최신 Professional-Machine-Learning-Engineer 무료덤프 - Google Professional Machine Learning Engineer

문제1

You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?

A. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

B. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.

C. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.

D. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket

E. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제2

You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry.
You want to perform A/B testing to identify the best-performing model using the simplest approach. What should you do?

A. Split incoming traffic among Google Kubernetes Engine (GKE) clusters and use Traffic Director to distribute prediction requests to different versions. Monitor the performance of each version using Cloud Monitoring.

B. Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Looker Studio dashboards that compare logged data for each version.

C. Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI's built-in monitoring tools.

D. Split incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제3

You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

A.

B.

C.

D.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제4

You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage Bucket. What should you do?

A. Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and use BigQuery ML to tram the model.

B. Use Vertex Al Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model.

C. Import the labeled images as a managed dataset in Vertex Al: and use AutoML to tram the model.

D. Use Vertex Al Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trams the model.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제5

During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A. Increase the learning rate hyperparameter

B. Increase the size of the training batch

C. Decrease the size of the training batch

D. Decrease the learning rate hyperparameter

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제6

You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

A. Manage your ML workflows with Vertex ML Metadata.

B. Manage all relational entities in the Hive Metastore.

C. Store all ML metadata in Google Cloud's operations suite.

D. Store your tf.logging data in BigQuery.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제7

You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

A. Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.

B. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

C. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result o find the table that you need.

D. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제8

You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?

A. Build a Dataflow pipeline to preprocess the new data in the bucket and store the processed features in BigQuery. Configure a cron job to trigger the pipeline execution.

B. Create a pipeline using the Vertex AI SDK. Schedule the pipeline with Cloud Scheduler to preprocess the new data in the bucket. Store the processed features in Vertex AI Feature Store.

C. Use the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.

D. Create a Cloud Run function that is triggered when new data arrives in the bucket. The function initiates a Vertex AI Pipeline to preprocess the new data and store the processed features in Vertex AI Feature Store.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제9

You have been tasked with deploying prototype code to production. The feature engineering code is in PySpark and runs on Dataproc Serverless. The model training is executed by using a Vertex Al custom training job. The two steps are not connected, and the model training must currently be run manually after the feature engineering step finishes. You need to create a scalable and maintainable production process that runs end-to-end and tracks the connections between steps. What should you do?

A. Create a Vertex Al Workbench notebook Use the notebook to submit the Dataproc Serverless feature engineering job Use the same notebook to submit the custom model training job Run the notebook cells sequentially to tie the steps together end-to-end

B. Create a Vertex Al Workbench notebook Initiate an Apache Spark context in the notebook, and run the PySpark feature engineering code Use the same notebook to run the custom model training job in TensorFlow Run the notebook cells sequentially to tie the steps together end-to-end

C. Create a Vertex Al Pipelines job to link and run both components Use the Kubeflow pipelines SDK to write code that specifies two components
- The first component initiates an Apache Spark context that runs the PySpark feature engineering code
- The second component runs the TensorFlow custom model training code Create a Vertex Al Pipelines job to link and run both components

D. Use the Kubeflow pipelines SDK to write code that specifies two components
- The first is a Dataproc Serverless component that launches the feature engineering job
- The second is a custom component wrapped in the
creare_cusrora_rraining_job_from_ccraponent Utility that launches the custom model training job.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제10

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

A. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

B. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

C. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler

D. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제11

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

A. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob

B. Create a library of VM images on Compute Engine; and publish these images on a centralized repository

C. Use the Al Platform custom containers feature to receive training jobs using any framework

D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제12

You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min- max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?

A. Write the transformations into Spark that uses the spark-bigquery-connector and use Dataproc to preprocess the data.

B. Add the transformations as a preprocessing layer in the TensorFlow models.

C. Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data process it and write it back to BigQuery.

D. Write SQL queries to transform the data in-place in BigQuery.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제13

You are creating a deep neural network classification model using a dataset with categorical input values.
Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

A. Convert each categorical value into an integer value.

B. Convert each categorical value into a run-length encoded string.

C. Convert the categorical string data to one-hot hash buckets.

D. Map the categorical variables into a vector of boolean values.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제14

You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity.
You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

A. Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.

B. Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.

C. Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

D. Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제15

You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%.
How can you make your production model more accurate?

A. Normalize the data for the training, and test datasets as two separate steps.

B. Split the training and test data based on time rather than a random split to avoid leakage

C. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

D. Add more data to your test set to ensure that you have a fair distribution and sample for testing

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제16

You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

A. Recurrent Neural Networks (RNN)

B. Reinforcement learning

C. Convolutional Neural Networks (CNN)

D. Recommender system

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

최신 Professional-Machine-Learning-Engineer 무료덤프 - Google Professional Machine Learning Engineer

우리와 연락하기

유용한 링크

최신 업데이트