VALID PROFESSIONAL-DATA-ENGINEER EXAM LABS | EFFICIENT PRACTICE PROFESSIONAL-DATA-ENGINEER ONLINE: GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM

Valid Professional-Data-Engineer Exam Labs | Efficient Practice Professional-Data-Engineer Online: Google Certified Professional Data Engineer Exam

Valid Professional-Data-Engineer Exam Labs | Efficient Practice Professional-Data-Engineer Online: Google Certified Professional Data Engineer Exam

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Google Certified Professional Data Engineer Exam Sample Questions (Q144-Q149):

NEW QUESTION # 144
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

  • A. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
  • B. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
  • C. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
  • D. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

Answer: A


NEW QUESTION # 145
You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

  • A. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.
  • B. Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.
  • C. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.
  • D. Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

Answer: A


NEW QUESTION # 146
You are developing an application that uses a recommendation engine on Google Cloud. Your solution
should display new videos to customers based on past views. Your solution needs to generate labels for
the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering
suggestions based on data from other customer preferences on several TB of data. What should you do?

  • A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
    classification model with Spark MLlib to filter results to match customer preferences. Deploy the
    models using Cloud Dataproc. Call the models from your application.
  • B. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
  • C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
    SQL, and join and filter the predicted labels to match the user's viewing history to generate
    preferences.
  • D. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
    Deploy the models using Cloud Dataproc. Call the model from your application.

Answer: B


NEW QUESTION # 147
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

  • A. You expect future mutations to have different features from the mutated samples in the database.
  • B. There are very few occurrences of mutations relative to normal samples.
  • C. You already have labels for which samples are mutated and which are normal in the database.
  • D. You expect future mutations to have similar features to the mutated samples in the database.
  • E. There are roughly equal occurrences of both normal and mutated samples in the database.

Answer: B,D

Explanation:
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set.
https://en.wikipedia.org/wiki/Anomaly_detection


NEW QUESTION # 148
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

  • A. DataFlow SDK
  • B. BiqQuery API
  • C. BigQuery Data Transfer Service
  • D. Cloud Dataflow connector

Answer: D

Explanation:
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.


NEW QUESTION # 149
......

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