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You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?

A.

Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.

B.

Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.

C.

Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.

D.

Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.

You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

A.

Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.

B.

Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

C.

Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

D.

Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.

You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A.

Cloud SQL

B.

Cloud Bigtable

C.

Cloud Spanner

D.

Cloud Datastore

Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).

What should you do?

A.

Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

B.

Add a try… catch block to your DoFn that transforms the data, extract erroneous rows from logs.

C.

Add a try… catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

D.

Add a try… catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. What should you do?

Choose 2 answers

A.

Configure your Dataflow pipeline to use local execution.

B.

Modify your Dataflow pipeline lo use the Flatten transform before writing to Bigtable.

C.

Modify your Dataflow pipeline to use the CoGrcupByKey transform before writing to Bigtable.

D.

Increase the maximum number of Dataflow workers by setting maxNumWorkers in PipelineOptions.

E.

Increase the number of nodes in the Bigtable cluster.

You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery In your current relational database, the author information is kept in a separate table and joined to the book information on a common key Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

A.

Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today

B.

Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc

C.

Create a table that includes information about the books and authors, but nest the author fields inside the author column

D.

Keep the schema the same, create a view that joins all of the tables, and always query the view

You work for a farming company. You have one BigQuery table named sensors, which is about 500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp. which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

A.

1. Create a retries column in the sensor? table.

2. Set record type and repeated mode for the metrics column.

3. Use an UPDATE statement every 30 seconds to add new metrics.

B.

1. Create a metrics column in the sensors table.

2. Set RECORD type and REPEATED mode for the metrics column.

3. Use an INSERT statement every 30 seconds to add new metrics.

C.

1. Create a metrics table partitioned by timestamp.

2. Create a sensorld column in the metrics table, that points to the id column in the sensors table.

3. Use an IHSEW statement every 30 seconds to append new metrics to the metrics table.

4. Join the two tables, if needed, when running the analytical query.

D.

1. Create a metrics table partitioned by timestamp.

2. Create a sensor Id column in the metrics table, that points to the _d column in the sensors table.

3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table.

4. Join the two tables, if needed, when running the analytical query.

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.

Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

C.

Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

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.

You need to compose visualization for operations teams with the following requirements:

    Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)

    The report must not be more than 3 hours delayed from live data.

    The actionable report should only show suboptimal links.

    Most suboptimal links should be sorted to the top.

    Suboptimal links can be grouped and filtered by regional geography.

    User response time to load the report must be <5 seconds.

You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

A.

Look through the current data and compose a series of charts and tables, one for each possible

combination of criteria.

B.

Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

C.

Export the data to a spreadsheet, compose a series of charts and tables, one for each possible

combination of criteria, and spread them across multiple tabs.

D.

Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

You need to compose visualizations for operations teams with the following requirements:

Which approach meets the requirements?

A.

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

B.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

C.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

D.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.