251#:~:text=OOM%20stands%20for%20%22out%20of,in%20your%20Dense%20%2C%20Conv2D%20layers
NEW QUESTION # 36
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?
- A. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
- B. Load the data into Cloud Bigtable, and read the data from Bigtable
- C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
- D. Load the data into BigQuery and read the data from BigQuery.
Answer: C
NEW QUESTION # 37
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?
- A. Federated learning
- B. Data Loss Prevention API
- C. Differential privacy
- D. MD5 to encrypt data
Answer: A
NEW QUESTION # 38
You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?
- A. Use AutoML Tables to train the model without early stopping.
- B. Use BQML XGBoost regression to train the model
- C. Use AutoML Tables to train the model with RMSLE as the optimization objective
- D. Create a custom TensorFlow DNN model.
Answer: B
Explanation:
https://cloud.google.com/bigquery-ml/docs/introduction
NEW QUESTION # 39
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- C. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
Answer: C
NEW QUESTION # 40
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?
- A. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
- B. Reduce the learning rate and run the training process until the training loss stops decreasing.
- C. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
- D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
Answer: B
NEW QUESTION # 41
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?
- A. F-score where recall is weighed more than precision
- B. RMSE
- C. F-score where precision is weighed more than recall
- D. F1 score
Answer: A
NEW QUESTION # 42
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?
- A. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
- B. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- C. Significantly increase the max_batch_size TensorFlow Serving parameter
- D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
Answer: D
NEW QUESTION # 43
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?
- A. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
- B. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
- C. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
- D. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
Answer: B
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION # 44
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